基于多种群差分进化的多目标优化算法

计算机科学 ›› 2012, Vol. 39 ›› Issue (8): 205-209.

基于多种群差分进化的多目标优化算法

宋通，庄毅

(南京航空航天大学计算机科学与技术学院南京210016)

出版日期:2018-11-16 发布日期:2018-11-16

A Kind of Multi-objective Optimization Algorithm Based on Differential Evolution with Multi-population Mechanism

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 针对差分进化算法(Differential Evolution Algorithm, DE)求解多目标优化问题时易陷入局部最优的问题，设计了一种双向搜索机制，它通过对相反进化方向产生的两个子代个体进行评价，来增强DE算法的局部搜索能力;设计了多种群机制，它可令各子群独立进化一定次数再执行全局进化，以完成子群间进化信息的交流，这一方面降低了算法陷入局部最优的风险，另一方面增强了Parct。解集的多样性，使Parct。前沿面的解集分布更为均匀。实验结果表明，相比于NSGA-II等同类算法，所提方法在搜索Paret。最优解时效率更高，并且Pareto最优解集的精度及分布程度比前者更好。

关键词: 差分进化，多目标优化，多种群

Abstract: In order to avoid the situation of falling into local optimum in solving the multi-objective optimization problem(MOP) with differential evolution algorithm(DE) , we designed a bidirectional search mechanism which can improve the ability of local search of the DE. We also designed a multi-population mechanism for DE, which can reduce the risk of local optimum, and make the Pareto fronts more evenly distributed. Experimental results shows that, compared with similar algorithms such as NSUA-II, the proposed method is more efficient, while the precision and distribution of Pareto optimal solution set is better than the former.

Key words: Differential evolution,Multi-o场ective optimization, Multi-population

宋通，庄毅. 基于多种群差分进化的多目标优化算法[J]. 计算机科学, 2012, 39(8): 205-209. https://doi.org/

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