Parameter control and hybridization techniques in differential evolution: a survey

Improving the performance of optimization algorithms is a trend with a continuous growth, powerful and stable algorithms being always in demand, especially nowadays when in the majority of cases, the computational power is not an issue. In this context, differential evolution (DE) is optimized by employing different approaches belonging to different research directions. The focus of the current review is on two main directions: (a) the replacement of manual control parameter setting with adaptive and self-adaptive methods; and (b) hybridization with other algorithms. The control parameters have a big influence on the algorithms performance, their correct setting being a crucial aspect when striving to obtain optimal solutions. Since their values are problem dependent, setting them is not an easy task. The trial and error method initially used is time and resource consuming, and in the same time, does not guarantee optimal results. Therefore, new approaches were proposed, the automatic control being one of the best solution developed by researchers. Concerning hybridization, the scope was to combine two or more algorithms in order to eliminate or to reduce the drawbacks of each individual algorithm. In this manner, different combinations at different levels were proposed. This work presents the main approaches mixing DE with global algorithms, DE with local algorithms and DE with global and local algorithms. In addition, a special attention was given to the situations in which DE is employed as a local search procedure or DE principles are included in other global search methods.