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The fifth is PSO algorithms with new efficient learning strategy: Comprehensive learning particle swarm optimizer (CLPSO) [49], Orthogonal particle swarm optimization (OPSO) [38], Orthogonal learning particle swarm optimization (OLPSO) [97], Genetic learning particle swarm optimization [35].
The function was subsequently optimized by particle swarm optimization.
Particle swarm optimization is used to optimize the model parameters.
Particle swarm optimization is an evolutionary algorithm for optimizing functions.
Standard particle swarm optimization (SPSO-BK) [8], Standard particle swarm optimization 2011 [24], Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) [66], Adaptive particle swarm optimization (APSO) [96].
Particle Swarm Optimization (PSO) algorithm was used to maximize probability, and gave the optimized parameters.
Three different variants of the Particle Swarm Optimization were tested, namely, the simple Particle Swarm Optimization, the inertia Particle Swarm Optimization and the Constriction Particle Swarm Optimization.
Comparative Study of Different Approaches to Particle Swarm Optimization in Theory and Practice.
Wang, Y. et al. An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm.
The novel dynamic particle swarm optimization algorithm framework was given.
The new algorithm is termed Democratic Particle Swarm Optimization.
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