Particle swarm optimisation with population size and acceleration coefficients adaptation using hidden Markov model state classification
Oussama Aoun, Malek Sarhani, Abdellatif El Afia · International Journal of Metaheuristics · 2018
Particle swarm optimisation (PSO) is a metaheuristic algorithm based on population, it succeeded in solving a large number of optimisation problems. Several adaptive PSO algorithms have been proposed to enhance the performance of the original one. In particular, parameter adaptation has become a promising issue of PSO. In this paper, we propose an adaptive control of two PSO parameters using hidden Markov model (HMM) classification to enhance PSO performance, called HMM adaptive control of PSO (HMM-ACPSO). That is, we integrate HMM to have a stochastic control of states at each iteration. Then, the classified state by HMM is used to adapt PSO with both acceleration parameters and population size. Furthermore, several strategies varying the swarm are adopted according to the classified state. We performed evaluations on several benchmark functions to test the HMM-ACPSO algorithm. Experimental results reveal that our suggested scheme gives competitive results comparing with PSO variants regarding both solution accuracy and convergence speed.