Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm

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@Article{Chan20111623,

• author = "Kit Yan Chan and Tharam S. Dillon and C. K. Kwong",
• title = "Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm",
• journal = "Information Sciences",
• volume = "181",
• number = "9",
• pages = "1623--1640",
• year = "2011",
• ISSN = "0020-0255",
• DOI = "doi:10.1016/j.ins.2011.01.006",
• URL = "http://www.sciencedirect.com/science/article/B6V0C-51X1VSV-7/2/12b12f977248967cf70b6cfd1dc37507",
• keywords = "genetic algorithms, genetic programming, PSO, Particle swarm optimisation, Time-varying systems, Polynomial modelling",
• abstract = "In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modelling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modelling methods which have been developed for solving dynamic optimisation problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP.",

}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon Che Kit Kwong

Citations