An adaptive reference vector and reference point based many-objective evolutionary algorithm

被引：0
|
作者：
Qin H. [1 ]
Li J.-H. [1 ]
Li M. [1 ]
Xu S.-S. [1 ]
机构：
[1] Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang
来源：
Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 03期
关键词：
adaptive reference vector and reference point; decomposing population; evolutionary algorithms; many-objective evolutionary algorithm;
D O I：
10.13195/j.kzyjc.2022.0640
中图分类号：
学科分类号：
摘要：
The research shows that the existing multi-objective evolutionary algorithms are difficult to effectively balance the convergence and diversity of the population when dealing with optimization problems with different Pareto fronts. To address the above situation, this paper proposes an adaptive reference vector and reference point based many-objective evlolutionary algorithm (ARVRPMEA), which mainly uses population sparsity to adaptively adjust reference vectors and reference points to improve population diversity. First, the ARVRPMEA generates a uniformly distributed subset of reference vectors and a subset of reference points, and uses this subset of reference vectors to decompose the population. Then, new reference vectors and reference points are generated according to the distribution of solutions in the largest subpopulation until the scale of the reference vector set and reference point set is satisfied. Finally, to further improve population convergence, the algorithm combines the metrics for environmental selection to preserve the individuals with higher convergence into the next generation of populations. The experimental results show that the ARVRPMEA has good performance in solving problems with different Pareto fronts. © 2024 Northeast University. All rights reserved.
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页码：759 / 767
页数：8
相关论文
共 37 条
  • [1] He C, Li M, Zhang C X, Et al., A competitive swarm optimizer with probabilistic criteria for many-objective optimization problems, Complex & Intelligent Systems, 8, 6, pp. 4697-4725, (2022)
  • [2] Lei H T, Wang R, Zhang T, Et al., A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center, Computers & Operations Research, 75, pp. 103-117, (2016)
  • [3] Gaspar-Cunha A, Covas J A., Robustness in multi-objective optimization using evolutionary algorithms, Computational Optimization and Applications, 39, 1, pp. 75-96, (2008)
  • [4] Zhang J W, Xing L N., A survey of multiobjective evolutionary algorithms, IEEE International Conference on Computational Science and Engineering and IEEE International Conference on Embedded and Ubiquitous Computing, pp. 93-100, (2017)
  • [5] Fleming P J, Purshouse R C, Lygoe R J., Many-objective optimization: An engineering design perspective, Evolutionary Multi-Criterion Optimization, pp. 14-32, (2005)
  • [6] Liu J C, Li F, Wang H H, Et al., Survey on evolutionary many-objective optimization algorithms, Control and Decision, 33, 5, pp. 879-887, (2018)
  • [7] Laware A R, Talange D B, Bandal V S., Evolutionary optimization of sliding mode controller for level control system, ISA Transactions, 83, pp. 199-213, (2018)
  • [8] Hu Z Y, Yang J M, Cui H H, Et al., MOEA3D: A MOEA based on dominance and decomposition with probability distribution model, Soft Computing, 23, 4, pp. 1219-1237, (2019)
  • [9] Hussien A G, Amin M, Wang M J, Et al., Crow search algorithm: Theory, recent advances, and applications, IEEE Access, 8, pp. 173548-173565, (2020)
  • [10] Abualigah L, Elaziz M A, Hussien A G, Et al., Lightning search algorithm: A comprehensive survey, Applied Intelligence, 51, 4, pp. 2353-2376, (2021)
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