کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4636577 1340724 2007 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization
چکیده انگلیسی

During the past decade, hybrid algorithms combining evolutionary computation and constraint-handling techniques have shown to be effective to solve constrained optimization problems. For constrained optimization, the penalty function method has been regarded as one of the most popular constraint-handling technique so far, whereas its drawback lies in the determination of suitable penalty factors, which greatly weakens the efficiency of the method. As a novel population-based algorithm, particle swarm optimization (PSO) has gained wide applications in a variety of fields, especially for unconstrained optimization problems. In this paper, a hybrid PSO (HPSO) with a feasibility-based rule is proposed to solve constrained optimization problems. In contrast to the penalty function method, the rule requires no additional parameters and can guide the swarm to the feasible region quickly. In addition, to avoid the premature convergence, simulated annealing (SA) is applied to the best solution of the swarm to help the algorithm escape from local optima. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed HPSO. Moreover, the effects of several crucial parameters on the performance of the HPSO are studied as well.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 186, Issue 2, 15 March 2007, Pages 1407–1422
نویسندگان
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