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 …

Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired
by the collective behavior of social animals. In particle swarm optimization (PSO) the set of …

There exist many multiobjective optimization problems (MOPs) containing a large number of
decision variables in real-world applications, which are known as large-scale MOPs. Due to …

The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological
behaviour of bird flocks searching for food sources. In this nature-based algorithm …

The particle swarm is an algorithm for finding optimal regions of complex search spaces
through the interaction of individuals in a population of particles. This paper analyzes a …

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 …

Particle swarm optimization (PSO) has undergone many changes since its introduction in
1995. As researchers have learned about the technique, they have derived new versions …

Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration
into the adaptive mechanisms that enable intelligent behaviour in complex and changing …

This paper presents an efficient and reliable evolutionary-based approach to solve the
optimal power flow (OPF) problem. The proposed approach employs particle swarm …

The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective
optimizer (mainly when dealing with continuous search spaces) has motivated researchers …