A Multiobjective Artificial Bee Colony Algorithm based on Decomposition

Guang Peng, Zhihao Shang, Katinka Wolter

2019

Abstract

This paper presents a multiobjective artificial bee colony (ABC) algorithm using the decomposition approach for improving the performance of MOEA/D (multiobjective evolutionary algorithm based on decomposition). Using a novel reproduction operator inspired by ABC, we propose MOEA/D-ABC, a new version of MOEA/D. Then, a modified Tchebycheff approach is adopted to achieve higher diversity of the solutions. Further, an adaptive normalization operator can be incorporated into MOEA/D-ABC to solve the differently scaled problems. The proposed MOEA/D-ABC is compared to several state-of-the-art algorithms on two well-known test suites. The experimental results show that MOEA/D-ABC exhibits better convergence and diversity than other MOEA/D algorithms on most instances.

Paper Citation

in Harvard Style

Peng G., Shang Z. and Wolter K. (2019). A Multiobjective Artificial Bee Colony Algorithm based on Decomposition. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA; ISBN 978-989-758-384-1, SciTePress, pages 188-195. DOI: 10.5220/0008167801880195

in Bibtex Style

@conference{ecta19,
author={Guang Peng and Zhihao Shang and Katinka Wolter},
title={A Multiobjective Artificial Bee Colony Algorithm based on Decomposition},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA},
year={2019},
pages={188-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008167801880195},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA
TI - A Multiobjective Artificial Bee Colony Algorithm based on Decomposition
SN - 978-989-758-384-1
AU - Peng G.
AU - Shang Z.
AU - Wolter K.
PY - 2019
SP - 188
EP - 195
DO - 10.5220/0008167801880195
PB - SciTePress