Abstract
Particle swarm optimization (PSO) is a metaheuristic inspired on the flight of a flock of birds seeking food, which has been widely used for a variety of optimization tasks [1,2]. However, its use in multimodal optimization (i.e., single-objective optimization problems having multiple optima) has been relatively scarce.
In this chapter, we will review the most representative PSO-based approaches that have been proposed to deal with multimodal optimization problems. Such approaches include the simple introduction of powerful mutation operators, schemes to maintain diversity that were originally introduced in the genetic algorithms literature (e.g., niching [3,4]), the exploitation of local topologies, the use of species, and clustering, among others.
Our review also includes hybrid methods in which PSO is combined with another approach to deal with multimodal optimization problems. Additionally, we also present a study in which the performance of different PSO-based approaches is assessed in several multimodal optimization problems. Finally, a case study consisting on the search of solutions for systems of nonlinear equations is also provided.
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Barrera, J., Coello, C.A.C. (2009). A Review of Particle Swarm Optimization Methods Used for Multimodal Optimization. In: Lim, C.P., Jain, L.C., Dehuri, S. (eds) Innovations in Swarm Intelligence. Studies in Computational Intelligence, vol 248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_2
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DOI: https://doi.org/10.1007/978-3-642-04225-6_2
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