A New Dynamic Multi-objective Optimization Evolutionary Algorithm

Abstract

Dynamic multi-objective optimization problems are very common in real-world applications. The researches on applying evolutionary algorithm into such problems are attracting more and more researchers. In this paper, a new dynamic multi-objective optimization evolutionary algorithm which utilizes hyper-mutation operator to deal with dynamics and geometrical Pareto selection to deal with multi-objective is introduced. The experimental results show that the performance is satisfactory.