  State transition algorithm (STA) has been emerging as a novel metaheuristic
method for global optimization in recent 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 the STA is taken in consideration. Firstly, a statistical study
with four benchmark two-dimensional 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 parameters
strategy is proposed to accelerate its search process. The proposed STA is
successfully applied to twelve benchmarks with 20, 30 and 50 dimensional space.
Comparison with other metaheuristics has also demonstrated the effectiveness of
the proposed method.
