Particle swarm optimization is a heuristic global optimization method put forward originally
by Doctor Kennedy and Eberhart in 1995. Various efforts have been made for solving …

During the last decade, many variants of the original particle swarm optimization (PSO)
algorithm have been proposed for global numerical optimization, but they usually face many …

The particle swarm optimization (PSO) algorithm is simple to implement and converges
quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance …

The particle swarm optimization algorithm (PSO) is a meta‐heuristic algorithm with swarm
intelligence. It has the advantages of easy implementation, high convergence accuracy, and …

Particle swarm optimization (PSO) algorithm is simple stochastic global optimization
technique, but it exists unbalanced global and local search ability, slow convergence speed …

To control particles to fly inside search space and deal with the problems of slow
convergence speed and premature convergence of particle swarm optimization (PSO) …

Meta-heuristic search algorithms are developed to solve optimization problems. Such
algorithms are appropriate for global searches because of their global exploration and local …

Although the particle swarm optimization (PSO) algorithm has been widely used to solve
many real world problems, it is likely to suffer entrapment in local optima when addressing …

Abstract Particle Swarm Optimization (PSO) is a bioinspired meta–heuristic for solving
complex global optimization problems. In standard PSO, the particle swarm frequently gets …

This paper presents a new and improved version of particle swarm optimization algorithm
(PSO) combining the global best and local best model, termed GLBest-PSO. The GLBest …