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

Abstract

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.