AN EFFICIENT ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATION: PARTICLE SWARM OPTIMIZATION
Keywords:
Particle swarm optimization (PSO), Function optimization, Goldstein- Price , De jong functionAbstract
The Particle swarm optimization (PSO) is one of the evolutionary computation techniques that can be applied to a wide range of real world problems. In this paper, PSO algorithm is numerically illustrated with a one dimensional unconstrained problem. The efficiency and robustness of this algorithm is demonstrated by applying it to the benchmark functions namely Goldstein- Price and De jong functions and the results were compared with those obtained using other optimization algorithms.Matlab code is created and used to solve the benchmark functions.
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