Dynamical multi-objective optimization evolutionary algorithm

Abstract

A dynamical multi-objective evolutionary algorithm (DMOEA) is proposed. It is the first study of the dynamical evolutionary algorithm (DEA) in multi-objective optimization process. All individuals called as particles in a population evolve through a new selection mechanism. We combine the selection mechanism in DEA and the elitists strategy in existing evolutionary multi-objective optimization algorithms in DMOEA. The performance of DMOEA has been analyzed in comparison with SPEA2. The experimental results show that DMOEA clearly outperforms SPEA2 for the whole benchmark set. Moreover, a better convergence is sometimes observed in DMOEA for some functions of the benchmark set. The numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.