An Improved Particle Swarm Optimization Algorithm

Ri Su Na; Qiang Li; Li Ji Wu

doi:10.4028/www.scientific.net/AMR.538-541.2658

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 538-541An Improved Particle Swarm Optimization Algorithm

An Improved Particle Swarm Optimization Algorithm

Abstract:

Based on the standard particle swarm optimization an improved PSO algorithm was introduced in this paper. The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergence is improved by joining the negative gradient. By adding negative term on standard PSO formula, combining with coefficient of negative gradient term and inertia weight , lead to effectively balance between the local and global search ability. It will accelerate convergence and avoid local optimum. Moreover, from the bionic point of view, this improved PSO algorithm is closer to the reality of the actual situation of the bird flocking. From the simulation results of four typical test functions, it can be seen that an improved particle swarm optimization with negative gradient can significantly improve the solving speed and solution quality.

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Periodical:

Advanced Materials Research (Volumes 538-541)

Pages:

2658-2661

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.538-541.2658

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Online since:

June 2012

Authors:

Ri Su Na, Qiang Li, Li Ji Wu

Keywords:

Inertia Weight, Negative Gradient, Optimization Algorithm, Particle Swarm Optimization (PSO), Swarm Intelligence

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