Research on particle swarm optimization of variable parameter
Aimed at particle swarm optimization, since there are a fewer adjustable parameters, when solving the multi-dimensional function, it is easy to meet premature convergence problem, so an improved particle swarm optimization of variable parameters is proposed. According to particle movement characteristics, the formula of particle velocity updating is improved to make all integrated into the corresponding weight factor; through weight factor, the particle optimization performance is adjusted. Three standard test functions are used for test, with comparison with other algorithms, and the simulation results show that by setting different weight factors, the proposed algorithm has better optimization precision and ability to execute, and the better result can be achieved when solving the multi-dimensional function.
KeywordsParticle Swarm Optimization Parameter Selection Weight Factor Convergence Analysis
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- 1.Perez R E, Behdinan K. Particle swarm approach for structural design optimization[J]. Computers&Structures,2007,85(19/20):1579-1588.Google Scholar
- 2.Coelho L S, Sierakowski C A. A software tool for teaching of particle swarm optimization fundamentals[J].Advances in Engineering Software,2008,39(11):877-887.Google Scholar
- 3.Fan S, Zahara E. A hybrid simplex search and particle swarm optimization for unconstrained optimization[J].European Journal of Operational Research,2007,181(2):527-548.Google Scholar
- 4.Li X D. Niching without niching paramenters:particle swarm optimization using a ring topology[J].IEEE Transactions on Evolutionary Computation,2010,14(1):150-169.Solitons&Fractals,2008,37(3):698-705.Google Scholar
- 5.Shi Y, Eberhart R C. A modified particle swarm optimizer. Proceedings of IEEE Enternational Conference on Evolutionary Computation, Anchorage,1998,69-73.Google Scholar
- 6.Clerc M. The swarm and the queen:Towards a deterministic and adaptive particle swarm optimization. Proceedings of the Congress of Evolutionary Computation, Washington,1999:1951-1957.Google Scholar
- 7.Jiao B, Lian Z G, Gu X S. A dynamic inertia weight particle swarm optimization algorithm[J].ChaosGoogle Scholar
- 8.G Ganesan and Y Li. Cooperative spectrum sensing for cognitive radios under bandwidth constraints[C]. Proceedings of the Wireless Communications and Networking Conference.Jun.2007:1-5.Google Scholar
- 9.Trelea I C. The particle swarm-explosion, stability, and convergence in a multidimensional complex space: optimization algorithm: Convergence analysis and parameter selection[J]; Information Process Letters,2003,85(6):317-325.Google Scholar