Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems
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DOI：
10.1109/tsmcb.2012.2209115
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发表时间：
2013-04-01
影响因子：
11.8
通讯作者：
Shi, Yu-Hui
中科院分区：
文献类型：
--
作者：
Zhan, Zhi-Hui;Li, Jingjing;Shi, Yu-Hui
传统的多目标进化算法（MOEA）在解决多目标优化问题（MOP）时将多个目标视为一个整体。然而，这种考虑可能会导致难以为个体分配适应度，因为不同的目标经常相互冲突。为了避免这个困难，本文在开发 MOEA 时提出了一种新颖的协同进化技术，称为多群体多目标（MPMO）。 MPMO 的新颖之处在于，它通过让每个群体仅对应一个目标，提供了一种简单直接的方法来解决 MOP。这样，就可以解决适应度分配问题，因为每个群体中个体的适应度可以通过相应的目标来分配。 MPMO是一种通用技术，每个群体都可以使用现有的优化算法。本文对每个种群采用粒子群优化（PSO），并基于MPMO技术开发了协同进化多群PSO（CMPSO）。此外，CMPSO 通过使用不同人群的外部共享档案来交换搜索信息并使用两种新颖的设计来提高性能，从而新颖且有效。一种设计是修改速度更新方程，以使用不同群体找到的搜索信息来快速逼近整个帕累托前沿（PF）。另一种设计是使用精英学习策略进行档案更新，以引入多样性以避免局部PF。 CMPSO 在具有不同特征的不同基准问题集上进行了全面测试，并与一些最先进的算法进行了比较。结果表明 CMPSO 在解决这些不同的 MOP 集方面具有优越的性能。
Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.