On performance metrics and particle swarm methods for dynamic multiobjective optimization problems

This paper describes two performance measures for measuring an EMO (evolutionary multiobjective optimization) algorithm´s ability to track a time-varying Pareto-front in a dynamic environment. These measures are evaluated using a dynamic multiobjective test function and a dynamic multiobjective PSO, maximinPSOD, which is capable of handling dynamic multiobjective optimization problems. maximinPSOD is an extension from a previously proposed multiobjective PSO, maximinPSO. Our results suggest that these performance measures can be used to provide useful information about how well a dynamic EMO algorithm performs in tracking a time-varying Pareto-front. The results also show that maximinPSOD can be made self-adaptive, tracking effectively the dynamically changing Pareto-front.