论文标题
关于MOEA/D的人口规模和子问题选择的综合影响
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
论文作者
论文摘要
本文旨在理解和改善基于分解的多目标进化算法的工作原理。我们回顾了良好的MOEA/D框架的设计，以支持不同策略的平稳整合以进行子问题选择，同时强调人口规模的作用以及每一代人创造的后代数量的作用。通过对各种多种目标组合NK景观进行全面的经验分析，我们为这些参数对基础搜索过程的任何时间性能的综合效果提供了新的见解。特别是，我们表明，即使是一个简单的随机策略，在随机的表现上选择子问题的现有策略都优于现有的复杂策略。我们还研究了此类策略对目标问题的坚固性和客观空间维度的敏感性。
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the smooth integration of different strategies for sub-problem selection, while emphasizing the role of the population size and of the number of offspring created at each generation. By conducting a comprehensive empirical analysis on a wide range of multi-and many-objective combinatorial NK landscapes, we provide new insights into the combined effect of those parameters on the anytime performance of the underlying search process. In particular, we show that even a simple random strategy selecting sub-problems at random outperforms existing sophisticated strategies. We also study the sensitivity of such strategies with respect to the ruggedness and the objective space dimension of the target problem.
