246 results
Search Results
2. Algorithms for the Reconstruction of Genomic Structures with Proofs of Their Low Polynomial Complexity and High Exactness.
- Author
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Gorbunov, Konstantin and Lyubetsky, Vassily
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DIRECTED graphs , *POLYNOMIALS , *ALGORITHMS , *COMPUTATIONAL complexity , *MATHEMATICAL optimization , *PROBLEM solving , *PATHS & cycles in graph theory , *BIPARTITE graphs - Abstract
The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
3. INNA: An improved neural network algorithm for solving reliability optimization problems.
- Author
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Kundu, Tanmay and Garg, Harish
- Subjects
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REDUNDANCY in engineering , *ALGORITHMS , *MULTIPLE comparisons (Statistics) , *STATISTICAL hypothesis testing , *MATHEMATICAL optimization , *PARTICLE swarm optimization , *PROBLEM solving - Abstract
The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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4. A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems †.
- Author
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Vakhnin, Aleksei and Sopov, Evgenii
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GLOBAL optimization , *COEVOLUTION , *ALGORITHMS , *PROBLEM solving , *MATHEMATICAL optimization , *SELF-adaptive software , *METAHEURISTIC algorithms , *EVOLUTIONARY algorithms - Abstract
Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process ("on fly") from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC'2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
5. UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.
- Author
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Dai, Jun, Liu, Songlin, Hao, Xiangyang, Ren, Zongbin, and Yang, Xiao
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KALMAN filtering , *GRAPH algorithms , *ALGORITHMS , *AERONAUTICAL navigation , *LOCALIZATION (Mathematics) , *MATHEMATICAL optimization , *PROBLEM solving - Abstract
With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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6. Anti-UAV High-Performance Computing Early Warning Neural Network Based on PSO Algorithm.
- Author
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Lei, Yang, Yao, Honglei, Jiang, Bo, Tian, Tian, and Xing, Peifei
- Subjects
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GLOBAL optimization , *ALGORITHMS , *MATHEMATICAL optimization , *WARNINGS , *PROBLEM solving - Abstract
In order to effectively solve the problem that the radar detection system is difficult to detect the "low, small, slow" UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information; the PSO algorithm is used to optimize the parameters of the anti-UAV to ensure that the anti-UAV not only relies on factors but also fully combines the dependence of the visual field factor to quickly obtain the optimal solution through analyzing the high-performance computing early warning neural network in this paper. This algorithm is used to initialize the anti-UAV resources and improve the global optimization capability of the algorithm proposed in this paper. Finally, the experimental results show that the proposed PSO algorithm has better high-performance computing early warning performance to meet the actual needs of network high-performance computing early-warning neural networks. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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7. A Hybrid Strategy Improved Whale Optimization Algorithm for Web Service Composition.
- Author
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Ju, Chuanxiang, Ding, Hangqi, and Hu, Benjia
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WEB services , *MATHEMATICAL optimization , *QUALITY of service , *DIFFERENTIAL evolution , *PROBLEM solving , *METAHEURISTIC algorithms , *ALGORITHMS - Abstract
With the rapid growth of the number of web services on the Internet, various service providers provide many similar services with the same function but different quality of service (QoS) attributes. It is a key problem to be solved urgently to select the service composition quickly, meeting the users' QoS requirements from many candidate services. Optimization of web service composition is an NP-hard issue and intelligent optimization algorithms have become the mainstream method to solve this complex problem. This paper proposed a hybrid strategy improved whale optimization algorithm, which is based on the concepts of chaos initialization, nonlinear convergence factor and mutation. By maintaining a balance between exploration and exploitation, the problem of slow or early convergence is overcome to a certain extent. To evaluate its performance more accurately, the proposed algorithm was first tested on a set of standard benchmarks. After, simulations were performed using the real quality of web service dataset. Experimental results show that the proposed algorithm is better than the original version and other meta-heuristic algorithms on average, as well as verifies the feasibility and stability of web service composition optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. Smart surgical control under RCM constraint using bio-inspired network.
- Author
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Khan, Ameer Tamoor and Li, Shuai
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OPERATIVE surgery , *SURGICAL robots , *PROBLEM solving , *ALGORITHMS , *INTELLIGENT control systems , *MATHEMATICAL optimization - Abstract
In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. A fixed structure learning automata‐based optimization algorithm for structure learning of Bayesian networks.
- Author
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Asghari, Kayvan, Masdari, Mohammad, Soleimanian Gharehchopogh, Farhad, and Saneifard, Rahim
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ANT algorithms , *BEES algorithm , *MATHEMATICAL optimization , *MACHINE learning , *ALGORITHMS , *PROBLEM solving , *KNOWLEDGE representation (Information theory) , *METAHEURISTIC algorithms - Abstract
One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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10. The effect of different stopping criteria on multi-objective optimization algorithms.
