589 results
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2. A Multi-population Hierarchical Differential Evolution for Feature Selection
- Author
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Guan, Jian, Yu, Fei, Diao, Zhenya, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Li, Kangshun, editor, and Liu, Yong, editor
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- 2024
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3. Adjusting Exploitation and Exploration Rates of Differential Evolution: A Novel Mutation Strategy
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Duan, Danting, Zhang, Yuhui, Zhong, Wei, Ye, Long, Zhang, Qin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Ye, Long, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor
- Published
- 2024
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4. A Two-Operator Hybrid DE for Global Numerical Optimization
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Li, Xiangping, Huang, Yingqi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Wang, Yong, editor, and Lin, Jianqing, editor
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- 2024
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5. Reinforcement Learning-Based Differential Evolution Algorithm with Levy Flight
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Liu, Xiaoyu, Zhang, Qingke, Xi, Hongtong, Zhang, Huixia, Gao, Shuang, Zhang, Huaxiang, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Wang, Yong, editor, and Lin, Jianqing, editor
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- 2024
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6. Solving Continuous Optimization Problems with a New Hyperheuristic Framework
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Bándi, Nándor, Gaskó, Noémi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos M., editor, and Umeton, Renato, editor
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- 2024
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7. Evolutionary Global Optimization Survival of the Fittest Algorithm
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Perov, D., Kuzenkov, O., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balandin, Dmitry, editor, Barkalov, Konstantin, editor, and Meyerov, Iosif, editor
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- 2024
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8. Transfer Learning-Based Evolutionary Multi-task Optimization
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Li, Shuai, Zhu, Xiaobing, Li, Xi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Wang, Yong, editor, and Lin, Jianqing, editor
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- 2024
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9. Hierarchical Competitive Differential Evolution for Global Optimization
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Xi, Hongtong, Zhang, Qingke, Liu, Xiaoyu, Zhang, Huixia, Gao, Shuang, Zhang, Huaxiang, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Wang, Yong, editor, and Lin, Jianqing, editor
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- 2024
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10. Resource Dispatch Optimization for Firefighting Using a Differential Evolution Algorithm
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Matos, Marina A., Gonçalves, Rui, Rocha, Ana Maria A. C., Costa, Lino A., Alvelos, Filipe, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pereira, Ana I., editor, Mendes, Armando, editor, Fernandes, Florbela P., editor, Pacheco, Maria F., editor, Coelho, João P., editor, and Lima, José, editor
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- 2024
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11. Special Issue: "2022 and 2023 Selected Papers from Algorithms' Editorial Board Members".
- Author
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Werner, Frank
- Subjects
EDITORIAL boards ,ALGORITHMS ,OPTIMIZATION algorithms ,DIFFERENTIAL evolution ,QUADRATIC assignment problem ,MACHINE learning ,TABU search algorithm - Abstract
This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]
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- 2024
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12. Multi-objective optimization design of anti-roll torsion bar using improved beluga whale optimization algorithm
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Li, Yonghua, Chen, Zhe, Hou, Maorui, and Guo, Tao
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- 2024
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13. Multi-agent game operation of regional integrated energy system based on carbon emission flow.
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Li Zhang, Dong Pan, Jianxiong Jia, Zhumeng Song, Xin Zhang, Nan Shang, and Jinshuo Su
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CARBON emissions ,CARBON pricing ,SUPPLY & demand ,DIFFERENTIAL evolution ,REINFORCEMENT learning ,POWER resources ,ELECTRICITY pricing - Abstract
In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision- making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization.
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He, Ji, Tang, Yefeng, Guo, Xiaoqi, Chen, Haitao, and Guo, Wen
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OPTIMIZATION algorithms ,WALRUS ,FLOOD control ,CONSTRAINT algorithms ,CONSTRAINED optimization ,DIFFERENTIAL evolution ,PARTICLE swarm optimization ,IMAGE enhancement (Imaging systems) - Abstract
Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]
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- 2024
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15. The Application of Machine Learning in Geotechnical Engineering.
