An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization
An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization
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DOI：
10.1016/j.ins.2016.01.068
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Xianpeng Wang;Lixin Tang
中科院分区：
文献类型：
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作者：
Xianpeng Wang;Lixin Tang
对于进化算法来说，进化过程中的搜索数据引起了人们的广泛关注，人们提出了多种数据挖掘方法来导出这些数据背后的有用信息，从而指导进化搜索。然而，这些方法主要集中于单目标优化问题。本文针对多目标优化问题，开发了一种基于搜索数据分析的自适应差分进化算法。该算法首先通过聚类和统计方法从进化过程中的搜索数据中导出有用信息，然后利用导出的信息指导新种群的生成和局部搜索。此外，所提出的差分进化算法采用多个子种群，每个子种群根据借用遗传算法的指定交叉算子进行进化，以生成扰动向量。在进化过程中，每个子种群的大小会根据搜索结果中的信息进行自适应调整。本地搜索由两个阶段组成，分别侧重于探索和利用。基准多目标问题的计算结果表明，该策略的改进是积极的，并且所提出的差分进化算法比文献中以前的一些多目标进化算法具有竞争力或优于它们。
For evolutionary algorithms, the search data during evolution has attracted considerable attention and many kinds of data mining methods have been proposed to derive useful information behind these data so as to guide the evolution search. However, these methods mainly centered on the single objective optimization problems. In this paper, an adaptive differential evolution algorithm based on analysis of search data is developed for the multi-objective optimization problems. In this algorithm, the useful information is firstly derived from the search data during the evolution process by clustering and statistical methods, and then the derived information is used to guide the generation of new population and the local search. In addition, the proposed differential evolution algorithm adopts multiple subpopulations, each of which evolves according to the assigned crossover operator borrowed from genetic algorithms to generate perturbed vectors. During the evolution process, the size of each subpopulation is adaptively adjusted based on the information derived from its search results. The local search consists of two phases that focus on exploration and exploitation, respectively. Computational results on benchmark multi-objective problems show that the improvements of the strategies are positive and that the proposed differential evolution algorithm is competitive or superior to some previous multi-objective evolutionary algorithms in the literature.