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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm inspired by the social behaviors of bird flocking and fish schooling. Each particle searches for a better solution through interaction with other particles. However, PSO tends to prematurely converge to a local minimum, particularly for large-scale multimodal problems. This paper proposes two extensions for avoiding...
Particle swarm optimization (PSO) is a type of meta-heuristic optimization method built on the basis of the principle of collective behaviors exhibited by simple organisms. Although PSO is a model of social behaviors, the present research attempts to model learning behaviors of an individual human with PSO in order to evaluate our hypothesis that the dynamics of knowledge that are being acquired and...
Particle swarm optimization (PSO) method is an evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle swarm optimization with area extension (AEPSO) was introduced to solve the weaknesses of basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed...
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