- Author
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Abu Doush, Iyad, El-Abd, Mohammed, Hammouri, Abdelaziz I., and Bataineh, Mohammad Qasem
- Subjects
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MATHEMATICAL optimization , *EVOLUTIONARY algorithms , *PROBLEM solving , *ALGORITHMS - Abstract
Evolutionary multi-objective optimization (EMO) refers to the domain in which an evolutionary algorithm is applied to tackle an optimization problem with multiple objective functions. The literature is rich with many approaches proposed to solve multi-objective problems including the NSGA-II, MOEA/D, and MOPSO algorithms. The proposed approaches include stand-alone as well as hybrid techniques. One critical aspect of any evolutionary algorithm (EA) is the stopping criterion. The selection of a specific stopping criterion can have a considerable effect on the performance and the final solution provided by the EA. A number of different stopping criteria, specifically designed for EMO, have been proposed in the literature. In this paper, the performance of six different EMO algorithms is tested and compared using four stopping criteria. The experiments are performed using the ZDT, DTLZ, CEC2009, Tanaka and Srivana test functions. Experimental results are analyzed to highlight the proper stopping criteria for different algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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11. A novel discrete elephant herding optimization for energy-saving flexible job shop scheduling problem with preventive maintenance.
- Author
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Lu Liu, Qiming Sun, Tianhua Jiang, Guanlong Deng, Qingtao Gong, and Yaping Li
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PRODUCTION scheduling , *ALGORITHMS , *ENERGY consumption , *MATHEMATICAL optimization , *PROBLEM solving - Abstract
Recently, energy-saving scheduling issues have attracted more and more attention in the manufacturing field. Meanwhile, in practical production, maintenance planning is viewed as a vital task in the workshop. However, the existing literature about energy-saving scheduling problems rarely consider the effect of preventive maintenance. Therefore, this paper investigates an energy-saving flexible job shop scheduling problem with preventive maintenance. A mathematical model is proposed considering the minimization of total energy consumption. To solve the problem, a novel discrete elephant herding optimization algorithm (NDEHO) is proposed according to the problem's characteristics. To test the NDEHO's performance, the Taguchi design of experiment approach is adopted to get the best combination of parameters in the algorithm. Numerical experiments are conducted based on twenty-four instances, including four benchmark instances and twenty randomly generated instances. Computational data indicate that NDEHO outperforms other compared algorithms for solving the considered problem. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
12. A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process.
- Author
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Dehghani, Mohammad, Trojovská, Eva, and Trojovský, Pavel
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METAHEURISTIC algorithms , *PROBLEM solving , *MATHEMATICAL optimization , *LEARNING , *HEURISTIC algorithms , *AUTOMOBILE driving schools , *ALGORITHMS - Abstract
In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
13. A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm.
- Author
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Li, Maodong, Xu, Guanghui, Lai, Qiang, and Chen, Jie
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MATHEMATICAL optimization , *PROBLEM solving , *ALGORITHMS , *CONSTRAINED optimization , *LEARNING strategies , *PARTICLE swarm optimization , *RANDOM numbers , *WHALES - Abstract
In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm's convergence accuracy and convergence speed are insufficient. Firstly, the proposed algorithm is initialized by a method based on chaotic maps and quadratic opposition-based learning strategy to obtain a population with better ergodicity. Secondly, by introducing an adaptive variable speed adjustment factor, each search link unites to form a negative feedback regulation network, thereby effectively balancing the algorithm's exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The results show that the improved algorithm has better performance than others in terms of convergence speed and convergence accuracy in a majority of cases, and can effectively jump out of the local optimum. [Display omitted] • Firstly, this paper proposed a quadratic Opposition-Base Learning strategy based on Bernoulli map, and successfully obtained a higher quality initial whale population. • Secondly, this paper proposed a new variable speed adjustment factor and a cooperative convergence factor, and combine them with individual fitness to form a negative feedback adjustment network. Experiments show that the algorithm has good self-adjusting ability. • Finally, the range of some of the original random numbers in whale optimization algorithm has been changed. This adjustment allows the algorithm to have a more variable search range. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
