Estimation of distribution algorithms based on increment clustering for multiple optima in dynamic environments

Abstract

Aiming to locate and track multiple optima in dynamic multimodal environments, an estimation of distribution algorithms based on increment clustering is proposed. The main idea of the proposed algorithm is to construct several probability models based on an increment clustering which improved performance for locating multiple local optima and contributed to find the global optimal solution quickly for dynamic multimodal problems. Meanwhile, a policy of diffusion search is introduced to enhance the diversity of the population in a guided fashion when the environment is changed. The policy uses both the current population information and the part history information of the optimal solutions available. Experimental studies on the moving peaks benchmark are carried out to evaluate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms from the literature. The results show that the proposed algorithm is effective for the function with moving optimum and can adapt to the dynamic environments rapidly.