Belief space-guided approach to self-adaptive particle swarm optimization

Abstract

Particle swarm optimization (PSO) performance is sensitive to the control parameter values used, but tuning of control parameters for the problem at hand is computationally expensive. Self-adaptive particle swarm optimization (SAPSO) algorithms attempt to adjust control parameters during the optimization process, ideally without introducing additional control parameters to which the performance is sensitive. This paper proposes a belief space (BS) approach, borrowed from cultural algorithms (CAs), towards development of a SAPSO. The resulting BS-SAPSO utilizes a belief space to direct the search for optimal control parameter values by excluding non-promising configurations from the control parameter space. The resulting BS-SAPSO achieves an improvement in performance of 3–55% above the various baselines, based on the solution quality of the objective function values achieved on the functions tested.