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The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

Recently, substantial progress has been made in developing efficient algorithms for solving combinatorial optimization problems. In this paper, following this direction, the problem of designing supply chains which is an important combinatorial optimization problem is considered. The structural properties of supply chain models are investigated to transform such models into a generalized network optimization model. The transformation to a generalized network optimization model reduces the algorithms' solution time. Moreover, this paper proposes a new efficient hybrid algorithm based on tabu search and generalized network simplex algorithm (GN-TSA) for designing multi-objectives supply chain models. The developed algorithm's parameters are tuned properly, validated, and evaluated. The algorithm's performance is then compared to an exact algorithm embedded in the General Algebraic Modeling System (GAMS) and two metaheuristic algorithms, namely a linear programming simplex algorithm integrated with the tabu search approach (LP-TSA) and simulated annealing. The findings indicated that the GN-TSA obtains solutions very close to the exact algorithm with less computation time. In addition, the proposed algorithm outperforms the LP-TSA and simulated annealing in terms of computation time, while the quality of the solutions is the same as solutions obtained by LP-TSA and better than simulated annealing. On average, the results revealed that the reduction in computational time is more than 26.30% using GN-TSA compared to LP-TSA and simulated annealing. [ABSTRACT FROM AUTHOR]

Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions. [ABSTRACT FROM AUTHOR]

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic algorithm called Ali Baba and the forty thieves (AFT) for solving global optimization problems. Recall the famous tale of Ali Baba and the forty thieves, where Ali Baba once saw a gang of forty thieves enter a strange cave filled with all kinds of treasures. The strategies pursued by the forty thieves in the search for Ali Baba inspired us to design ideas and underlie the basic concepts to put forward the mathematical models and implement the exploration and exploitation processes of the proposed algorithm. The performance of the AFT algorithm was assessed on a set of basic benchmark test functions and two more challenging benchmarks called IEEE CEC-2017 and IEEE CEC-C06 2019 benchmark test functions. These benchmarks cover simple and complex test functions with various dimensions and levels of complexity. An extensive comparative study was performed between the AFT algorithm and other well-studied algorithms, and the significance of the results was proved by statistical test methods. To study the potential performance of AFT, its further development is discussed and carried out from five aspects. Finally, the applicability of the AFT algorithm was subsequently demonstrated in solving five engineering design problems. The results in both benchmark functions and engineering problems show that the AFT algorithm has stronger performance than other competitors' algorithms. [ABSTRACT FROM AUTHOR]

The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Lévy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm's efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions. [ABSTRACT FROM AUTHOR]

Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks' lifetime. This paper presents a static clustering algorithm that employs predator–prey optimization (PPO) for identifying cluster heads as well as routes for sending data to the sink. The objective of the optimization algorithm is to reduce the energy consumed in data collection and transmission, to achieve equalization in energy utilization by the wireless sensor nodes and to prolong the wireless sensor network lifetime while avoiding the expenses of cluster reformation in each communication round. The novelty of this algorithm is to treat the identification of cluster heads and the choice of transmission paths a unified optimization problem of minimizing the total energy cost of the network, whereas existing algorithms consider them two separate optimization sub-problems. PPO algorithm is applied to select the most appropriate pair of cluster heads for each cluster. It also identifies the optimum communication path, which can be single or multiple hop. The energy consumed in data transmission is reduced and a uniformity in residual energy of the nodes is achieved. The performance of the novel algorithm has been evaluated by observing the patterns in which nodes consume their energies. The number of packets that are successfully delivered has been found to be better than the existing static clustering algorithms, and at par with the finest dynamic clustering algorithms. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency. [ABSTRACT FROM AUTHOR]

