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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Bibliographic Details
Published in:Applied mathematics and computation Vol. 186; no. 2; pp. 1407 - 1422
Main Authors: He, Qie, Wang, Ling
Format: Journal Article
Language:English
Published: New York, NY Elsevier Inc 15.03.2007
Elsevier
Subjects:
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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Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2006.07.134