An evolutionary algorithm for dynamic multi-objective optimization

In this paper, the dynamic multi-objective optimization problem (DMOP) is first approximated by a series of static multi-objective optimization problems (SMOPs) by dividing the time period into several equal subperiods. In each subperiod, the dynamic multi-objective optimization problem is seen as a static multi-objective optimization problem by taking the time parameter fixed. Then, to decrease the amount of computation and efficiently solve the static problems, each static multi-objective optimization problem is transformed into a two-objective optimization problem based on two re-defined objectives. Finally, a new crossover operator and mutation operator adapting to the environment changing are designed. Based on these techniques, a new evolutionary algorithm is proposed. The simulation results indicate that the proposed algorithm can effectively track the varying Pareto fronts with time.