In recent years, many improved particle swarm optimization (PSO) algorithms have been
developed to improve the performance of PSO. These improved algorithms have greatly …

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been
applied to solve various real‐world problems. However, the performance of PSO on a …

To overcome the disadvantages of premature convergence and easy trapping into local
optimum solutions, this paper proposes an improved particle swarm optimization algorithm …

To avoid the premature convergence caused by basic particle swarm optimization (PSO) in
resolving engineering optimization design of highly complex and nonlinear constraints, a …

Particle swarm optimization (PSO) is one of the most classical metaheuristic algorithms that
has gained significant attention since its inception. It has some inherent advantages, such as …

In this article, a new model for particle swarm optimization (PSO) is proposed. In this model,
each particle's behaviour is influenced by the best experience among its neighbours, its own …

Purpose–In this paper, a new method to improve the performance of particle swarm
optimization is proposed. Design/methodology/approach–This paper introduces hypothesis …

Particle swarm optimization (PSO) algorithms have been proposed to solve optimization
problems in engineering design, which are usually constrained (possibly highly constrained) …

Penalty function approach is the common method for constrained optimization in
engineering design. However, its results are sensitive to the penalty factor, and it is …

Particle swarm optimization (PSO) algorithm has shown promising performances on various
benchmark functions and engineering optimization problems. However, it is still difficult to …