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Gao, Wei
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MACHINE learning ,DIFFERENTIAL evolution ,GEOTECHNICAL engineering ,ARTIFICIAL neural networks ,BUILDING foundations ,ARTIFICIAL intelligence ,METALLIC surfaces ,ROCK slopes - Abstract
This document provides a summary of a special issue on the application of machine learning in geotechnical engineering. The issue includes 19 articles that explore different applications of machine learning in this field, such as determining geotechnical parameters, predicting geotechnical disasters, and optimizing construction processes. The articles cover various topics in geotechnical engineering, including underground and foundation engineering, and discuss the use of machine learning algorithms to predict and estimate various parameters and behaviors. While machine learning shows potential in improving predictions, the papers also acknowledge the limitations of purely data-driven models and the need to incorporate mechanical models and improve data collection methods. Overall, these articles provide valuable insights and serve as a starting point for future research in the field. [Extracted from the article]
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- 2024
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16. A novel differential evolution algorithm with multi-population and elites regeneration.
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Cao, Yang and Luan, Jingzheng
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DIFFERENTIAL evolution ,EVOLUTIONARY algorithms ,DISTRIBUTION (Probability theory) ,ALGORITHMS ,GLOBAL optimization - Abstract
Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm.
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Zhu, Yue and Zhao, Tao
- Abstract
The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated based on the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition among the structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and the parameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of the model. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725, respectively with rule counts of rules is 8 and 12, respectively. When compared to alternative methods, the results indicate that the proposed method offers improvements in accuracy and rule counts. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm.
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Hao, Jie, Guo, Xiuting, Li, Yan, and Wu, Tao
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FUZZY sets ,AFFINE transformations ,WIND power ,LINEAR programming ,ENERGY development ,DIFFERENTIAL evolution ,SIMPLEX algorithm - Abstract
The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.
- Author
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Xu, Ning, Li, Zhihe, Guo, Na, Wang, Te, Li, Aijuan, and Song, Yumin
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SEARCH algorithms ,GENETIC algorithms ,CUBIC curves ,SAFETY factor in engineering ,AGRICULTURAL development ,DIFFERENTIAL evolution - Abstract
With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective. [ABSTRACT FROM AUTHOR]
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- 2024
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20. Accurate Prediction of Dissolved Oxygen in Perch Aquaculture Water by DE-GWO-SVR Hybrid Optimization Model.
- Author
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Bao, Xingsheng, Jiang, Yilun, Zhang, Lintong, Liu, Bo, Chen, Linjie, Zhang, Wenqing, Xie, Lihang, Liu, Xinze, Qu, Fangfang, and Wu, Renye
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GREY Wolf Optimizer algorithm ,WATER quality monitoring ,DIFFERENTIAL evolution ,WATER quality ,BODIES of water ,PEARSON correlation (Statistics) ,DISSOLVED oxygen in water ,IONIC conductivity - Abstract
In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf Optimizer (GWO), shortened to DE-GWO-SVR, to predict the DO content with the characteristics of nonlinear and non-smooth water quality data. Experimentally, data for the water quality, including pH, water temperature, conductivity, salinity, total dissolved solids, and DO, were collected. Pearson's correlation coefficient (PPMCC) was applied to explore the correlation between each water quality parameter and DO content. The optimal DE-GWO-SVR model was established and compared with models based on SVR, back-propagation neural network (BPNN), and their optimization models. The results show that the DE-GWO-SVR model proposed in this paper can effectively realize the nonlinear prediction and global optimization performance. Its R
2 , MSE, MAE and RMSE can be up to 0.94, 0.108, 0.2629, and 0.3293, respectively, which is better than those of other models. This research provides guidance for the efficient prediction of DO in perch aquaculture water bodies for increasing the aquaculture effectiveness and reducing the aquaculture risk, providing a new exploratory path for water quality monitoring. [ABSTRACT FROM AUTHOR]- Published
- 2024
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21. Comparative Evaluation of the Application Effectiveness of Intelligent Production Optimization Methods in Offshore Oil Reservoirs.
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Liu, Chen, Feng, Qihong, Zhang, Kai, Wang, Jialin, and Lin, Jingqi
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DIFFERENTIAL evolution ,PETROLEUM reservoirs ,EVOLUTIONARY computation ,OPTIMIZATION algorithms ,EVOLUTIONARY algorithms ,PRODUCTION methods ,PETROLEUM in submerged lands ,ARTIFICIAL intelligence - Abstract
The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization. [ABSTRACT FROM AUTHOR]
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- 2024
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22. An Underwater Passive Electric Field Positioning Method Based on Scalar Potential.