Modeling and optimization for compliant mechanisms are challenging tasks thanks to an unclear kinematic merging among rigid and flexible links. Hence, this paper develops a computational intelligence-based method for modeling and optimization. The proposed method concerns about statistics, numerical simulation, computational intelligence, and metaheuristics. A two degrees of freedom compliant mechanism is investigated to illustrate the effectiveness of the suggested computational intelligence method. First, numerical datasets are collected by simulations. Then, sensitivity of design parameters is analyzed by analysis of variance and Taguchi technique. The results of sensitivity are employed to separate a few populations for lightning attachment procedure optimization (LAPO). Next, the values of two output performances of the mechanism are changed into the values in the range from zero to one through desirability function method. The calculated output values become two inputs of the fuzzy logic model, and the output of this system is a single objective function (SOF). Subsequently, the SOF is modeled by using adaptive neuro-fuzzy inference system (ANFIS). LAPO algorithm is then utilized to maximize the SOF. The results revealed that the numerical example 3 is the best design for the mechanism. In comparison with artificial intelligence techniques and regression, the results show that the performance indexes of the proposed ANFIS model (R2 close 1, MSE about 10–4, and RMSE about 10–2) are superior to those of the multilayer perceptron, deep neural network, and multiple-linear regression. Additionally, the proposed computational intelligence method is more effective than the Taguchi-fuzzy logic, ANFIS-integrated teaching learning-based optimization, and ANFIS-integrated Jaya in searching the optimal design of the compliant mechanism. The results determined that the optimal displacement and parasitic error are about 2.2109 mm and 0.0028 mm, respectively. [ABSTRACT FROM AUTHOR]

Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery needs to deal with the non-uniform arrival of the grapes, while guaranteeing suppliers' satisfaction and wine quality. The best management of the resources of the suppliers (i.e., grapes and trucks) and winery (i.e., grain-tanks and pressing machines) must be ensured. In this paper, the underlying optimization problem for grape reception is solved by developing a genetic algorithm (GA) tailored for this specific challenge. The results of this algorithm are compared with a FIFO policy for a typical scenario that occurs on the harvest days of a real winery. Additionally, different scenarios are simulated to assess the validity and quality of the solutions found. The results show that, using modest computational resources, it is possible to achieve better solutions with the proposed GA. This allows for the algorithm to be used in real time, even whenever plant conditions change significantly (e.g., when a new truck arrives, when a machine fails). Furthermore, the trucks and grapes waiting time for the results using the developed GA are significantly smaller than the ones observed using a FIFO approach. [ABSTRACT FROM AUTHOR]

Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with β -hill climbing optimizer (β HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while β HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named β -GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. β -GWO is comparatively measured using 49 comparative methods. The results obtained by β -GWO outperform others in most test cases. In conclusion, the proposed β -GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain. [ABSTRACT FROM AUTHOR]

In this paper, a new comprehensive learning gravitational search algorithm (CLGSA) is proposed to enhance the performance of basic GSA. The proposed algorithm is a new kind of intelligent optimization algorithm which has better ability to choose good elements. An intensive comprehensive learning methodology is proposed to enrich the optimization ability of the GSA. The efficiency of the proposed algorithm was evaluated by 28 benchmark functions which have been proposed in IEEE-CEC 2013 sessions. The results are compared with eight state-of-the-art algorithms IPOP, BIPOP, NIPOP, NBIPOP, DE/rand, SPSRDEMMS, SPSO-2011 and GSA. A variety of ways are considered to examine the ability of the proposed technique in terms of convergence ability, success rate and statistical behavior of algorithm over dimensions 10, 30 and 50. Apart from experimental studies, theoretical stability of the proposed CLGSA is also proved. It was concluded that the proposed algorithm performed efficiently with good results. [ABSTRACT FROM AUTHOR]

