On performance metrics and particle swarm methods for dynamic multiobjective optimization problems
Xiaodong Li, Jürgen Branke, Michael Kirley · 2007
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.maximinPSODis 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 thatmaximinPSODcan be made self-adaptive, tracking effectively the dynamically changing Pareto-front.