Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization
Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization
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DOI：
10.1109/tevc.2015.2433672
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发表时间：
2016-04-01
影响因子：
14.3
通讯作者：
Abbass, Hussein A.
中科院分区：
文献类型：
--
作者：
Qiu, Xin;Xu, Jian-Xin;Abbass, Hussein A.
将差分进化（DE）扩展到多目标优化（MO）时，收敛性能和参数敏感性是两个容易被忽视的问题。为了填补这一研究空白，我们开发了两种新颖的变异算子和一种新的参数适应机制。通过整合所提出的策略获得多目标 DE 变体。本文的主要创新点是从基于目标的角度同时使用跨代个体。通过混合跨代变异操作实现了良好的收敛-多样性权衡和令人满意的探索-利用平衡。此外，跨代适应机制使个体不仅能够在优化阶段而且在目标空间方面自适应其相关参数。实证结果表明，在处理 MO 问题方面，所提出的算法比几种最先进的进化算法具有统计优越性。
Convergence performance and parametric sensitivity are two issues that tend to be neglected when extending differential evolution (DE) to multiobjective optimization (MO). To fill this research gap, we develop two novel mutation operators and a new parameter adaptation mechanism. A multiobjective DE variant is obtained through integration of the proposed strategies. The main innovation of this paper is the simultaneous use of individuals across generations from an objective-based perspective. Good convergence-diversity trade-off and satisfactory exploration-exploitation balance are achieved via the hybrid cross-generation mutation operation. Furthermore, the cross-generation adaptation mechanism enables the individuals to self-adapt their associated parameters not only optimization-stage-wise but also objective-space-wise. Empirical results indicate the statistical superiority of the proposed algorithm over several state-of-the-art evolutionary algorithms in handling MO problems.