- Author
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Zhang, Yi, Chen, Cong, Sun, Jiaqing, Qiu, Mingjie, and Wu, Xu
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ELECTRIC fields ,DIFFERENTIAL evolution ,SUBMERSIBLES ,UNDERWATER acoustics ,SENSOR arrays ,MATHEMATICAL models - Abstract
In order to fulfill the practical application demands of precisely localizing underwater vehicles using passive electric field localization technology, we propose a scalar-potential-based method for the passive electric field localization of underwater vehicles. This method is grounded on an intelligent differential evolution algorithm and is particularly suited for use in three-layer and stratified oceanic environments. Firstly, based on the potential distribution law of constant current elements in a three-layer parallel stratified ocean environment, the mathematical positioning model is established using the mirror method. Secondly, the differential evolution (DE) algorithm is enhanced with a parameter-adaptive strategy and a boundary mutation processing mechanism to optimize the key objective function in the positioning problem. Additionally, the simulation experiments of the current element in the layered model prove the effectiveness of the proposed positioning method and show that it has no special requirements for the sensor measurement array, but the large range and moderate number of sensors are beneficial to improve the positioning effect. Finally, the laboratory experiments on the positioning method proposed in this paper, involving underwater simulated current elements and underwater vehicle tracks, were carried out successfully. The results indicate that the positioning method proposed in this paper can achieve the performance requirements of independent initial value, strong anti-noise capabilities, rapid positioning speed, easy implementation, and suitability in shallow sea environments. These findings suggest a promising practical application potential for the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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23. Optimization Design Method for Vibration Reduction of Frame Structure of Cable-Driven Parallel Robot.
- Author
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Da Song, Zhichao Sun, Lei Zhao, Ming Lu, Haocheng Wang, and Lixun Zhang
- Subjects
PARALLEL robots ,CABLE structures ,STRUCTURAL frames ,OPTIMIZATION algorithms ,ALUMINUM construction ,DIFFERENTIAL evolution - Abstract
This paper proposes a comprehensive optimization algorithm for the crossbeam cross-section size to solve the vibration problem of the flexible cable-driven parallel robot frame. The goal is to achieve an innovative design of the cross-sectional structure. By using a differential evolution algorithm, a comprehensive scale analysis of the cross-section size of the beam is carried out. This optimization method effectively improves the cross-section moment of inertia and the bending stiffness of the beam. Additionally, based on the variable density penalty method, the topological optimization design of the cross-section is carried out under the condition of minimum flexibility, the optimization objective function is established, and the objective function is solved by the moving asymptote method. The optimization results show that the beam stiffness is maximized while meeting the minimum flexibility required by the project. Through comparative analysis with various aluminum profile structures and their constructed frames, it is verified that the cross-sectional shape obtained through the optimized design method can significantly improve the anti-vibration stability of the frame. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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24. An energy‐efficient timetable optimization method for express/local train with on‐board passenger number considered.
- Author
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Liu, Zhen, Pan, Jinshan, Yang, Yuhua, and Chi, Xinyi
- Subjects
PASSENGER trains ,EXPRESS trains ,TIME perspective ,TRAVEL time (Traffic engineering) ,ENERGY consumption ,DIFFERENTIAL evolution ,ROLLING stock ,TRAIN schedules - Abstract
With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers' weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. A modified evolutionary reinforcement learning for multi-agent region protection with fewer defenders.
- Author
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Sun, Siqing, Dong, Huachao, and Li, Tianbo
- Subjects
DEEP reinforcement learning ,REWARD (Psychology) ,REINFORCEMENT learning ,MULTIAGENT systems ,EVOLUTIONARY algorithms ,DIFFERENTIAL evolution - Abstract
Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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26. Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems.
- Author
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Nishihara, Kei and Nakata, Masaya
- Subjects
DIFFERENTIAL evolution ,BIOLOGICAL evolution ,EVOLUTIONARY algorithms ,ALGORITHMS - Abstract
In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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27. RESEARCH ON DYNAMIC OPTIMIZATION ALGORITHM OF WAREHOUSING LOCATION LAYOUT BASED ON NONLINEAR OPTIMIZATION.
