A uniform approach for the comparison of opposition-based learning

Abstract

Although remarkable progress has been made in the application of opposition-based learning in recent years, the complete theoretical comparison is seldom reported. In this paper, an evaluation function of opposition strategy is defined and then a uniform evaluation approach to compute the mean minimum Euclidean distance to the optimal solution is proposed for one dimensional case. Thus different opposition strategies can be compared easily by means of the mathematical expectation of these evaluation functions. Theoretical analysis and simulation experiments can support each other, and also show the effectiveness of this method for sampling problems.