In recent years, the backward trajectory model has been widely used in the research of meteorological and atmospheric environmental quality. This paper presents a comprehensive study on a stepwise clustering analysis algorithm in the clustering process of backward trajectory model and an application of the clustering analysis of single-particle backward trajectory in 2016 in Changchun City. This study starts with an analysis of the original stepwise clustering algorithm and its application to a clustering process of 8784 backward trajectories during 48 h in Changchun City as a benchmark test case. Then, two improvements are made in the algorithm: First, in the process of finding the optimal classification, the algorithm complexity is improved from original O(n3) to O(log(n)*n2) through algorithm improvement. The algorithm performance is enhanced by log(n) times. Second, in the process of re-establishing the classification, the algorithm complexity is improved from the original O(m*n2) to O(m*log(n)*n), that is another algorithm performance improvement by a factor of log(n). Therefore, the accumulative execution efficiency improvement through the algorithm optimization is 2*log(n) times, which has been further verified in the practical application in Changchun City. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new binary version of the flower pollination algorithm (BFPA) for solving 0–1 knapsack problem. The standard flower pollination algorithm (FPA) is used for the continuous optimization problems. So, a transformation function is used to convert the continuous values generated from FPA into binary ones. A penalty function is added to the evaluation function to give negative values for the infeasible solutions. The infeasible solutions are treated by using a two-stage repair operator called flower repair. Experimental results have proved the superiority of BFPA over other algorithms. [ABSTRACT FROM AUTHOR]

Weather describes the condition of our atmosphere during a specific period of time, and climate represents a composite of day to day weather over longer period of time. Climatology attempts to analyze and explain the impact of climate so that the society can plan accordingly. Climatology analysis is often done on radar images representing various climatic conditions. These images contain varying scale of severity for any specific climatic parameter of study. The climatologists often find it convenient to analyze climatic conditions if tools are available to segment the weather images based on the severity scale which is represented by different colors. Segmentation of the weather radar image is also used for automated analysis of weather conditions. Differential evolution (DE) approach instead is used for fast selection of optimal threshold. In present paper, we have applied DE with multilevel thresholding for weather image segmentation which results in minimum computational time and excellent image quality. A new mutation strategy for DE named reconstructed differential evolution (RDE) strategy is suggested for better performance over image segmentation. Using fuzzy entropy and RDE for multilevel thresholding provides better results in comparison with last suggested methods. [ABSTRACT FROM AUTHOR]

Short-term wind speed forecasting has become an important technology to utilize sustainable energy, reduce the impact of wind power grid and improve the control of wind turbines. Current forecasting models based on individual algorithm and hybrid optimization algorithm could be applied to a variety of wind speed forecast. However, these algorithms ignored the limitation of optimizing parameter and the use of recent data, which may result in forecasting poor accuracy. In this paper, integrated approaches, combining singular spectrum analysis technique, cuckoo search algorithm (CSA) and harmony search algorithm (HSA), and back propagation neural network (BPNN), were introduced for conducting short-term wind speed forecasting. Firstly, the SSA technique is used for identifying and extracting instable components from raw wind speed signals. Then, the parameters of BPNN are employed to be optimized by the CSA/HSA, which improve precision of forecast results. Finally, the BPNN is utilized to deal with the wind speed series. The proposed hybrid model is conducted by the wind speed at three stations located in Penglai, China. Our experiment reveals that the proposed hybrid model can generate a more accurate, reliable and robust result and SSA technique is found to be superior technique to preprocess wind speed series. Furthermore, we compare to the forecast results with SSA and non SSA denoising procedure and found that hybrid model with SSA technique outperforms other prediction model. [ABSTRACT FROM AUTHOR]

In this paper, the update process of harmony search (HS) algorithm is modified to improve its concept of diversity. The update process in HS is based on a greedy mechanism in which the new harmony solution, created in each generation, replaces the worst individual in the population, if better. This greedy process could be improved with other updates mechanisms in order to control the diversity perfectly. Three versions of HS have been proposed: (1) Natural Proportional HS ; (2) Natural Tournament HS; (3) Natural Rank HS. These three HS versions employed the natural selection principle of the “survival of the fittest”. Instead of replacing the worst individual in population, any individual can be replaced based on certain criteria. Four versions of economic loading dispatch (ELD) problems with valve point have been used to measure the effect of the newly proposed HS versions. The results show that the new HS versions are very promising for ELD domain. This claim is proved based on the comparative evaluation process where the new HS versions are able to excel the state-of-the-art methods in almost ELD problems used. [ABSTRACT FROM AUTHOR]