- Author
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GUANG CHEN, ZHIWEI TU, SHENG ZHANG, JING FANG, and FAN SHE
- Subjects
OPTIMIZATION algorithms ,WAREHOUSES ,BIOLOGICAL evolution ,BIG data ,DIFFERENTIAL evolution ,WAREHOUSE management - Abstract
The paper aims to improve the turnover rate and operation efficiency of goods that are shipped out and replenished in the warehouses of electric power enterprises through big data analysis and optimization algorithms. The data is distributed in diverse locations and data nonlinear optimization algorithms certainly helps to understand the patterns for effective management of warehouses. This article focuses on reducing the delay in the operational processes.A multi-objective optimization (MOO) has been proposed which is aiming at improving the efficiency of transition process of commodities, storage, and overall warehouse operations. The study helps in the optimization of the allocation of cargo spaces with the aid of big data analysis optimization technology which collects and manages data in a distributed environment. A multi-objective cargo space optimization algorithm is proposed along with consideration of dynamic constraints. The algorithm is based on the coefficient of variation adaptive differential evolution algorithm.Individual decoding is performed according to the real-time cargo space availability. The simulation results show that the convergence speed of the algorithm is greatly improved.Meanwhile, the efficiency of warehouse transition process, shelf stability and the classification of commodities are remarkably improved.In nutshell, the multi-objective decision-making with the integration of big data analysis optimization technology assists in the effective organization of warehouse allocation system by considering multiple factors and constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. UNCREWED BOAT PATH PLANNING ALGORITHM BASED ON EVOLUTIONARY POTENTIAL FIELD MODEL IN DENSE OBSTACLE ENVIRONMENT.
- Author
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WEI ZHENG and XIN HUANG
- Subjects
EVOLUTIONARY algorithms ,DIFFERENTIAL evolution ,ALGORITHMS - Abstract
In the trajectory planning of crewless ships, the artificial potential field method is commonly used. The results obtained using the classic potential field model for path design are not optimal and cannot fully meet the trajectory design requirements of uncrewed ships. This paper uses the evolutionary potential field model for trajectory planning. The evaluation formula of the potential path is combined with the differential evolution algorithm to evaluate and optimize the potential. A quadratic optimization smoothing algorithm is designed to limit the maximum turning angle of the uncrewed ship. Simulation experiments show that this method is effective and reliable. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
29. ITERATIVE DECODING OF SHORT LOW-DENSITY PARITY-CHECK CODES BASED ON DIFFERENTIAL EVOLUTION.
- Author
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Shtompel, Mykola and Prykhodko, Sergii
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DIFFERENTIAL evolution ,ITERATIVE decoding ,DECODING algorithms ,PARITY-check matrix ,ERROR-correcting codes - Abstract
Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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30. A continuous and long-term in-situ stress measuring method based on fiber optic. Part I: Theory of inverse differential strain analysis.
- Author
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Kun-Peng Zhang, Mian Chen, Chang-Jun Zhao, Su Wang, and Yong-Dong Fan
- Subjects
OPTIMIZATION algorithms ,ROCK properties ,FIBER optics ,SPATIAL resolution ,DIFFERENTIAL evolution - Abstract
A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stress measurements with high spatial resolution and frequency, significantly enhancing the ability to measure in-situ stress. The sensing casing, spirally wrapped with fiber optic, is cemented into the formation to establish a formation sensing nerve. Injecting fluid into the casing generates strain disturbance, establishing the relationship between rock mass properties and treatment pressure. Moreover, an optimization algorithm is established to invert the elastic parameters of formation via fiber optic strains. In the first part of this paper series, we established the theoretical basis for the inverse differential strain analysis method for in-situ stress measurement, which was subsequently verified using an analytical model. This paper is the fundamental basis for the inverse differential strain analysis method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment.
- Author
-
Ghafari, R. and Mansouri, N.
- Subjects
DIFFERENTIAL evolution ,SCHEDULING ,CUSTOMER satisfaction ,PRODUCTION scheduling ,ENERGY consumption - Abstract
Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. RISOPA: Rapid Imperceptible Strong One-Pixel Attacks in Deep Neural Networks.
