Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

The optimization problems are becoming more complicated, requiring new and efficient
optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have …

The “curse of dimensionality” and the high computational cost have still limited the
application of the evolutionary algorithm in high-dimensional feature selection (FS) …

Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with
remarkable performance when solving diverse optimization problems. However, PSO faces …

Particle swarm optimization (PSO) has shown its advantages in various optimization
problems. Topology and updating strategies are among its key concepts and have …

The particle swarm optimization (PSO) algorithm has been one of the most effective methods
for solving various engineering optimization problems. Most existing PSO variants frequently …

There are two common challenges in particle swarm optimization (PSO) research, that is,
selecting proper exemplars and designing an efficient learning model for a particle. In this …

This paper discusses the merging of two optimization algorithms, atom search optimization
and particle swarm optimization, to create a hybrid algorithm called hybrid atom search …

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 …

Particle swarm optimization (PSO) has been widely used to solve various practical
engineering problems, but it has shortcomings such as premature convergence and poor …