14. Using Variational Quantum Algorithm to Solve the LWE Problem.
- Author
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Lv, Lihui, Yan, Bao, Wang, Hong, Ma, Zhi, Fei, Yangyang, Meng, Xiangdong, and Duan, Qianheng
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PROBLEM solving , *ALGORITHMS , *APPROXIMATION algorithms , *MATHEMATICAL optimization , *DECODING algorithms , *QUBITS - Abstract
The variational quantum algorithm (VQA) is a hybrid classical–quantum algorithm. It can actually run in an intermediate-scale quantum device where the number of available qubits is too limited to perform quantum error correction, so it is one of the most promising quantum algorithms in the noisy intermediate-scale quantum era. In this paper, two ideas for solving the learning with errors problem (LWE) using VQA are proposed. First, after reducing the LWE problem into the bounded distance decoding problem, the quantum approximation optimization algorithm (QAOA) is introduced to improve classical methods. Second, after the LWE problem is reduced into the unique shortest vector problem, the variational quantum eigensolver (VQE) is used to solve it, and the number of qubits required is calculated in detail. Small-scale experiments are carried out for the two LWE variational quantum algorithms, and the experiments show that VQA improves the quality of the classical solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
15. A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection.
- Author
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Li, Jun, Ren, Hao, Li, ChenYang, and Chen, Huiling
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WEB services , *ALGORITHMS , *METAHEURISTIC algorithms , *SEARCH algorithms , *PROBLEM solving , *COMBINATORIAL optimization , *MATHEMATICAL optimization - Abstract
The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
16. A novel hybrid algorithm based on rat swarm optimization and pattern search for parameter extraction of solar photovoltaic models.
- Author
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Eslami, Mahdiyeh, Akbari, Ehsan, Seyed Sadr, Seyed T., and Ibrahim, Banar F.
- Subjects
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PHOTOVOLTAIC power systems , *PARTICLE swarm optimization , *ALGORITHMS , *RATS , *PROBLEM solving , *MATHEMATICAL optimization , *SOLAR energy - Abstract
Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
17. Improved FunkSVD Algorithm Based on RMSProp.
- Author
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Yue, Xiaochen and Liu, Qicheng
- Subjects
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ALGORITHMS , *DEEP learning , *MACHINE learning , *MATHEMATICAL optimization , *PROBLEM solving - Abstract
To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process.
- Author
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Dehghani, Mohammad, Trojovská, Eva, and Trojovský, Pavel
- Subjects
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METAHEURISTIC algorithms , *PROBLEM solving , *MATHEMATICAL optimization , *LEARNING , *HEURISTIC algorithms , *AUTOMOBILE driving schools , *ALGORITHMS - Abstract
In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
19. STOCHASTIC MULTILEVEL COMPOSITION OPTIMIZATION ALGORITHMS WITH LEVEL-INDEPENDENT CONVERGENCE RATES.
- Author
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BALASUBRAMANIAN, KRISHNAKUMAR, GHADIMI, SAEED, and NGUYEN, ANTHONY
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MATHEMATICAL optimization , *PROBLEM solving , *ALGORITHMS , *MOVING average process - Abstract
In this paper, we study smooth stochastic multilevel composition optimization problems, where the objective function is a nested composition of T functions. We assume access to noisy evaluations of the functions and their gradients, through a stochastic first-order oracle. For solving this class of problems, we propose two algorithms using moving-average stochastic estimates, and analyze their convergence to an e-stationary point of the problem. We show that the first algorithm, which is a generalization of [S. Ghadimi, A. Ruszczynski, and M. Wang, SIAM J. Optim., 30 (2020), pp. 960-979] to the T level case, can achieve a sample complexity of OT(1/ε6) by using minibatches of samples in each iteration, where OT hides constants that depend on T. By modifying this algorithm using linearized stochastic estimates of the function values, we improve the sample complexity to OT(1/ε4). This modification not only removes the requirement of having a minibatch of samples in each iteration, but also makes the algorithm parameter-free and easy to implement. To the best of our knowledge, this is the first time that such an online algorithm designed for the (un)constrained multilevel setting obtains the same sample complexity of the smooth single-level setting, under standard assumptions (unbiasedness and boundedness of the second moments) on the stochastic first-order oracle. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
20. A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems.
- Author
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Goldanloo, Mina Javanmard and Gharehchopogh, Farhad Soleimanian
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MATHEMATICAL functions , *SEARCH algorithms , *ALGORITHMS , *MATHEMATICAL optimization , *METAHEURISTIC algorithms , *PROBLEM solving - Abstract
The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
21. Candidate word generation for OCR errors using optimization algorithm.
- Author
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Pham, D. T., Nguyen, D. Q., Le, A. D., Phan, M. N., and Kromer, P.
