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Authors: Yu Takei ; Hernán Aguirre and Kiyoshi Tanaka

Affiliation: Department of Electrical and Computer Engineering, Shinshu University, Wakasato, Nagano, Japan

Keyword(s): Many-Objective Optimization, Pareto Dominance Extension, AεSεH, Improving ε-Sampling, MNK-Landscapes.

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 no n-linear problems as we increase the dimensionality of the objective space and decision space. (More)

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Paper citation in several formats:
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 - ECTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 86-95. DOI: 10.5220/0012181300003595

@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 - ECTA},
year={2023},
pages={86-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012181300003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA
TI - Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm
SN - 978-989-758-674-3
IS - 2184-3236
AU - Takei, Y.
AU - Aguirre, H.
AU - Tanaka, K.
PY - 2023
SP - 86
EP - 95
DO - 10.5220/0012181300003595
PB - SciTePress

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