A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization

Dynamic multiobjective optimization problems exist widely in the real word and require the optimization algorithms to track the Pareto front (PF) over time. A prediction strategy based on special points and multi-region knee points (MRKPs) is proposed for solving dynamic multiobjective optimization problems. Whenever a change is detected, the prediction strategy reacts effectively to the change by generating four subpopulations based on four strategies. The first subpopulation is created by selecting the representative individuals using a special point strategy. The second subpopulation consists of a solution set using a multiregion knee point strategy. The third subpopulation is introduced to the nondominated set by a convergence strategy. The fourth subpopulation comprises diverse individuals from an adaptive diversity maintenance strategy. The four subpopulations merge into a new population to accurately predict the location and distribution of the PF after an environmental change. MRKP is compared with four popular evolutionary algorithms on standard instances with different changing dynamics. Finally, MRKP provides better results than other competitors in terms of Inverted Generational Distance and Hypervolume metrics. The results reveal that MRKP can quickly adapt to changing environments and provide good tracking ability when dealing with dynamic multiobjective optimization problems.