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MATHEMATICAL optimization , *PROBLEM solving , *ALGORITHMS , *INTERNATIONAL competition , *HEURISTIC algorithms - Abstract
OCR post-processing is an important step to improve OCR text accuracy. It includes two main tasks, error detection and error correction. Hill climbing algorithm is a heuristic search method used for solving optimization problems. In this paper, we present a novel OCR error correction approach using an adapted version of the Hill climbing algorithm. Correction candidates of OCR errors are explored by random character edits and evolved with the Hill climbing. The character edit patterns are obtained from the training data. The proposed model is evaluated on the benchmark dataset in the OCR post-correction competition of the International Conference on Document Analysis and Recognition 2017. It is shown that our model outperforms various baseline approaches in the competition. In addition, the randomness of the proposed algorithm is analyzed to verify its stability under parameter configurations. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
22. Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation.
- Author
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Sefati, SeyedSalar, Mousavinasab, Maryamsadat, and Zareh Farkhady, Roya
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MATHEMATICAL optimization , *CLOUD computing , *LOAD balancing (Computer networks) , *INFORMATION technology , *METAHEURISTIC algorithms , *PROBLEM solving , *ALGORITHMS - Abstract
The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
23. An adaptive surrogate-assisted particle swarm optimization for expensive problems.
- Author
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Li, Xuemei and Li, Shaojun
- Subjects
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PARTICLE swarm optimization , *EVOLUTIONARY algorithms , *ALGORITHMS , *PROBLEM solving , *MATHEMATICAL optimization , *RADIAL basis functions - Abstract
To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
24. Adaptive multi-objective particle swarm optimization using three-stage strategy with decomposition.
- Author
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Huang, Weimin and Zhang, Wei
- Subjects
- *
PARTICLE swarm optimization , *PROBLEM solving , *MATHEMATICAL optimization , *ALGORITHMS - Abstract
Balancing the convergence and the diversity is one of the crucial researches in solving multi-objective problems (MOPs). However, the optimization algorithms are inefficient and require massive iterations. The convergence accuracy and the distribution of the obtained non-dominated solutions are defective in solving complex MOPs. To solve these problems, a novel adaptive multi-objective particle swarm optimization using a three-stage strategy (tssAMOPSO) is proposed in this paper. Firstly, an adaptive flight parameter adjustment is proposed to manage the states of the algorithm, switching between the global exploration and the local exploitation. Then, the three-stage strategy, including adaptive optimization, decomposition, and Gaussian attenuation mutation, is conducted by population in each iteration. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Furthermore, the convergence analysis of three-stage strategy is provided in detail. Finally, particles are equipped with memory interval to improve the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of non-dominated solutions directly. A series of benchmark instances, ZDT and DTLZ test suits, are used to verify the performance of tssAMOPSO. Several classical and state-of-the-art algorithms are employed for experimental comparisons. Experimental results show that tssAMOPSO outperforms the other algorithms and achieves admirable comprehensive performance. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
25. Adaptive opposition slime mould algorithm.
- Author
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Naik, Manoj Kumar, Panda, Rutuparna, and Abraham, Ajith
- Subjects
- *
MYXOMYCETES , *ALGORITHMS , *PROBLEM solving , *SEARCH engines , *MATHEMATICAL optimization - Abstract
Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition-based learning (OBL) will be used or not. Sometimes, the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions that consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA's performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon's rank-sum test. It also ranked one in Friedman's mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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26. Overlapping Decentralized Control Strategies of Building Structures' Vibration with Time Delay Based on H∞ Control Algorithms under Seismic Excitation.
- Author
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Kang, Xiaofang, Wu, Jian, Zhang, Yewei, Liu, Guoliang, Zhang, Suhui, Tao, Xueting, Xia, Guanghui, Man, Dawei, and Xu, Qinghu
- Subjects
- *
LINEAR matrix inequalities , *PROBLEM solving , *ALGORITHMS , *GENETIC algorithms , *MATHEMATICAL optimization - Abstract
A decentralized control strategy can effectively solve the control problem of the large-scale time delayed structures. In this paper, combining the overlapping decentralized control method, linear matrix inequality (LMI) method, and H∞ control algorithm, overlapping decentralized H∞ control approach of the time delayed structures has been established. The feedback gain matrixes of all subsystems are obtained by this method based on genetic algorithm optimization tools and the specific goal of optimization control. The whole vibration control system of the time delayed structures is divided into a series of overlapping subsystems by overlapping decentralized control strategy. The feedback gain matrixes of each subsystem can be obtained by using H∞ control algorithm to calculate each subsystem. The vibration control of a twenty layers' antiseismic steel structure Benchmark model was analyzed with the numerical method. The results show that the proposed method can be applied to control system with time delay. The overlapping decentralized control strategies acquire the similar control effects with that of the centralized control strategy. Moreover, the flexibility of the controller design has been enhanced by using overlapping decentralized control strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
