Multi-objective optimization problem based on non-dominated sorting genetic algorithm

LiHao Zhang

doi:10.1117/12.2672196

28 March 2023 Multi-objective optimization problem based on non-dominated sorting genetic algorithm

LiHao Zhang

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973V (2023) https://doi.org/10.1117/12.2672196
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Multi-objective optimization problem is a very common problem in mathematical modeling and daily research [1]. It belongs to the planning problem, and its ultimate goal is to find the optimal solution under certain constraints [2]. In order to solve the multi-objective programming problem, we usually use the multi-objective programming function in MATLAB, or take the traditional intelligent algorithm to solve, but the traditional algorithm is easy to fall into local optimal solution, and the error is large, which cannot meet the actual needs [3]. Therefore, this paper proposes an improved genetic algorithm, that is, non-dominated sorting genetic algorithm (NSGA-II) to solve multi-objective optimization problems, in order to better meet the research and practical needs [4]. In order to verify its superiority, this paper uses two examples, by comparing the results of the traditional algorithm and the improved genetic algorithm, using the comprehensive evaluation method to analyze, and finally draws the conclusion that the improved genetic algorithm is superior to the traditional algorithm.

Citation Download Citation

LiHao Zhang "Multi-objective optimization problem based on non-dominated sorting genetic algorithm", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973V (28 March 2023); https://doi.org/10.1117/12.2672196

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