Aijun Wu

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Canonical particle swarm optimization (PSO) utilizes the historical best experience and neighborhood’s best experience of particle through linear summation to guide its search direction. Such a learning strategy is easy to use, but is inefficient when searching in the complex problem space since the global best individual only considers the fitness value(More)
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