Greedy adaptation of control parameters in differential evolution for global optimization problems

Differential evolution (DE) is a very attractive evolutionary and meta-heuristic technique to solve many optimization problems in various real-world scenarios. However, the proper setting of control parameters of DE is highly dependent on the problem to solve as well as on the different stages of the search process. This paper proposes a new greedy adaptation method for dynamic adjustment of mutation factor and crossover rate in DE. The proposed method is based on the idea of greedy search to find better parameter assignment in the neighborhood of a current candidate. Our work emphasizes reliable evaluation of candidates via applying a candidate with a number of times in the search process. As our purpose is not merely to increase the success rate (the survival of more trial solutions) but also to accelerate the speed of fitness improvement, we suggest a new metric termed as progress rate to access the quality of candidates in support of the greedy search. This greedy parameter adaptation method has been incorporated into basic DE, leading to a new DE algorithm called Greedy Adaptive Differential Evolution (GADE). GADE was tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtains the best ranking among the counterparts in terms of the summation of relative errors across the benchmark functions.