Abstract
Particle Swarm Optimization (PSO) [Kennedy, 2010, Kennedy and Eberhart, 1995] is an optimization algorithm designed for continuous optimization. Like GAs, it is a population-based stochastic method, but unlike GAs it does not take its inspiration from the Theory of Evolution of Darwin, but from the social behavior of bird flocking or fish schooling [Reynolds, 1987]. For instance, one may imagine a flock of birds flying over an area, to find a point to land. In such a situation, defining where the whole swarm should land is a complex problem, since it depends on many pieces of information, such as, for instance, maximizing the availability of food or minimizing the risk of existence of predators.
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Vanneschi, L., Silva, S. (2023). Particle Swarm Optimization. In: Lectures on Intelligent Systems. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-031-17922-8_4
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DOI: https://doi.org/10.1007/978-3-031-17922-8_4
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