- Author
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Nam, Wonhong, Kim, Kunha, Moon, Hyunwoo, Noh, Hyeongmin, Park, Jiyeon, and Kil, Hyunyoung
- Subjects
ARTIFICIAL neural networks ,DIFFERENTIAL evolution ,CONVOLUTIONAL neural networks ,RANDOM walks ,MACHINE learning - Abstract
Recent research has revealed that subtle imperceptible perturbations can deceive well-trained neural network models, leading to inaccurate outcomes. These instances, known as adversarial examples, pose significant threats to the secure application of machine learning techniques in safety-critical systems. In this paper, we delve into the study of one-pixel attacks in deep neural networks, recently reported as a kind of adversarial examples. To identify such one-pixel attacks, most existing methodologies rely on the differential evolution method, which utilizes random selection from the current population to escape local optima. However, the differential evolution technique might waste search time and overlook good solutions if the number of iterations is insufficient. Hence, in this paper, we propose a gradient ascent with momentum approach to efficiently discover good solutions for the one-pixel attack problem. As our method takes a more direct route to the goal compared to existing methods relying on blind random walks, it can effectively identify one-pixel attacks. Our experiments conducted on popular CNNs demonstrate that, in comparison with existing methodologies, our technique can detect one-pixel attacks significantly faster. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems.
- Author
-
Yang, Qingyong, Chu, Shu-Chuan, Pan, Jeng-Shyang, Chou, Jyh-Horng, and Watada, Junzo
- Subjects
DIFFERENTIAL evolution ,REINFORCEMENT learning ,ALGORITHMS ,ENGINEERING design ,SET functions ,RANDOM sets - Abstract
The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Optimization Operation Strategy for Shared Energy Storage and Regional Integrated Energy Systems Based on Multi-Level Game.
- Author
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Yang, Yulong, Chen, Tao, Yan, Han, Wang, Jiaqi, Yan, Zhongwen, and Liu, Weiyang
- Subjects
ENERGY storage ,DIFFERENTIAL evolution ,ECONOMIC efficiency - Abstract
Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Differential protection scheme for distribution network with distributed generation based on improved feature mode decomposition and derivative dynamic time warping.
- Author
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Wang, Lei, Song, Xin, Jiang, Weijian, Okedu, Kenneth E., and Jianjun, Ma
- Subjects
DISTRIBUTED power generation ,METAHEURISTIC algorithms ,TELECOMMUNICATION ,POWER resources ,DIFFERENTIAL evolution ,ELECTRON tube grids ,CURRENT distribution - Abstract
With the progress of communication technology, the cost of optical fiber and 5G continues to decrease, and data transmission becomes more convenient and fast, making it possible to realize differential protection of distribution network by various intelligent algorithms using signal waveforms. Aiming at the problem that the traditional relay protection device can not meet the actual demand when the single-phase ground fault occurs in the distribution network with distributed generation, this paper proposes a new differential protection scheme. The characteristic mode decomposition improved by the whale optimization algorithm is used to decompose the zero-sequence current waveform collected at both ends of the line. Based on the basic principle of current differential protection, the derivative dynamic time warping of the component with the largest fault feature can effectively solve the problem that the grounding current of the distribution network cannot meet the working requirements of the differential protection device, and ensure the safe and stable operation of the system. Finally, based on MATLAB software, the performance of this method is comprehensively evaluated by simulating different fault conditions, so as to ensure the feasibility and accuracy of this method in the case of diversified faults when the distributed generation is used as part of the power supply. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. Keywords--Robot Manipulators; Fuzzy Neural Network; Sliding Mode Control; Robust Adaptive Control.
- Author
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Patil, Ramakant S., Jadhav, Sharad P., and Patil, Machhindranath D.
- Subjects
SLIDING mode control ,ADAPTIVE fuzzy control ,ANT algorithms ,FUZZY neural networks ,ADAPTIVE control systems ,ROBUST control ,EVOLUTIONARY algorithms ,MANIPULATORS (Machinery) - Abstract
PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
37. Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm.
- Author
-
Yue Zhu and Tao Zhao
- Abstract
The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS’s deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated based on the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition among the structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and the parameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of the model. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725, respectively with rule counts of rules is 8 and 12, respectively. When compared to alternative methods, the results indicate that the proposed method offers improvements in accuracy and rule counts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Comparison of a Hybrid Firefly–Particle Swarm Optimization Algorithm with Six Hybrid Firefly–Differential Evolution Algorithms and an Effective Cost-Saving Allocation Method for Ridesharing Recommendation Systems.
