A Novel Particle Swarm Optimization with Random Parameters

Abstract:

To improve the performance of standard particle swarm optimization algorithm that is easily trapped in local optimum, based on analyzing and comparing with all kinds of algorithm parameter settings strategy, this paper proposed a novel particle swarm optimization algorithm which the inertia weight (ω) and acceleration coefficients (c1 and c2) are generated as random numbers within a certain range in each iteration process. The experimental results show that the new method is valid with a high precision and a fast convergence rate.

• Periodical: Advanced Materials Research (Volumes 631-632)
• Pages: 1324-1329
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.631-632.1324
• Online since: January 2013
• Authors: Shao Rong Huang
• Keywords: Acceleration Coefficients, Inertia Weight, Particle Swarm Optimization (PSO), Random Parameters

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