Bare bones particle swarm with scale mixtures of Gaussians for dynamic constrained optimization

Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown potential for solving single-objective constrained optimization problems in static environments. In this paper, a generalized BBPSO for dynamic single-objective constrained optimization problems is proposed. An empirical study was carried out to evaluate the performance of the proposed approach. Experimental results show the suitability of the proposed algorithm in terms of effectiveness to find good solutions for all benchmark problems investigated. For comparison purposes, experimental results found by other algorithms are also presented.