- Author
-
Hsieh, Fu-Shiung
- Subjects
METAHEURISTIC algorithms ,OPTIMIZATION algorithms ,RECOMMENDER systems ,RIDESHARING ,PARTICLE swarm optimization ,DIFFERENTIAL evolution ,ALGORITHMS - Abstract
The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy.
- Author
-
Huang, Yawei, Qian, Xuezhong, and Song, Wei
- Subjects
DIFFERENTIAL evolution ,MATHEMATICAL optimization ,INFORMATION sharing - Abstract
The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population's capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm's performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. An improved method for classifying depth-based human actions using self-adaptive evolutionary technique.
- Author
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Pareek, Preksha and Thakkar, Ankit
- Abstract
Automatic Human Action Recognition (HAR) using RGB-D (Red, Green, Blue, and Depth) videos captivated a lot of attention in the pattern classification field due to low-cost depth cameras. Feature extraction in action recognition is an important aspect. As compared to Depth Motion Maps (DMM), Depth Motion Maps–Local Binary Pattern (DMM–LBP) provides compact representation of features. After extracting features using DMM–LBP, Principal Component Analysis (PCA) is used for dimensionality reduction. For classification task, randomly generated input weights of Extreme Learning Machine (ELM) can lead to non-optimal results. Therefore, in this paper, we have used an improved learning algorithm named Self-adaptive Differential Evolution (SaDE) ELM for action classification. In the proposed approach, DMM–LBP is used for feature extraction and SaDE–ELM is used for action classification. To evaluate strength of the proposed approach, experiments are performed on four public datasets, namely, MSRAction3D, MSRDaily Activity3D, UTD–MHAD, and MSRGesture3D. The proposed approach gives better accuracy compared to existing approaches Kernel ELM (KELM), l2-Collaborative Representation Classifier (l2-CRC), and Probabilistic CRC (ProCRC) methods. We have also presented statistical significance of results in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. Automatic Column Grouping of 3D Steel Frames via Multi-Objective Structural Optimization.
- Author
-
Resende, Cláudio, Martha, Luiz Fernando, Lemonge, Afonso, Hallak, Patricia, Carvalho, José, and Motta, Júlia
- Subjects
COLUMNS ,STEEL framing ,STRUCTURAL optimization ,DIFFERENTIAL evolution ,EVOLUTIONARY algorithms ,STEEL buildings - Abstract
Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure's weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem.
- Author
-
Liang Zeng, Junyang Shi, Yanyan Li, Shanshan Wang, and Weigang Li
- Subjects
PRODUCTION scheduling ,DIFFERENTIAL evolution ,OPTIMIZATION algorithms ,FLOW shops ,ALGORITHMS ,MANUFACTURING processes ,COMBINATORIAL optimization - Abstract
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution (NSGA-III-SD). By incorporating constrained differential evolution and simulated binary crossover genetic operators, this algorithm effectively improves NSGA-III's global search capability while mitigating premature convergence issues. Furthermore, it introduces a reinforced dominance relation to address the tradeoff between convergence and diversity in NSGA-III. Additionally, effective encoding and decoding methods for discrete job shop scheduling are proposed, which can improve the overall performance of the algorithm without complex computation. To validate the algorithm's effectiveness, NSGA-III-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances. The experimental results demonstrate that NSGA-III-SD achieves better solution quality and diversity, proving its effectiveness in solving the multi-objective job shop scheduling problem. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Parameter estimation of solar PV models using self-adaptive differential evolution with dynamic mutation and pheromone strategy.
- Author
-
Singsathid, Pirapong, Wetweerapong, Jeerayut, and Puphasuk, Pikul
- Subjects
DIFFERENTIAL evolution ,PARAMETER estimation ,PHEROMONES - Abstract
In this paper we investigate the parameter estimation of solar photovoltaic (PV) models using the self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The dynamic mutation divides the population into three groups according to fitness values and selects groups and their vectors with adaptive probabilities to create a mutant vector. The algorithm also encodes scaling factor and crossover rate values into target vectors to use in mutation and crossover operations and adjusts them with pheromones in the selection process. Experimental results show that the SDE-FMP algorithm can give the solutions with the lowest errors and is overall competitive with the compared methods regarding the mean errors. [ABSTRACT FROM AUTHOR]
- Published
- 2024
44. Multi agent collaborative search algorithm with adaptive weights.
- Author
-
Cao, Li, Wang, Maocai, Vasile, Massimiliano, and Dai, Guangming
- Subjects
- *
EVOLUTIONARY algorithms , *DIFFERENTIAL evolution , *UTILITY functions , *SOCIAL action , *SEARCH algorithms - Abstract
This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Empowering Diabetic Eye Disease Detection: Leveraging Differential Evolution for Optimized Convolution Neural Networks.
