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Zbl 1127.90407
Dong, Ying; Tang, Jiafu; Xu, Baodong; Wang, Dingwei
An application of swarm optimization to nonlinear programming. (English)
[J] Comput. Math. Appl. 49, No. 11-12, 1655-1668 (2005). ISSN 0898-1221

Summary: Particle swarm optimization (PSO) is an optimization technique based on population, which has similarities to other evolutionary algorithms. It is initialized with a population of random solutions and searches for optima by updating generations. Particle swarm optimization has become the hotspot of evolutionary computation because of its excellent performance and simple implementation. After introducing the basic principle of the PSO, a particle swarm optimization algorithm embedded with constraint fitness priority-based ranking method is proposed in this paper to solve nonlinear programming problem. By designing the fitness function and constraints-handling method, the proposed PSO can evolve with a dynamic neighborhood and varied inertia weighted value to find the global optimum. The results from this preliminary investigation are quite promising and show that this algorithm is reliable and applicable to almost all of the problems in multiple-dimensional, nonlinear and complex constrained programming. It is proved to be efficient and robust by testing some example and benchmarks of the constrained nonlinear programming problems.

MSC 2000:: *90C30 Nonlinear programming
90C59 Approximation methods and heuristics

Keywords: particle swarm optimization; nonlinear programming; global optimization; evolutionary algorithm; priority-based ranking

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