论文标题
在双目标多模式优化中获得平滑导航的近似集
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization
论文作者
论文摘要
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches, reducing the insight for decision makers.为了解决此问题，提出了一个新的MMOEA：多模式Bézier进化算法（MM-BEZEA），该算法产生近似集，涵盖单个利基市场并表现出固有的决策空间平滑度，因为它们被Bézier曲线参数化。 MM-Bezea结合了最近引入的bezea和Mo-Hillvallea背后的概念，以找到所有本地最佳近似集。当用线性帕累托套件上的MMOP上的MMOEAS MO_RING_PSO_SCD和MO-HILLVALLEA进行基准测试时，发现MM-BEZEA在最佳Hypervolume方面表现最好。
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches, reducing the insight for decision makers. To tackle this issue, a new MMOEAs is proposed: the Multi-Modal Bézier Evolutionary Algorithm (MM-BezEA), which produces approximation sets that cover individual niches and exhibit inherent decision-space smoothness as they are parameterized by Bézier curves. MM-BezEA combines the concepts behind the recently introduced BezEA and MO-HillVallEA to find all locally optimal approximation sets. When benchmarked against the MMOEAs MO_Ring_PSO_SCD and MO-HillVallEA on MMOPs with linear Pareto sets, MM-BezEA was found to perform best in terms of best hypervolume.
