An Adaptive Clustering and Re-clustering Based Crowding Differential Evolution for Continuous Multi-modal Optimization

In real-life a particular system, operating under a given set of conditions, may need to switch other set of conditions due to change in physical condition or failure in its existing state. Niching techniques facilitates in such situations by tracking multiple optima (solutions). When integrated with Evolutionary Algorithms (EAs), they seek parallel convergence of population members to find multiple solutions to a problem (landscape) without loss in optimality. In this paper an effective new grouping strategy namely adaptive clustering and re-clustering (ACaR) is proposed based on Fuzzy c-means clustering technique and is integrated with a hybrid of crowding niching technique and a real-parameter optimizing algorithm called Differential Evolution (DE). The performance of the proposed ACaR-CDE algorithm has been evaluated on different niching benchmark problems with diverse characteristics ranging from simple objectives to complex composite problems and compared with other published state-of-the-art niching algorithms. From experimental observation, we observe that the proposed strategy is apt in restraining solutions within its local environment, typically applicable to niching environment.