An improved particle swarm algorithm and its application

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• 作者：Bingkun Gao^a ; Xiuju Ren^b ; Mingzi Xu^a ;   ; ^b ;   ; ^{renxiuju229@163.com}
• 关键词：Particle Swarm Optimization ; Optimal Particle ; Inertia Weight ; Learning Factor ; Combination Forecasting Model
• 刊名：Procedia Engineering
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：15
• 期：Complete
• 页码：2444-2448
• 全文大小：317 K

文摘

In this article we proposed an improved particle swarm algorithm .In this algorithm, three aspects were improved that from the optimal particle, inertia weight and learning factor. Experimental results show that the improved PSO algorithm compared with the standard particle swarm algorithm overcome easy to fall into local optimal solution, slow convergence, poor accuracy and other shortcomings and We use this improved method to solve the weight in combination forecasting model, simulation results demonstrate the effectiveness of the method, improved method is more conducive to the weight solution of combination forecasting model.