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Title: Particle Swarm Optimization
Authors: Wong, Yuen Lam
Department: Department of Electronic Engineering
Issue Date: 2010
Supervisor: Supervisor: Dr. Wu, Angus K M; Assessor: Dr. Leung, Andrew C S
Abstract: Particle Swarm Optimization (PSO) is a method to find the minimum of a numerical function, on a continuous definition domain. This project aims to improve the performance of PSO and further investigate that the performance of Chaotic PSO is better than PSO. The accelerator coefficients self-recognition coefficient c1 and social coefficient c2 have the great effect on the performance of PSO. The coefficient of c2 will be studied in this project. There are 5 coefficients, position of particle, fitness value of particle, linear time-varying accelerator and non-linear time-varying accelerators. 16 Benchmark test functions are used to evaluate the performance of PSO.
Appears in Collections:Electronic Engineering - Undergraduate Final Year Projects

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