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

• URL: http://arxiv.org/abs/2203.09214v3
• Date: Mon, 4 Jul 2022 13:29:12 GMT
• Title: Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization
• Abstract summary: MM-BezEA produces approximation sets that cover individual niches and exhibit inherent decision-space smoothness. MM-BezEA was found to perform best in terms of best hypervolume.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 B\'ezier Evolutionary Algorithm (MM-BezEA), which produces approximation sets that cover individual niches and exhibit inherent decision-space smoothness as they are parameterized by B\'ezier 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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