Obtaining Smoothly Navigable Approximation Sets in Bi-objective Multi-modal Optimization

Conference paper (2022)

Authors

R.J. Scholman Centrum Wiskunde & Informatica (CWI), Algorithmics - EEMCS
Anton Bouter Centrum Wiskunde & Informatica (CWI)
Leah R.M. Dickhoff Leiden University Medical Center
T. Alderliesten Leiden University Medical Center
P.A.N. Bosman Centrum Wiskunde & Informatica (CWI), Algorithmics - EEMCS
Research Group
Algorithmics (EEMCS) (TU Delft)
Copyright: © 2022 R.J. Scholman, Anton Bouter, Leah R.M. Dickhoff, T. Alderliesten, P.A.N. Bosman
DOI: https://doi.org/10.1007/978-3-031-14721-0_18
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

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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