State transition algorithm for constrained optimization problems

In this study, a population-based continuous state transition algorithm (STA) is investigated into continuous constrained optimization problems. After an analysis of the advantages and disadvantages of two well-known constraint-handling techniques, penalty function method and feasibility preference method, a two-stage strategy is proposed for constrained STA, in which, the feasibility preference method is adopted in the early stage of an iteration process whilst it is changed to the penalty function method in the later stage. Several benchmark tests are given to evaluate the performance of the proposed method, and the experimental results show that the constrained STA with a two-stage strategy outperforms other single strategy in terms of both global search ability and solution precision.