An adaptive state transition algorithm with local enhancement for global optimization

Abstract

State transition algorithm (STA) is an efficient and powerful metaheuristic method for solving global optimization problems, and it has been successfully applied in many engineering fields in the past few years. However, the basic STA has weak local search capability and shows slow convergence rate and low convergence accuracy in the later search stage. In view of the above shortcomings, an adaptive state transition algorithm (ASTA) with local enhancement is proposed in this paper. Firstly, the order of using state transformation operators and the optimal parameters of the operators are considered in each iteration of ASTA, and a statistical method is employed to adaptively select the optimal transformation operator and the parameter values of the optimal operator to speed up the search process. Then, an adaptive call strategy is adopted to determine its convergence to the neighborhood of the optimal solution and to decide whether to perform the quasi-Newton operator for local enhancement. Finally, the degree to which the current solution is close to the optimal solution is judged by the information of historical solutions, and an analytical solution is quickly obtained by calling the quadratic interpolation operator. The effectiveness of the proposed ASTA is checked, through a comparison with other metaheuristic methods, on 15 benchmark functions and several real-world optimization problems. Experimental results show that ASTA has a stronger search capability than the basic STA, STA variants, and some state-of-the-art metaheuristic methods.