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• W2122265268 abstract "Particle swarm methods are inspired from the dynamics of social interaction and employ information sharing to seek solutions to difficult optimisation problems. In this paper we introduce an approach that combines ideas from particle swarm optimisation (PSO) and the theory of nonextensive statistical mechanics. We develop two algorithms that adopt this approach and conduct an experimental study using benchmark functions to investigate their effectiveness in nonlinear optimisation. Results appear to be promising, as the tested algorithms outperform in most cases the standard PSO and other, recently proposed, PSO variants." @default.
• W2122265268 created "2016-06-24" @default.
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• W2122265268 date "2008-06-13" @default.
• W2122265268 modified "2023-09-23" @default.
• W2122265268 title "Particle Swarms and Nonextensive Statistics for Nonlinear Optimisation" @default.
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• W2122265268 doi "https://doi.org/10.2174/1874110x00802010173" @default.
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