Abstract
Particle Swarm Optimization (PSO ) is an optimization technique, deriving from the EO [5]: the main features are the natural inspiration and the possibility to implement PSO onto different levels. This chapter is divided in three section: (1) the PSO definitions and relationship with MAS (Multi Agent Systems) framework; (2) three applications of PSO methods; (3) some general conclusions and perspectives. We try to show that PSO has a marked multidisciplinary character since systems with swarm characteristics can be observed in a variety of domains: the main argument in favor to PSO is proper the multidisciplinary character. Besides, POS can resolve multiobjective otpimization problems in efficient way, because POS naturally incorporates some concepts from Pareto-Optimal framework.
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Cecconi, F., Campenní, M. (2010). PSO (Particle Swarm Optimization): One Method, Many Possible Applications. In: Sarker, R.A., Ray, T. (eds) Agent-Based Evolutionary Search. Adaptation, Learning, and Optimization, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13425-8_11
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DOI: https://doi.org/10.1007/978-3-642-13425-8_11
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