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Solving large scale global optimization using improved Particle Swarm Optimizer

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4 Author(s)
Sheng-Ta Hsieh ; Department of Communication Engineering, Oriental Institute of Technology, Taipei County, Taiwan, R.O.C. ; Tsung-Ying Sun ; Chan-Cheng Liu ; Shang-Jeng Tsai

As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer (EPUS-PSO). This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on 7 CEC 2008 test functions to present solution searching ability of the proposed method.

Published in:

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-6 June 2008