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Multi-objective particle swarm optimization based on cooperative hybrid strategy
Applied Intelligence ( IF 5.3 ) Pub Date : 2019-07-20 , DOI: 10.1007/s10489-019-01496-3
Hui Yu , YuJia Wang , ShanLi Xiao

Abstract

A multi-objective particle swarm optimization based on cooperative hybrid strategy (CHSPSO) is presented in this paper to solve complex multi-objective problems. Most algorithms usually contain only one strategy, which makes them unable to trade off the convergence and diversity when solving the complex multi-objective problems. The proposed cooperative hybrid strategy can effectively guarantee the convergence and the diversity of the algorithm. The multi-population strategy and the dynamic clustering strategy are employed to improve the convergence and the diversity. At the same time, the life strategy and lottery probability selection strategy are used to further ensure the diversity of the population. A series of test functions are used to verify the effectiveness of CHSPSO. The performance of the proposed algorithm is compared with other evolutionary algorithms. The results show that CHSPSO can obtain a better convergence and diversity for the complex multi-objective problems.



中文翻译:

基于协同混合策略的多目标粒子群算法

摘要

为了解决复杂的多目标问题,提出了一种基于合作混合策略的多目标粒子群算法。大多数算法通常只包含一种策略,这使得它们在解决复杂的多目标问题时无法权衡收敛性和多样性。提出的协同混合策略可以有效地保证算法的收敛性和多样性。采用多种群策略和动态聚类策略来提高收敛性和多样性。同时,采用生活策略和彩票概率选择策略进一步保证了人口的多样性。使用一系列测试功能来验证CHSPSO的有效性。将该算法的性能与其他进化算法进行了比较。结果表明,CHSPSO可以较好地解决复杂的多目标问题。

更新日期:2020-01-04
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