Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …

Although various population initialization techniques have been employed in evolutionary
algorithms (EAs), there lacks a comprehensive survey on this research topic. To fill this gap …

In classification, feature selection is an important data pre-processing technique, but it is a
difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is …

Deep learning is widely used to automate processes, improve performance, detect patterns,
and solve problems. Thus, applications of deep learning are limitless. Particle swarm …

Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions
of simple information-processing units. The concept of a swarm suggests multiplicity …

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization
problems. For an optimization problem, population initialization plays a significant role in …

Nowadays, particle swarm optimisation (PSO) is one of the most commonly used
optimisation techniques. However, PSO parameters significantly affect its computational …

The performance of the Particle Swarm Optimization method in coping with Constrained
Optimization problems is investigated in this contribution. In the adopted approach a non …

Abstract Particle Swarm Optimization (PSO), in its present form, has been in existence for
roughly a decade, with formative research in related domains (such as social modelling …

This paper presents approaches for effectively computing all global minimizers of an
objective function. The approaches include transformations of the objective function through …