A Two-Population Algorithm for Large-Scale Multi-objective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection

Multi-objective optimization problems (MOPs) containing a large number of decision variables, which are also known as large-scale multi-objective optimization problems (LSMOPs), pose great challenges to most existing evolutionary algorithms. This is mainly because that a high dimensional decision sp...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2023, p.1-1
Main Authors: Li, Bingdong, Zhang, Yan, Yang, Peng, Yao, Xin, Zhou, Aimin
Format: Article
Language:English
Subjects:
Classification algorithms
Convergence
Evolutionary Algorithm
Evolutionary computation
Evolutionary Multi-objective Optimization
Fitness-Aware Operator
Large-Scale Multi-objective Optimization
Optimization
Search problems
Sociology
Statistics
Two-Archive Algorithm
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Summary:Multi-objective optimization problems (MOPs) containing a large number of decision variables, which are also known as large-scale multi-objective optimization problems (LSMOPs), pose great challenges to most existing evolutionary algorithms. This is mainly because that a high dimensional decision space degrades the effectiveness of search operators notably, and balancing convergence and diversity becomes a challenging task. In this paper, we propose a two-population based algorithm for large-scale multi-objective optimization named LSTPA. In the proposed algorithm, solutions are classified in to two subpopulations: a Convergence subPopulation (CP) and a Diversity subPopulation (DP), aiming at convergence and diversity respectively. In order to improve convergence speed, a fitness-aware variation operator (FAVO) is applied to drive DP solutions towards CP. Besides, an adaptive penalty based boundary intersection (APBI) strategy is adopted for environmental selection in order to balance convergence and diversity temporally during different stages of evolution process. Experimental results on benchmark test problems with 100-2000 decision variables demonstrate that the proposed algorithm can achieve the best overall performance compared with several state-of-the-art large-scale multi-objective evolutionary algorithms.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3296488