In open vehicle routing problem (OVRP), after delivering service to the last customer, the vehicle does not necessarily return to the initial depot. This type of problem originally defined about thirty years ago and still is an open issue. In real life, the OVRP is similar to the delivering newspapers and consignments. The problem of service delivering to a set of customers is a particular open VRP with an identical fleet for transporting vehicles that do not necessarily return to the initial depot. Contractors which are not the employee of the delivery company use their own vehicles and do not return to the depot. Solving the OVRP means to optimize the number of vehicles, the traveling distance and the traveling time of a vehicle. In time, several algorithms such as tabu search, deterministic annealing and neighborhood search were used for solving the OVRP. In this paper, a new combinatorial algorithm named OVRP_GELS based on gravitational emulation local search algorithm for solving the OVRP is proposed. We also used record-to-record algorithm to improve the results of the GELS. Several numerical experiments show a good performance of the proposed method for solving the OVRP when compared with existing techniques. [ABSTRACT FROM AUTHOR]

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases. [ABSTRACT FROM AUTHOR]

Single-hidden-layer feedforward neural network (SLFN) is an effective model for data classification and regression. However, it has a very important defect that it is rather time-consuming to explore the training algorithm of SLFN. In order to shorten the learning time, a special non-iterative learning algorithm was proposed, named as extreme learning machine (ELM). The main idea is that the input weights and bias are chosen randomly and the output weights are calculated by a pseudo-inverse matrix. However, ELM also has a very important drawback that it cannot achieve stable solution for different runs because of randomness. In this paper, we propose a stabilized learning algorithm based on iteration correction. The convergence analysis shows that the proposed algorithm can finish the learning process in fewer steps than the number of neurons. Three theorems and their proofs can prove that the proposed algorithm is stable. Several data sets are selected from UCI databases, and the experimental results demonstrate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is a new stochastic optimization technique for global optimization. In the paper, we introduce both simulated annealing and Gaussian perturbations into the standard bat algorithm so as to enhance its search performance. As a result, we propose a simulated annealing Gaussian bat algorithm (SAGBA) for global optimization. Our proposed algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also speeds up the global convergence rate. We have used BA, simulated annealing particle swarm optimization and SAGBA to carry out numerical experiments for 20 test benchmarks. Our simulation results show that the proposed SAGBA can indeed improve the global convergence. In addition, SAGBA is superior to the other two algorithms in terms of convergence and accuracy. [ABSTRACT FROM AUTHOR]

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Self-organizing overlay networks are emerging as next generation networks capable of adapting to the needs of applications at runtime. Applications performance significantly depends on the structure and behaviors of the underlying self-organizing overlay networks. To achieve desired performance, not only the logical overlay topology but also the behaviors of nodes in this overlay network need to be optimized. Moreover, self-organizing overlay networks are extremely dynamic, unreliable and often large-scale. It is therefore important to design new optimizing approaches to meet these challenges. In this paper, we present an evolutionary optimization methodology for self-organizing overlay network. The optimizations of self-organizing overlay networks are modeled as dynamically evolutionary process, in which the nodes interact with each other, change their internal structures and alter their external links to improve the collective performance. To design appropriate fitness functions and rules that guides the direction of the evolution, overlay network can reach a stable state with desired global application performance eventually. Such a methodology leads to our distributed algorithms for proximity-based overlay topology maintenance and Peer-to-Peer living media streaming, in which every node in the overlay network rewires their behaviors and connectivity according to local available information and embedded rules. These algorithms are shown to perform well using simulations. [ABSTRACT FROM AUTHOR]

Multi-variable generalized predictive control algorithm has obtained great success in process industries. However, it suffers from a high computational cost because the multi-stage optimization approach in the algorithm is time-consuming when constraints of the control system are considered. In this paper, a dual neural network is employed to deal with the multi-stage optimization problem, and bounded constraints on the input and output signals of the control system are taken into account. The dual neural network has many favorable features such as simple structure, rapid execution, and easy implementation. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. In addition, the dual network model can yield the exact optimum values of future control signals while many other neural networks only obtain the approximate optimal solutions. Hence the multi-variable generalized predictive control algorithm based on the dual neural network is suitable for industrial applications with the real-time computation requirement. Simulation examples are given to demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]