Rank-Based Differential Evolution with Multiple Mutation Strategies for Large Scale Global Optimization

Differential Evolution (DE) is one of the most powerful global numerical optimization algorithms in the field of evolutionary algorithm. However, the performance of DE is affected by control parameters and mutation strategies. In addition, the choice of the control parameters and mutation strategies is strongly dependent on the characteristics of optimization problems. As a result, studies focused on controlling the parameters and mutation strategies is currently an active area of research. One of them, DE with landscape modality detection (LMDEa) which detects the landscape modality using the current search points, showed excellent performance for large scale optimization problem. In our research, we improve LMDEa by introducing the concept of Rank-based Differential Evolution (RDE). The proposed method utilizes ranking information of search points in order to assign a suitable scaling factor (F) and a crossover rate (C R) for each individual. Furthermore multiple mutation strategies are employed; in addition, they are also assigned by the ranking information for realizing a well-balanced exploration and exploitation ability. Through experimentation, using the set of benchmark functions, we show the effectiveness of the proposed method.