A Novel Particle Swarm Optimization with Random Parameters

2013 ◽  
Vol 631-632 ◽  
pp. 1324-1329
Author(s):  
Shao Rong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Iteration Process ◽  
Local Optimum ◽  
Random Parameters ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Fast Convergence Rate

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.

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