PUB - Publikationen an der Universität Bielefeld

Using evolutionary algorithms (EAs) to handle dynamic multiobjective optimization problems (DMOPs) is a challenging topic. In this paper, a prediction strategy based on reference points is proposed to improve the performance of EAs in solving DMOPs. The reference point based strategy is to partition the population into several subpopulations according to the reference points. When a change is detected, a sequence of the subpopulation centers in the previous environments belonging to the same reference point are used to estimate the center of in new environment. Based on the predicted centers, the EA will generate an initial population for the new environment using a combination of a Uniform distribution for enhancing population diversity and a Gaussian distributions for accelerating convergence. Experiments on ten test instances have been carried out to evaluate the performance of proposed strategy and the results show that reference point based prediction strategy exhibits superior performance in dealing with DMOPs with nonlinear correlations between decision variables and severe environmental changes.