1. A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive
- Author
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Fangqing Gu, Hai-Lin Liu, Chaoda Peng, and Jiechang Wen
- Subjects
Scheme (programming language) ,0209 industrial biotechnology ,Mathematical optimization ,Optimization problem ,Evolutionary algorithm ,Pareto principle ,Boundary (topology) ,02 engineering and technology ,Genetic operator ,Constraint (information theory) ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Decomposition (computer science) ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,computer ,Algorithm ,Software ,computer.programming_language ,Mathematics - Abstract
In this paper, we propose a decomposition-based evolutionary algorithm with boundary search and archive for constrained multi-objective optimization problems (CMOPs), named CM2M. It decomposes a CMOP into a number of optimization subproblems and optimizes them simultaneously. Moreover, a novel constraint handling scheme based on the boundary search and archive is proposed. Each subproblem has one archive, including a subpopulation and a temporary register. Those individuals with better objective values and lower constraint violations are recorded in the subpopulation, while the temporary register consists of those individuals ever found before. To improve the efficiency of the algorithm, the boundary search method is designed. This method makes the feasible individuals with a higher probability to perform genetic operator with the infeasible individuals. Especially, when the constraints are active at the Pareto solutions, it can play its leading role. Compared with two algorithms, i.e. CMOEA/D-DE-CDP and Gary’s algorithm, on 18 CMOPs, the results show the effectiveness of the proposed constraint handling scheme.
- Published
- 2015
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