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 …

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has
gained prominence in the last two decades due to its ease of application in unsupervised …

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 …

Having passed the primitive phases and starting to revolutionize many different fields in
some way, artificial intelligence is on its way to becoming a disruptive technology. It is also …

Many optimization problems in reality involve both continuous and discrete decision
variables, and these problems are called mixed-variable optimization problems (MVOPs) …

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is
proposed, where a new velocity updating mechanism is designed to adjust the personal …

The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based
solver for realizing discrete problems. In order to make it also suitable for solving continuous …

In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is
proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is …

Social learning plays an important role in behavior learning among social animals. In
contrast to individual (asocial) learning, social learning has the advantage of allowing …

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 …