Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization

瓶颈 数学优化 粒子群优化 计算机科学 进化算法 元启发式 人口 趋同(经济学) 多目标优化 进化计算 多群优化 最优化问题 人工智能 数学 嵌入式系统
作者
Xiaofang Liu,Zhi-Hui Zhan,Ying Gao,Jie Zhang,Sam Kwong,Jun Zhang
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:23 (4): 587-602 被引量:150
标识
DOI:10.1109/tevc.2018.2875430
摘要

The application of multiobjective evolutionary algorithms to many-objective optimization problems often faces challenges in terms of diversity and convergence. On the one hand, with a limited population size, it is difficult for an algorithm to cover different parts of the whole Pareto front (PF) in a large objective space. The algorithm tends to concentrate only on limited areas. On the other hand, as the number of objectives increases, solutions easily have poor values on some objectives, which can be regarded as poor bottleneck objectives that restrict solutions’ convergence to the PF. Thus, we propose a coevolutionary particle swarm optimization with a bottleneck objective learning (BOL) strategy for many-objective optimization. In the proposed algorithm, multiple swarms coevolve in distributed fashion to maintain diversity for approximating different parts of the whole PF, and a novel BOL strategy is developed to improve convergence on all objectives. In addition, we develop a solution reproduction procedure with both an elitist learning strategy (ELS) and a juncture learning strategy (JLS) to improve the quality of archived solutions. The ELS helps the algorithm to jump out of local PFs, and the JLS helps to reach out to the missing areas of the PF that are easily missed by the swarms. The performance of the proposed algorithm is evaluated using two widely used test suites with different numbers of objectives. Experimental results show that the proposed algorithm compares favorably with six other state-of-the-art algorithms on many-objective optimization.
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