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A novel niching particle swarm optimization (PSO) method based on a hill valley function is proposed. In this algorithm, the hill valley function is used to decide whether the niching seed particle and its neighbour are on the same hill, and if they are, a new niching is formed. The hill valley function is also used to decide whether two niching subswarms are on the same hill, and if they are, the...
This paper presents a new BP neural network algorithm which is based on an improved particle swarm optimization (PSO) algorithm. The improved PSO (which is called IPSO) algorithm adopts adaptive inertia weight and acceleration coefficients to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. This study uses the IPSO algorithm to...
Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schafferpsilas F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once...
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