Evaluating the performance of DNPSO in dynamic environments

In our previous work, we proposed DNPSO (Dynamic Niching Particle Swarm Optimizer) and evaluated its performance in static multi-modal functions. DNPSO is a multiswarm technique in which the main swarm is divided into some sub-swarms and a number of free particles. Due to the dynamic creation and deletion of sub-swarms, DNPSO is able to provide a good performance both in static multi-modal and dynamic optimization problems. In this paper, the performance of DNPSO is investigated using two different dynamic function generators. Experimental results for three different dynamic environments show that DNPSO is a suitable algorithm for these types of problems and can completely outperform PSO.