多种群约束多目标优化算法的资源分配指标设计
Resource Allocation Indicator Design of Multi-Population-Based Constrained Multi-Objective Optimization Algorithm
DOI: 10.12677/ORF.2023.132106, PDF,   
作者: 方静静：广东工业大学数学与统计学院，广东 广州
关键词: 约束多目标；优化；多种群；资源分配；Constrained Multi-Objective； Optimization； Multi-Population； Resource Allocation
摘要: 本文研究基于多种群的约束多目标优化算法的种群间资源分配问题。本文提出了一个新的指标来衡量种群资源的合理分配。所提出的指标根据不同种群的变化及进化代数，为不同种群分配进化资源。它根据种群中理想点和最差点的变化，来衡量该种群的变化。所提出的指标被嵌入到一个先进的多种群约束多目标算法。在实验中，通过在20个基准函数的数据实验，本文展示了所提出指标可以有效地为不同种群分配资源。
Abstract: In this paper, we study the problem of resource allocation among multiple populations on multi-populations-based constrained multi-objective optimization algorithm. This paper proposes a new indicator to measure the allocation of population evolutionary resources. The proposed indicator allocates evolutionary resources for different populations according to the changes of different populations and evolutionary generations. It measures the change of the population according to the change of the ideal point and the nadir point in the population. The proposed indicator is embedded into an advanced multi-population constrained multi-objective algorithm. In the numerical experiment, this paper shows that the proposed indicators can effectively allocate resources for different populations on 20 benchmark functions.
文章引用：方静静. 多种群约束多目标优化算法的资源分配指标设计[J]. 运筹与模糊学, 2023, 13(2): 1027-1034. https://doi.org/10.12677/ORF.2023.132106

参考文献

[1] Liang, J., Ban, X.X., Yu, K.J., et al. (2022) A Survey on Evolutionary Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 27, 201-221. [Google Scholar] [CrossRef] 
[2] Fan, Z., Li, W., Cai, X., et al. (2019) An Improved Epsilon Constraint-Handling Method in MOEA/D for CMOPs with Large Infeasible Regions. Soft Computing, 23, 12491-12510. [Google Scholar] [CrossRef] 
[3] Fan, Z., Li, W., Cai, X., et al. (2019) Push and Pull Search for Solving Constrained Multi-Objective Optimization Problems. Swarm and Evolutionary Computation, 44, 665-679. [Google Scholar] [CrossRef] 
[4] Wang, J., Liang, G. and Zhang, J. (2018) Cooperative Differential Evolution Framework for Constrained Multiobjective Optimization. IEEE Transactions on Cybernetics, 49, 2060-2072. [Google Scholar] [CrossRef] 
[5] Li, K., Chen, R., Fu, G., et al. (2018) Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 23, 303-315. [Google Scholar] [CrossRef] 
[6] Yuan, J., Liu, H.L., Ong, Y.S., et al. (2021) Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimiza-tion Problems. IEEE Transactions on Evolutionary Computation, 26, 379-391. [Google Scholar] [CrossRef] 
[7] Tian, Y., Zhang, T., Xiao, J., et al. (2020) A Coevolutionary Framework for Constrained Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computa-tion, 25, 102-116. [Google Scholar] [CrossRef] 
[8] Asafuddoula, M., Ray, T. and Sarker, R. (2015) A De-composition-Based Evolutionary Algorithm for Many Objective Optimization. IEEE Transactions on Evolutionary Computation, 19, 445-460. [Google Scholar] [CrossRef] 
[9] Zhou, Y., Zhu, M., Wang, J., et al. (2018) Tri-Goal Evolu-tion Framework for Constrained Many-Objective Optimization. IEEE Transactions on Systems, Man, and Cyber-netics: Systems, 50, 3086-3099. [Google Scholar] [CrossRef] 
[10] Liu, Z.Z. and Wang, Y. (2019) Handling Constrained Multiobjective Optimization Problems with Constraints in Both the Decision and Objective Spaces. IEEE Transac-tions on Evolutionary Computation, 23, 870-884. [Google Scholar] [CrossRef] 
[11] Ma, Z. and Wang, Y. (2019) Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons. IEEE Transactions on Evolu-tionary Computation, 23, 972-986. [Google Scholar] [CrossRef] 
[12] Bosman, P.A.N. and Thierens, D. (2003) The Balance be-tween Proximity and Diversity in Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 7, 174-188. [Google Scholar] [CrossRef] 
[13] Zitzler, E. and Thiele, L. (1998) Multiobjective Optimization Using Evolutionary Algorithms—A Comparative Case Study. Parallel Problem Solving from Nature—PPSN V: 5th International Conference, Amsterdam, 27-30 September 1998, 292-301. [Google Scholar] [CrossRef] 

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