An Improved Genetic Algorithm for Constrained Optimization Problems

作     者：Wang, Fulin Xu, Gang Wang, Mo 

作者机构：Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2023年第11卷

页      面：10032-10044页

核心收录：

基　　金：Natural Science Foundation of Heilongjiang Province [LH2020C004] 

主　　题：Optimization Statistics Social factors Genetic algorithms Linear programming Evolutionary computation Search problems Genetic algorithm constrained optimization problem two-direction crossover grouped mutation 

摘      要：The mathematical form of many optimization problems in engineering is constrained optimization problems. In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation is proposed to solve constrained optimization problems. In addition to making full use of the direction information of the parent individual, the two-direction crossover adds an additional search direction and finally searches in the better direction of the two directions, which improves the search efficiency. The grouped mutation divides the population into two groups and uses mutation operators with different properties for each group to give full play to the characteristics of these mutation operators and improve the search efficiency. In experiments on the IEEE CEC 2017 competition on constrained real-parameter optimization and ten real-world constrained optimization problems, the proposed algorithm outperforms other state-of-the-art algorithms. Finally, the proposed algorithm is used to optimize a single-stage cylindrical gear reducer.