Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm

Yu Takei, Hernán Aguirre, Kiyoshi Tanaka

2023

Abstract

AεSεH is one of the evolutionary algorithms used for many-objective optimization. It uses ε-dominance during survival selection to sample from a large set of non-dominated solutions to reduce it to the required population size. The sampling mechanism works to suggest a subset of well distributed solutions, which boost the performance of the algorithm in many-objective problems compared to Pareto dominance based multi-objective algorithms. However, the sampling mechanism does not select exactly the target number of individuals given by the population size and includes a random selection component when the size of the sample needs to be adjusted. In this work, we propose a more elaborated method also based on ε-dominance to reduce randomness and obtain a better distributed sample in objective-space to further improve the performance of the algorithm. We use binary MNK-landscapes to study the proposed method and show that it significantly increases the performance of the algorithm on non-linear problems as we increase the dimensionality of the objective space and decision space.

Paper Citation

in Harvard Style

Takei Y., Aguirre H. and Tanaka K. (2023). Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-674-3, SciTePress, pages 86-95. DOI: 10.5220/0012181300003595

in Bibtex Style

@conference{ecta23,
author={Yu Takei and Hernán Aguirre and Kiyoshi Tanaka},
title={Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2023},
pages={86-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012181300003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm
SN - 978-989-758-674-3
AU - Takei Y.
AU - Aguirre H.
AU - Tanaka K.
PY - 2023
SP - 86
EP - 95
DO - 10.5220/0012181300003595
PB - SciTePress