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arXiv · 2203.09214

Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization

Renzo J. Scholman·Anton Bouter·Leah R. M. Dickhoff·Tanja Alderliesten·Peter A. N. Bosman
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Abstract

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.

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Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff, Tanja Alderliesten, Peter A. N. Bosman. 2022-07-04. Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization. https://arxiv.org/abs/2203.09214

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