论文标题
在基于分解的进化算法中处理多模式多目标优化的框架
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms
论文作者
论文摘要
多模式多目标优化是在尽可能多的（几乎）等效的帕累托最佳解决方案定位（几乎）。尽管基于分解的进化算法在多目标优化方面具有良好的性能，但由于缺乏维持解决方案空间多样性的机制，它们对于多模式多目标优化的性能很差。为了解决这个问题，本文提出了一个框架，以提高基于分解的进化算法的性能，以进行多模式多模式多目标优化。我们的框架基于三个操作：分配，删除和加法操作。一个或多个人可以分配给同一子问题以处理多个同等的解决方案。在每次迭代中，一个孩子都会根据其目标向量（即其在目标空间中的位置）分配给子问题。将孩子与分配给同一子问题的解决方案空间中的邻居进行了比较。通过我们的框架进行了改进版本的六种基于分解的进化算法的性能，以涉及各种测试问题，涉及目标数量，决策变量和同等的帕累托最佳解决方案集。结果表明，改进的版本的性能明显优于原始算法。
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multi-objective optimization, they are likely to perform poorly for multi-modal multi-objective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this paper proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multi-modal multi-objective optimization. Our framework is based on three operations: assignment, deletion, and addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms.
