title: Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems.
abstract: In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.
authors.forename: Renato A
authors.initials: RA
authors.lastname: Krohling
authors.forename: Leandro dos Santos
authors.initials: Ldos S
authors.lastname: Coelho