27. Artificial bee colony algorithm with directed scout.
- Author
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Saleh, Radhwan A. A. and Akay, Rustu
- Subjects
- *
BEES algorithm , *BEE colonies , *PROBLEM solving , *MATHEMATICAL optimization , *ALGORITHMS , *GLOBAL optimization , *HONEYBEES - Abstract
As a relatively new model, the artificial bee colony algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modifying the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on CEC2014, and CEC2015 benchmark suites, real-life problems. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching–Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
28. Artificial chicken swarm algorithm for multi-objective optimization with deep learning.
- Author
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Wei, Qianzhou, Huang, Dongru, and Zhang, Yu
- Subjects
- *
MATHEMATICAL optimization , *ALGORITHMS , *PROBLEM solving , *FORAGING behavior , *DEEP learning - Abstract
With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
29. Optimal allocation of a hybrid photovoltaic‐based DG and DSTATCOM under the load and irradiance variability.
- Author
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Oda, Eyad S., Ebeed, Mohamed, Abd El Hamed, Amal M., Ali, Abdelfatah, Elbaset, Adel A., and Abdelsattar, Montaser
- Subjects
- *
PROBLEM solving , *COST control , *SOLAR spectra , *MATHEMATICAL optimization , *ALGORITHMS , *RENEWABLE energy sources , *SOLAR radiation - Abstract
Summary: This paper addresses the allocation of a hybrid system that includes PV‐DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV‐DG and DSTATCOM. The considered objective function is a multi‐objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two‐hybrid system on a large 94‐bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single‐hybrid PV‐DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two‐hybrid systems is superior compared with the inclusion of a single‐hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
30. A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks.
- Author
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Zhang, Yichuan, Yong, Yibo, Yang, Shujun, and Zhang, Tian
- Subjects
- *
SOCIAL influence , *SOCIAL networks , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
31. A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks.
- Author
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Zhang, Yichuan, Yong, Yibo, Yang, Shujun, and Zhang, Tian
- Subjects
- *
SOCIAL influence , *SOCIAL networks , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
32. Fitness distance correlation and mixed search strategy for differential evolution.
- Author
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Li, Wei, Meng, Xiang, and Huang, Ying
- Subjects
- *
DIFFERENTIAL evolution , *EVOLUTIONARY algorithms , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
33. A new optimization algorithm to solve multi-objective problems.
- Author
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Sharifi, Mohammad Reza, Akbarifard, Saeid, Qaderi, Kourosh, and Madadi, Mohamad Reza
- Subjects
- *
PROBLEM solving , *EVOLUTIONARY algorithms , *MATHEMATICAL optimization , *BENCHMARK problems (Computer science) , *ALGORITHMS , *MOTHS - Abstract
Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
34. A robust supervised subspace learning approach for output-relevant prediction and detection against outliers.
- Author
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Li, Wenqing and Wang, Yue
- Subjects
- *
SUPERVISED learning , *ORTHOGONAL decompositions , *PROBLEM solving , *ALGORITHMS , *OUTLIER detection , *MATHEMATICAL optimization - Abstract
This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. Design of Metasurface-Based Multi-layer THz Filters Utilizing Optimization Algorithm with Distinct Fitness Function Definitions.
- Author
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Nemat-Abad, Hamed Mohammadi, Zareian-Jahromi, Ehsan, and Basiri, Raheleh
- Subjects
- *
MATHEMATICAL optimization , *ALGORITHMS , *DEFINITIONS , *METAHEURISTIC algorithms , *PROBLEM solving , *BANDWIDTHS , *EVOLUTIONARY algorithms , *TERAHERTZ technology - Abstract
In this paper, an evolutionary optimization procedure is presented to generate band-pass metasurface-based filters in terahertz regime. As a measure of novelty, pass-band, transition, and out-band characteristics are investigated separately, all of which result in different metasurfaces for filtering applications. The presented approach is defined based on random hill climbing algorithm, regarding the established link between Matlab and HFSS software. A metasurface-based filter with specific properties is considered as the main problem to be solved by the optimization method. Moreover, the fuzzy theory, mean square method, and weighting coefficient procedure are considered to define an efficient fitness function evaluation approach. Also, a step-by-step procedure is used to generate desired structures with a great note of efficiency. The final generated structure has magnificent characteristics including sharp transitions together with transmittance around 0.68 and less than 0.04 at pass-band and out-band regions, respectively. Also, the generated metasurface benefits from wide bandwidth (65%) and great compactness compared to other previous works. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on Jaya algorithm.
