Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 36; no. 6; pp. 1407 - 1416
• Main Authors: Krohling, R.A., dos Santos Coelho, L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2006
• Subjects: Acceleration; Algorithms; Benchmark testing; Constrained optimization; Constraint optimization; Constraints; Cybernetics; Equations; Gaussian; Gaussian distribution; Genetic algorithms; Genetic mutations; min-max problem; Normal distribution; Optimization; Particle swarm optimization; particle swarm optimization (PSO); Probability distribution; Random number generation; Robustness
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2006.873185

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 s. 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