A novel hybrid algorithm for optimization in multimodal Dynamic environments
Alireza Sepas‐Moghaddam, Alireza Arabshahi, Danial Yazdani, Mohammad Mahdi Dehshibi · 2012
Objective function or the constraints and consequently the optimal value of the problem can be changed during time in Dynamic optimization problems. There are several challenges in dynamic environments, so that algorithms designed for optimization in these environments would utilize several mechanisms in order to conquer the challenges. In this paper, a novel hybrid algorithm for optimization in dynamic environments, called HPSOLS, is proposed based on particle swarm optimization and local search approaches. In this approach, it aims to increase the ability of local search around optimum with focusing on best found peak in each environment. The results of the proposed approach are evaluated using moving peak benchmark, which is currently the most well-known benchmark for evaluating dynamic environments, and are compared with results of several state-of-the-art algorithms in this domain. Experimental results show that the efficiency of the proposed method outperforms that of other algorithms in this domain.