- Author
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Hao, Xiaochen, Gao, Yong, Yang, Xunian, and Wang, Junwei
- Subjects
- *
ALGORITHMS , *CEMENT , *LIME (Minerals) , *EVOLUTIONARY algorithms , *PROBLEM solving , *MATHEMATICAL optimization - Abstract
Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. Research on hybrid modified pathfinder algorithm for optimal reactive power dispatch.
- Author
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SURESH, V. and KUMAR, S. SENTHIL
- Subjects
- *
REACTIVE power , *ALGORITHMS , *CAPACITOR banks , *DIFFERENTIAL evolution , *PROBLEM solving , *MATHEMATICAL optimization - Abstract
Hybridization of meta-heuristic algorithms plays a major role in the optimization problem. In this paper, a new hybrid meta-heuristic algorithm called hybrid pathfinder algorithm (HPFA) is proposed to solve the optimal reactive power dispatch (ORPD) problem. The superiority of the Differential Evolution (DE) algorithm is the fast convergence speed, a mutation operator in the DE algorithm incorporates into the pathfinder algorithm (PFA). The main objective of this research is to minimize the real power losses and subject to equality and inequality constraints. The HPFA is used to find optimal control variables such as generator voltage magnitude, transformer tap settings and capacitor banks. The proposed HPFA is implemented through several simulation cases on the IEEE 118-bus system and IEEE 300-bus power system. Results show the superiority of the proposed algorithm with good quality of optimal solutions over existing optimization techniques, and hence confirm its potential to solve the ORPD problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm.
- Author
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Huang, Chen
- Subjects
- *
PARTICLE swarm optimization , *MATHEMATICAL optimization , *ALGORITHMS , *PROBLEM solving , *DRONE aircraft - Abstract
This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCA ∗ PSO) based dynamic divide-and-conquer (DC) strategy and modified A ∗ algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, A ∗ algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCA ∗ PSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCA ∗ PSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCA ∗ PSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCA ∗ PSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
39. Set Theoretical Variants of Optimization Algorithms for System Reliability-based Design of Truss Structures.
- Author
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Kaveh, Ali, Hamedani, Kiarash Biabani, and Kamalinejad, Mohammad
- Subjects
- *
MATHEMATICAL optimization , *TRUSSES , *PROBLEM solving , *ALGORITHMS , *RELIABILITY in engineering , *STRUCTURAL reliability , *FAILURE mode & effects analysis - Abstract
In this paper, recently developed set theoretical variants of the teaching-learning-based optimization (TLBO) algorithm and the shuffled shepherd optimization algorithm (SSOA) are employed for system reliability-based design optimization (SRBDO) of truss structures. The set theoretical variants are designed based on a simple framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. In addition, the framework is applied to the Jaya algorithm, leading to a set-theoretical variant of the Jaya algorithm. So far, most of the reliability-based design optimization studies have focused on the reliability of single structural members. This is due to the fact that the optimization problems with system reliability-based constraints are computationally expensive to solve. This is especially the case of statically redundant structures, where the number of failure modes is so high that it is impractical to identify all of them. System-level reliability analysis of truss structures is carried out by the branch and bound method by which the stochastically dominant failure paths are identified within a reasonable time. At last, three numerical examples, including size optimization of truss structures, are presented to illustrate the effectiveness of the proposed SRBDO approach. The results indicate the efficiency and applicability of the set theoretical optimization algorithms to solve the SRBDO problems of truss structures. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
40. CSCF: a chaotic sine cosine firefly algorithm for practical application problems.
- Author
-
Hassan, Bryar A.
- Subjects
- *
PARTICLE swarm optimization , *PROBLEM solving , *ALGORITHMS , *COMPUTATIONAL complexity , *MATHEMATICAL optimization - Abstract
Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
41. A supervised multi-view feature selection method based on locally sparse regularization and block computing.
- Author
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Lin, Qiang, Men, Min, Yang, Liran, and Zhong, Ping
- Subjects
- *
FEATURE selection , *PROBLEM solving , *ALGORITHMS , *MATHEMATICAL optimization , *CLASSIFICATION - Abstract
• A supervised multi-view model is proposed to realize a block-based feature selection. • The proposed model is composed of all sharing sub-models in each class. • The sparse regularizer can enhance the sparsity of blocks from features and views. • The proposed algorithm can realize the block separation and independent solution. • Numerical experiments show the effectiveness of our method on large-scale datasets. With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. On the Linear Convergence of Two Decentralized Algorithms.