- Author
-
Ray, Rahul, Jena, Sudarson, Parida, Priyadarsan, Dash, Laxminarayan, and Biswal, Sangita Kumari
- Subjects
CONVOLUTIONAL neural networks ,DIFFERENTIAL evolution ,EYE diseases ,DIABETIC retinopathy ,RETINAL blood vessels - Abstract
Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Differential Evolution Algorithm with Three Mutation Operators for Global Optimization.
- Author
-
Wang, Xuming and Yu, Xiaobing
- Subjects
EVOLUTIONARY algorithms ,ARTIFICIAL intelligence ,GLOBAL optimization ,ALGORITHMS ,DIFFERENTIAL evolution ,BENCHES - Abstract
Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. A novel interpretable predictive model based on ensemble learning and differential evolution algorithm for surface roughness prediction in abrasive water jet polishing.
- Author
-
Xie, Shutong, He, Zongbao, Loh, Yee Man, Yang, Yu, Liu, Kunhong, Liu, Chao, Cheung, Chi Fai, Yu, Nan, and Wang, Chunjin
- Subjects
DIFFERENTIAL evolution ,WATER jets ,PREDICTION models ,MACHINE learning ,ABRASIVES ,ALGORITHMS ,GRINDING & polishing ,SURFACE roughness - Abstract
As an important indicator of the surface quality of workpieces, surface roughness has a great impact on production costs and the quality performance of the finished components. Effective surface roughness prediction can not only increase productivity but also reduce costs. However, the current methods for surface roughness prediction have some limitations. On the one hand, the prediction accuracy of classical experimental and statistical-based surface roughness prediction methods is low. On the other hand, the results of deep learning-based surface roughness prediction methods are uninterpretable due to their black-box learning mechanism. Therefore, this paper presents an ensemble learning with a differential evolution algorithm, applies it to the prediction of surface roughness of abrasive water jet polishing (AWJP), and conducts an interpretability analysis to identify key factors contributing to the prediction accuracy of surface roughness. First, we proposed automatically constructing features by an Evolution Forest algorithm to train the base regression models. The differential evolution algorithm with a simplified encoding mechanism was then used to search for the best weighted-ensemble to integrate the base regression models for obtaining highly accurate prediction results. Extensive experiments have been conducted on AWJP to validate the effectiveness of our proposed methods. The results show that the prediction accuracy of our proposed method is higher than the existing machine learning algorithms. In addition, this is the first of its time for the contributions of machining parameters (i.e., features) on surface roughness prediction by using interpretable analysis methods. The analysis results can provide a reference basis for subsequent experiments and studies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Advanced differential evolution for gender-aware English speech emotion recognition.
- Author
-
Yue, Liya, Hu, Pei, and Zhu, Jiulong
- Subjects
EMOTION recognition ,GENDER differences (Psychology) ,GENDER differences (Sociology) ,FEATURE extraction ,ENGLISH language ,DIFFERENTIAL evolution - Abstract
Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. A metaheuristic-based algorithm for optimizing node deployment in wireless sensor network.
- Author
-
Xie, Meng, Pi, Dechang, Dai, Chenglong, and Xu, Yue
- Subjects
- *
WIRELESS sensor networks , *WIRELESS sensor nodes , *METAHEURISTIC algorithms , *DIFFERENTIAL evolution , *TELECOMMUNICATION systems , *ALGORITHMS - Abstract
Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring.
- Author
-
Zhou, Peng, Chen, Wei, Wang, Jing, Wang, Huan, Zhang, Yunfeng, Cao, Bingyu, Sun, Shan, and He, Lina
- Subjects
AGRICULTURE ,WIRELESS sensor networks ,INTERNET of things ,DATA transmission systems ,CROP management ,DIFFERENTIAL evolution ,FRUIT rots - Abstract
The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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