A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization

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-han...

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Vydáno v:	Applied mathematics and computation Ročník 186; číslo 2; s. 1407 - 1422
Hlavní autoři:	He, Qie, Wang, Ling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY Elsevier Inc 15.03.2007 Elsevier
Témata:	Calculus of variations and optimal control Constrained optimization Exact sciences and technology Feasibility-based rule Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Particle swarm optimization Sciences and techniques of general use Simulated annealing Feasibility-based rule Particle swarm optimization Simulated annealing Constrained optimization Constraint Evolutionary algorithm Optimization method Estimator robustness Determination Penalty method Convergence Particle Penalty function Efficiency Population Mathematical programming Addition Unconstrained optimization Benchmarks Algorithm Contrast Numerical analysis Simulation Applied mathematics Region Feasibility Performance Application Variety Gain Handling
ISSN:	0096-3003, 1873-5649
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Abstract	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.
AbstractList	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.
Author	He, Qie Wang, Ling
Author_xml	– sequence: 1 givenname: Qie surname: He fullname: He, Qie email: heq@mails.tsinghua.edu.cn – sequence: 2 givenname: Ling surname: Wang fullname: Wang, Ling
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Keywords	Feasibility-based rule Particle swarm optimization Simulated annealing Constrained optimization Constraint Evolutionary algorithm Optimization method Estimator robustness Determination Penalty method Convergence Particle Penalty function Efficiency Population Mathematical programming Addition Unconstrained optimization Benchmarks Algorithm Contrast Numerical analysis Simulation Applied mathematics Region Feasibility Performance Application Variety Gain Handling
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Snippet	During the past decade, hybrid algorithms combining evolutionary computation and constraint-handling techniques have shown to be effective to solve constrained...
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SubjectTerms	Calculus of variations and optimal control Constrained optimization Exact sciences and technology Feasibility-based rule Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Particle swarm optimization Sciences and techniques of general use Simulated annealing
Title	A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization
URI	https://dx.doi.org/10.1016/j.amc.2006.07.134
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