Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

Particle swarm optimization (PSO) is a population-based, stochastic search algorithm
inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather …

This paper presents a comprehensive learning particle swarm optimization algorithm with
enhanced exploration and exploitation, named as “heterogeneous comprehensive learning …

Abstract According to the “No Free Lunch (NFL)” theorem, there is no single optimization
algorithm to solve every problem effectively and efficiently. Different algorithms possess …

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. In many cases, the difference between two variants can be …

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn
increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have …

Particles in the standard particle swarm optimization (PSO) algorithms, and most of its
modifications, follow the same behaviours. That is, particles implement the same velocity …

In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity
and many variants and hybrid approaches have been proposed to improve it. In this paper …

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique
inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control …