Intelligent optimization algorithm of non-convex function based on genetic algorithm

作     者：Zhao, Xi Li, Ying Boonen, P. 

作者机构：Guangdong Polytech Sci & Technol Comp Engn Tech Coll Zhuhai 519090 Peoples R China Tilburg Univ Dept Econometr & Operat Res Tilburg Netherlands 

出 版 物：《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)

年 卷 期：2018年第35卷第4期

页      面：4289-4297页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Genetic algorithm non-convex function intelligence optimization global optimal solution 

摘      要：There are many local optimums for the non-convex function. The traditional algorithm is easy to fall into the local optimum and cannot obtain the optimal solution of non-convex function. To address this problem, a new intelligent optimization algorithm for non-convex function based on genetic algorithm is proposed in this paper. A proximal point sequence is obtained by using the idea of proximal point algorithm. Two simple and easily solved non-convex function subproblems are constructed by convexity technique, cutting plane method, and alternating linearization method. The basic operation process of genetic algorithm is analyzed. The combination selection operator, the initial population molding, the cross probability and the mutation probability are improved to ensure the global optimum. The processing result of the non-convex function is taken as the objective function. The mapping relationship between the fitness function and the objective function is constructed. Intelligent optimization of non-convex function is achieved by optimized genetic algorithm. Experimental results show that the proposed algorithm can obtain the global optimal solution of the non-convex function, and the optimization performance is better.