- Author
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Li, Yao and Yan, Ming
- Subjects
- *
ALGORITHMS , *PROBLEM solving , *MATHEMATICAL optimization , *MATRIX functions - Abstract
Decentralized algorithms solve multi-agent problems over a connected network, where the information can only be exchanged with the accessible neighbors. Though there exist several decentralized optimization algorithms, there are still gaps in convergence conditions and rates between decentralized and centralized algorithms. In this paper, we fill some gaps by considering two decentralized algorithms: EXTRA and NIDS. They both converge linearly with strongly convex objective functions. We will answer two questions regarding them. What are the optimal upper bounds for their stepsizes? Do decentralized algorithms require more properties on the functions for linear convergence than centralized ones? More specifically, we relax the required conditions for linear convergence of both algorithms. For EXTRA, we show that the stepsize is comparable to that of centralized algorithms. For NIDS, the upper bound of the stepsize is shown to be exactly the same as the centralized ones. In addition, we relax the requirement for the objective functions and the mixing matrices. We provide the linear convergence results for both algorithms under the weakest conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
43. A Global Optimization Algorithm for Solving Indefinite Quadratic Programming.
- Author
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Chunfeng Wang, Yaping Deng, and Peiping Shen
- Subjects
- *
GLOBAL optimization , *MATHEMATICAL optimization , *LINEAR programming , *ALGORITHMS , *PROBLEM solving - Abstract
It is very difficult to solve indefinite quadratic programming problem. In this paper, for globally solving such problem, a novel algorithm is presented. Firstly, the initial problem (P) is converted into an equivalent problem (EP); then, the problem (EP) is reduced to a sequence of linear programming problems, which are very easy to be solved. The convergence and the complexity of the proposed algorithm is presented, and some experiments are provided to show its feasibility. [ABSTRACT FROM AUTHOR]
- Published
- 2020
44. Reduction of power loss by Henry's law-based soluble gas, mobula alfredi and balanced condition optimization algorithms.
- Author
-
Kanagasabai, Lenin
- Subjects
- *
HENRY'S law , *MATHEMATICAL optimization , *REACTIVE power , *PROBLEM solving , *ALGORITHMS - Abstract
Purpose: Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation. Design/methodology/approach: In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation. Findings: Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained. Originality/value: For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
45. A binary-continuous invasive weed optimization algorithm for a vendor selection problem.
- Author
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Niknamfar, Amir Hossein and Niaki, Seyed Taghi Akhavan
- Subjects
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MATHEMATICAL optimization , *BINARY number system , *ALGORITHMS , *FEATURE selection , *PROBLEM solving , *VENDORS (Real property) - Abstract
This paper introduces a novel and practical vendor selection problem of a firm that cooperates with multiple geographically dispersed stores. In this problem, the firm entrusts some of its business process to external vendors, and each store can split the ordered quantity between one or more potential vendors, represented as a multi-sourcing strategies. Moreover, the Cobb–Douglas demand function is utilised to establish a relationship between the market demand and the selling price; representing price-sensitive demand. This paper seeks to choose the best vendors, to allocate the stores to them, and to find the optimal values for inventory-related decisions. The approach is based on the integration of the vendor selection problem and the inventory-related decisions in order to generate additional opportunities for system-wide operational efficiency and cost-effectiveness. The aim is to minimise total cost of the firm consisting of costs associated with the vendor selection and inventory-related decisions. A novel meta-heuristic called the binary-continuous invasive weed optimization (BCIWO) algorithm that is capable of solving both binary and continuous optimization problems is developed to solve the complicated NP-hard problem. As there is no benchmark available in the literature, an efficient genetic algorithm enhanced by a multi-parent crossover operator is designed to solve the problem in order to validate the results obtained using BCIWO. The algorithms are tuned using the response surface methodology, based on which their performances are analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
46. Algorithms for Investment Project Distribution on Regions.
- Author
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Alharbi, Mafawez and Jemmali, Mahdi
- Subjects
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NP-hard problems , *ALGORITHMS , *MATHEMATICAL optimization , *INVESTMENTS , *PROBLEM solving , *COMPUTER scheduling - Abstract
This paper proposes an optimization system for solving an NP-hard problem by using several new algorithms and application programs. This study aims to identify a suitable distribution of investment projects across several developed industrial regions. It is assumed that all industrial regions involved have the same economic and strategic characteristics. The problem involves a set of projects that are to be assigned across regions. Each project creates an estimated number of new jobs, and the distribution of projects can be guided by minimizing the maximum total number of newly created jobs. The problem is NP-hard one, and it is difficult to determine the most appropriate distribution. We apply scheduling algorithms in order to solve the analyzed problem. Severalheuristics are developedto obtain the appropriate distribution of newly created jobs across all regions. A branch-and-bound method is employed in order to obtain the exact solution. The performance of the algorithm is demonstrated by the experimental results for a total number of 1850 instances. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
47. Modelling of Assembly Sequence Planning Problem using Base Part Concept.
- Author
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Abdullah, M. A., Ab Rashid, M. F. F., Ghazalli, Z., Nik Mohamed, N. M. Z., and Mohd Rose, A. N.
