TY - GEN
T1 - An Improved Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization
AU - Wen, Chengxin
AU - Li, Lihua
AU - Ma, Hongbin
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - The hypervolume indicator is commonly utilized in indicator-based evolutionary algorithms due to its strict adherence to the Pareto domination relationship. However, its high computational complexity in high-dimensional objective spaces limits its widespread adoption and application. In this paper, we propose a fast and efficient method for approximating the overall hypervolume to overcome this challenge. We then integrate this method into the basic evolutionary computation framework, forming an algorithm for solving many-objective optimization problems. To evaluate its performance, we compared our proposed algorithm with six state-of-the-art algorithms on WFG and DTLZ test problems with 3, 5, 10, and 15 objectives. The results demonstrate that our proposed method is highly competitive in most cases.
AB - The hypervolume indicator is commonly utilized in indicator-based evolutionary algorithms due to its strict adherence to the Pareto domination relationship. However, its high computational complexity in high-dimensional objective spaces limits its widespread adoption and application. In this paper, we propose a fast and efficient method for approximating the overall hypervolume to overcome this challenge. We then integrate this method into the basic evolutionary computation framework, forming an algorithm for solving many-objective optimization problems. To evaluate its performance, we compared our proposed algorithm with six state-of-the-art algorithms on WFG and DTLZ test problems with 3, 5, 10, and 15 objectives. The results demonstrate that our proposed method is highly competitive in most cases.
KW - Evolutionary algorithms
KW - Many-objective optimization
KW - Overall hypervolume approximation
UR - http://www.scopus.com/inward/record.url?scp=85176950831&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7590-7_23
DO - 10.1007/978-981-99-7590-7_23
M3 - Conference contribution
AN - SCOPUS:85176950831
SN - 9789819975891
T3 - Communications in Computer and Information Science
SP - 283
EP - 297
BT - Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings
A2 - Xin, Bin
A2 - Kubota, Naoyuki
A2 - Chen, Kewei
A2 - Dong, Fangyan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023
Y2 - 3 November 2023 through 5 November 2023
ER -