A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm

The state transition algorithm (STA) has been emerging as a novel metaheuristic method for global optimization over the past few years. In our previous study, the parameter of transformation operator in continuous STA is kept constant or decreasing itself in a periodical way. In this paper, the optimal parameter selection of operators in continuous STA is taken into consideration. First, a statistical study with four benchmark 2-D functions is conducted to show how these parameters affect the search ability of the STA. Based on the experience gained from the statistical study, then, a new continuous STA with optimal parameter strategy is proposed to accelerate its search process. The proposed STA is successfully applied to 12 benchmarks with 20-D, 30-D, and 50-D space. A comparison with other metaheuristics has also demonstrated the effectiveness of the proposed method.