A Micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy

A Micro-cloning local exploitation and an adaptive change reaction strategy are developed to address complex dynamic multiobjective optimization problems. The former is applied to exploit the uncrowded regions in decision space through cloning a few nondominated individuals, enhancing the exploitation and exploration capability of the proposed algorithm, while the latter accelerates the ability of tracking the changing Pareto front using a specific mechanism. The adaptive change reaction scheme is used to reinitialize the population in terms of a change rate checked and ensure that the proposed algorithm can quickly track each moving Pareto front over time. In addition, a lower computational cost update approach of nondominated set is proposed to obtain a well-distributed and well-spread set of nondominated solutions. We systematically compare the proposed algorithm with several state-of-art algorithms on fourteen dynamic multiobjective test instances with different challenging difficulties, and meanwhile, the performance of these algorithms is compared with each other in terms of several performance measure indicators and nonparametric statistical approaches. Experimental results indicate that the proposed algorithm can obtain a promising tracking ability and well-distributed Pareto front on most of the test instances in each environment.