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Polynomial modeling in dynamic environment based on a particle swarm optimization

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2012
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Book Chapter

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Chan, K.Y,. and Dillon, Tharam S. 2012. Polynomial modeling in dynamic environment based on a particle swarm optimization, in Lam, H.K. and Ling, S.H. and Nguyen, H.T. (ed), Computational intelligence and its application: Evolutionary computation, fuzzy logic, neural network and support vector machine techniques. pp. 23-38. London: Imperial College Press.
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Computational intelligence and its application: evolutionary computation, fuzzy logic, neural network and support vector machine techniques
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Abstract

In this chapter, a particle swarm optimization (PSO) is proposed for polynomial modeling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modeling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modeling.