Abstract
Various real-world multi-objective optimization problems are dynamic, requiring evolutionary algorithms to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of evolutionary algorithms in solving a dynamic multi-objective optimization problem. To more accurately predict the moving location of the Pareto set, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the severity of the environmental change. To examine the performance of the proposed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular evolutionary algorithms for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.