Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multiobjective Algorithms
Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multiobjective Algorithms
复制标题
研究进化多目标算法中收敛性与多样性之间不平衡的影响
DOI：
10.1109/tevc.2016.2606577
复制
发表时间：
2017-06-01
影响因子：
14.3
通讯作者：
Goodman, Erik D.
中科院分区：
文献类型：
--
作者：
Liu, Hai-Lin;Chen, Lei;Goodman, Erik D.
通过进化多目标（EMO）算法解决多目标优化问题（MOP）涉及两个主要任务：1）使种群收敛到接近帕累托最优前沿；2）保持足够的种群多样性。然而，大多数最先进的 EMO 算法都是基于“收敛第一、多样性第二”的原则设计的。据观察，尽管这些 EMO 算法已经成功地优化了许多现实世界的 MOP，但它们未能解决某些在多样性保留和实现收敛之间严重不平衡的问题。本文通过明确定义属性并指出现有 EMO 算法难以解决它们的原因来描述不平衡 MOP。然后我们提出 14 个有约束和无约束的不平衡问题。使用四种现有 EMO 算法的计算结果：精英非支配排序遗传算法（NSGA-II）、基于分解的多目标进化算法（MOEA/D）、强度 Pareto 进化算法 2（SPEA2）和 S 度量选择 EMO 算法（SMS-EMOA）以及提出的广义向量评估遗传算法 然后提出算法。可以看出，这些 EMO 算法无法解决这些不平衡问题，但通过多目标到多目标 (M2M)（一种将总体分解为多个相互作用的子总体的方法）增强后，它们能够解决这些问题。这些结果以及 EMO 方法与 M2M 方法的成功应用（甚至在标准的所谓平衡问题上）表明了使用 M2M 方法的有用性。
There are two main tasks involved in addressing a multiobjective optimization problem (MOP) by evolutionary multiobjective (EMO) algorithms: 1) make the population converge close to the Pareto-optimal front and 2) maintain adequate population diversity. However, most state-of-the-art EMO algorithms are designed based on the “convergence first and diversity second” principle. It has been observed that although these EMO algorithms have been successful in optimizing many real-world MOPs, they fail to solve certain problems that feature a severe imbalance between diversity preservation and achieving convergence. This paper characterizes an imbalanced MOP by clearly defining properties and indicating the reasons for the existing EMO algorithms’ difficulties in solving them. We then present 14 imbalanced problems, with and without constraints. Computational results using four existing EMO algorithms—elitist non-dominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), strength Pareto evolutionary algorithm 2 (SPEA2), and S metric selection EMO algorithm (SMS-EMOA) and a proposed generalized vector-evaluated genetic algorithm are then presented. It is seen that these EMO algorithms cannot solve these imbalanced problems, but they are able to solve the problems when augmented by multiobjective to multiobjective (M2M), an approach that decomposes the population into several interacting subpopulations. These results and the successful application of the EMO methods with the M2M approach even on standard so-called balanced problems indicate the usefulness of using the M2M approach.