Intelligent Optimization Strategies in Uncertain and Dynamic Environments

In recent years, particle swarm optimization has emerged as a suitable optimization technique for dynamic environments, mainly its multi-swarm variant. However, in the search for good solutions some particles may produce transitions between non improving ones. Although this fact is usual in stochastic algorithms like PSO, when the problem at hand is dynamic in some sense one can consider that those particles are wasting resources (evaluations, time, etc). To overcome this problem, a novel operator for controlling particle trajectories is introduced into a multi-swarm PSO algorithm. Experimental studies over a benchmark problem shows the benefits of the proposal.