论文标题
基于分解的多目标进化算法设计在两个算法框架下
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
论文作者
论文摘要
在进化计算社区中，有效而有效的多目标优化（EMO）算法的发展一直是一个积极的研究主题。多年来，已经提出了许多EMO算法。现有的EMO算法主要基于最终人口框架开发。在最终的人口框架中，表EO算法的最终人群将介绍给决策者。因此，要求Emo算法产生的最终人群是一个很好的解决方案集。最近，建议使用解决方案选择框架来设计表情算法。该框架具有无限的外部存档，可以存储所有检查的解决方案。从档案中选择了预先指定的解决方案作为最终解决方案提交给决策者。当使用解决方案选择框架时，可以更灵活地设计Emo算法，因为最终人群不一定是一个好的解决方案集。在本文中，我们在这两个框架下研究了MOEA/D的设计。我们使用一个基于遗传算法的超高热效方法来找到MOEA/D在每个框架中的最佳配置。 DTLZ和WFG测试套件及其负版本用于我们的实验。实验结果表明，当使用解决方案选择框架时，可以获得更灵活，健壮和高性能MOEA/D算法的可能性。
The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EMO algorithms are mainly developed based on the final population framework. In the final population framework, the final population of an EMO algorithm is presented to the decision maker. Thus, it is required that the final population produced by an EMO algorithm is a good solution set. Recently, the use of solution selection framework was suggested for the design of EMO algorithms. This framework has an unbounded external archive to store all the examined solutions. A pre-specified number of solutions are selected from the archive as the final solutions presented to the decision maker. When the solution selection framework is used, EMO algorithms can be designed in a more flexible manner since the final population is not necessarily to be a good solution set. In this paper, we examine the design of MOEA/D under these two frameworks. We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The DTLZ and WFG test suites and their minus versions are used in our experiments. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.
