Memetic algorithm based on self-adaptive differential evolution and improved simplex crossover for large scale global optimization

Large Scale Global Optimization (LSGO) problems recently arise as an important field of research and many heuristic algorithms have been proposed for solving them. In this paper, a new memetic algorithm for LSGO is proposed, which employs the self-adaptive differential evolution with neighbourhood search (SaNSDE) as the global search operator and an improved version of simplex crossover (ISPX) as the local search operator, respectively. Among them, SaNSDE can gradually self-adapted enhance the ability of searching the whole problem space, while ISPX pays more attention to enhance local search ability and finds better results by applying the knowledge of the previous good solutions. Consequently, suitable offspring replacement strategy is adaptively generated to better balance exploration and exploitation. Experiments on 20 benchmark functions of CEC2010 verify the effectiveness and efficiency of the proposed method. The experimental results also show that the proposed method achieves competitive performance with respect to several other state-of-the-art evolutionary algorithms on LSGO.