An Adaptive Differential Evolution Algorithm Utilizing Failure Information and Success Information

Differential Evolution (DE) has been successfully applied to a variety of optimization problems. The performance of DE is affected by two algorithm parameters of the scaling factor and the crossover rate. Much research has been done in order to adaptively control the parameters. One of the most successful studies on adaptive parameter control is JADE, where the two parameter values are generated according to two probability distributions which are tuned by the parameter values in success cases. In this paper, we propose a new method that utilizes not only success information but also failure information. A measure, which indicates how much the pair of generated parameter values will lead to failure, is defined. The pair is rejected probabilistically according to the measure and a new pair is regenerated until a good pair is generated. The effect of the proposed method is shown by solving various benchmark problems.