A constrained multi-objective evolutionary algorithm based on weak cooperation framework and multi-chaotic operators

In this paper, we propose a multi-objective evolutionary algorithm (LCMO) based on weak co evolution framework and multi chaotic operators. In the framework of two population weak cooperation, we use hybrid chaotic operators to help candidate solution populations improve their distribution uniformit...

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Vydáno v:2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT) s. 102 - 106
Hlavní autoři: Zhang, Libiao, Yu, Ru, Liu, Jiayu, Xu, Yao, Jiang, Hui, Xu, Peng, Lin, Peng, Luo, Yu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2022
Témata:
Artificial intelligence
evolutionary algorithms
Evolutionary computation
Information technology
Maintenance engineering
multi-objective optimization
Pareto domination
Robots
Sociology
Statistics
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Shrnutí:In this paper, we propose a multi-objective evolutionary algorithm (LCMO) based on weak co evolution framework and multi chaotic operators. In the framework of two population weak cooperation, we use hybrid chaotic operators to help candidate solution populations improve their distribution uniformity, and use the second selection criteria to refine Pareto level. In this paper, The IGD and true Pareto Front performance indicators obtained from the experiments verify the good performance of the algorithm.
DOI:10.1109/GCRAIT55928.2022.00030