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In order to overcome shortages of fuzzy neural network (FNN) and basic Particle Swarm Optimization (PSO) algorithm, the article proposes a novel method that the parameters of structure equivalent FNN (SEFNN) trained by Time Variant Particle Swarm Optimization (TVPSO) algorithm. TVPSO is made adaptive in nature by adaptively and dynamically changing its acceleration coefficients and its inertia weight...
Owing to the problem that particle swarm optimization algorithm is easily falling into local optima point in optimization of high-dimensional and complex functions. In this paper, a novel two sub-swarms exchange particle swarm optimization based on multi-phases(TSEM-PSO) is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the standard PSO model, and the...
Particle swarm optimization and its modification for two sub-swarms exchange appear premature convergence for complex optimization problem, because particles' performance becomes same in seeking later period. Therefore, in this paper, a modified two sub-swarms exchange particle swarm optimization is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the...
In order to overcome the drawback of the standard PSO, such as being subject to falling into local optimization, an improved PSO algorithm based on three sub-swarms exchange is proposed. Firstly the method divides the whole swarm into three sub-swarms which evolve jointly according to three different models, that is, one evolves with the standard PSO model, and the second with social only model and...
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