Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

Construction of Fuzzy Models for Dynamic Systems Using Multi-population Cooperative Particle Swarm Optimizer

“…As already has been mentioned by Angeline [22], the original PSO, while successful in the optimization of several difficult benchmark problems, presented problems in controlling the balance between exploration and exploitation, namely when fine tuning around the optimum is attempted.…”

Multi-Objective Evolutionary Optimization of PID Controller by Chaotic Particle Swarm Optimization

“…PSO shows a promising performance on nonlinear function optimization and has thus received much attention [26].However, the performance of the traditional PSO greatly depends on its parameters, and it often suffers the problem of being trapped in local optima [27], [28]. In order to avoid these disadvantages, the chaotic particle swarm optimization (CPSO) method based on the logistic equation has been proposed [28].…”

Intelligent Backstepping Control for Genesio-Tesi ChaoticSystem Using a Chaotic Particle Swarm OptimizationAlgorithm

“…Unlike the most of the evolutionary algorithms, each potential solution (individual) in PSO is also associated with a randomized velocity, and the potential solutions, called particles, are then ""flown"" through the problem space [18]. The performance of the traditional PSO greatly depends on its parameters, and it often suffers the problem of being trapped in local optima [19], [20]. In order to avoid these disadvantages, the chaotic particle swarm optimization (CPSO) method based on the logistic equation has been proposed [20], [21].…”