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PROBLEM solving , *MATHEMATICAL optimization , *PRODUCTION planning , *ALGORITHMS , *GEOMETRIC series - Abstract
Assembly sequence planning (ASP) plays an important role in manufacturing because it determines the investment on the assembly facilities. Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. According to the existing research, the most accurate approach to represent the ASP is to model base on the part of product. However, this approach resulting in the large number of assembly sequence possibility, and finally make the optimization process become harder. This paper proposed a simpler version of part-based representation using the base part concept. In the proposed model, the base part for the product is defined prior to the establishment of the precedence constraint. This approach has contributed to a smaller search space that resulting in better chance to obtain optimum solution during the optimization. The proposed ASP modelling approach however limited the number of possible solution, since the base part was fixed prior to the optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
48. Reviewing the use of the theory of inventive problem solving (TRIZ) in green supply chain problems.
- Author
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Ben Moussa, Fatima Zahra, Rasovska, Ivana, Dubois, Sébastien, De Guio, Roland, and Benmoussa, Rachid
- Subjects
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SUPPLY chains , *PROBLEM solving , *TRIZ theory , *ALGORITHMS , *MATHEMATICAL optimization - Abstract
The purpose of the paper is to review the practice of the theory of inventive problem solving (TRIZ) in Green Supply Chain (GSC) problems and to identify new research challenges focusing on the question: “To what extent is it necessary to evolve TRIZ tools, methods and theoretical grounding for addressing GSC inventive problems?” First, a review of the past contributions of TRIZ based methods to GSC problem resolution is presented. As the result of the papers review did not provide a comprehensive understanding of the limitations and areas of potential application of TRIZ in GSC, three experiments were conducted to complete the literature review, in order to provide a more comprehensive answer to the posed question and identify research challenges. The experiments addressing GSC problems were also conducted to explore to what extent the more mature meta-methods of classical TRIZ, namely ARIZ 85 A, C and the related sub-methods, can be used as in GSM problems. The examples were chosen to explore types of GSC problems that were not yet addressed with TRIZ. The experiment results highlight limitations on the use of the TRIZ in GSC inventive problems, which were not mentioned in the GSC literature. Moreover it highlights the limitation of using the more mature meta-methods of TRIZ (ARIZ 85A and ARIZ 85C) when the conflict to overcome contains more than two evaluation parameters and one action parameter. Finally, research challenges to overcome the limitations and to improve the use of TRIZ in GSC inventive problems are stated. Among them, methods for quickly establishing the existence of classical TRIZ contradictions or for informing the problem solver when no TRIZ contradictions are present in a given inventive problem in GSC should be proposed. Such methods would permit determining whether ARIZ 85C could be used and avoid a long and fruitless search for a system of contradictions. Find alternatives to the algorithms proposed in the past to be able to establish the generalized contradictions of inventive problems. Make evolve meta-methods ARIZ 85C or substitute it with methods which can address the inventive problems that cannot be treated by ARIZ 85C. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
49. A long-step feasible predictor-corrector interior-point algorithm for symmetric cone optimization.
- Author
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Asadi, S., Mansouri, H., Darvay, Zs., Lesaja, G., and Zangiabadi, M.
- Subjects
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ALGORITHMS , *PROBLEM solving , *MATHEMATICAL optimization , *ITERATED integrals , *ITERATIVE methods (Mathematics) - Abstract
In this paper, we present a feasible predictor-corrector interior-point method for symmetric cone optimization problem in the large neighbourhood of the central path. The method is generalization of Ai-Zhang's predictor-corrector algorithm to the symmetric cone optimization problem. Starting with a feasible point in given large neighbourhood of the central path, the algorithm still terminates in at most iterations. This matches the best known iteration bound that is usually achieved by short-step methods, thereby, closing the complexity gap between long- and short-step interior-point methods for symmetric cone optimization. The preliminary numerical results on a selected set of NETLIB problems show advantage of the method in comparison with the version of the algorithm that is not based on the predictor-corrector scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
50. Path optimization of taxi carpooling.
- Author
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Ma, Changxi, He, Ruichun, and Zhang, Wei
- Subjects
- *
CAR sharing , *PASSENGERS , *PROBLEM solving , *GENETIC algorithms , *MATHEMATICAL optimization , *ATTITUDE (Psychology) - Abstract
The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
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