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- W2941035650 abstract "Particle swarm optimisation (PSO) is a widely used meta-heuristic algorithm, which has been successfully applied to a large variety of problems in optimisation, prediction, classification, visualisation and robotics. However, the underlying mechanism that leads to good solutions in these domains is not well understood. A number of parameters influence the algorithm mainly by determining the balance between exploration and exploitation. In this chapter, we describe the behaviour of PSO as a product of random matrices, and use a Lyapunov exponent to precisely characterise the critical parameters for PSO. The theoretical results are discussed based on numerical experiments for standard benchmark problems." @default.
- W2941035650 created "2019-05-03" @default.
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- W2941035650 date "2018-09-29" @default.
- W2941035650 modified "2023-09-26" @default.
- W2941035650 title "The critical state in particle swarm optimisation" @default.
- W2941035650 doi "https://doi.org/10.1049/pbce119f_ch10" @default.
- W2941035650 hasPublicationYear "2018" @default.
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