A Particle Swarm Optimizer for Constrained Numerical Optimization

Cagnina, Leticia; Esquivel, Susana Cecilia; Coello, Carlos A. Coello

doi:10.1007/11844297_92

Cited by 26 publications

(29 citation statements)

References 4 publications

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“…The constraint-handling was made with an adaptive penalty function. Cagnina, Esquivel and Coello [12] presented a combination of global-local best PSO with inertia weight. The authors also added a dynamic mutation operator to promote diversity.…”

Section: Pso Conceptsmentioning

confidence: 99%

“…For the first experiment, we included some representative convergence graphs as to show the effect of the modifications in our proposed PSO. The state-of-the-art approaches selected for comparison in the second experiment were the PSO proposed by Toscano & Coello [8], labeled as approach "A", Li et al PSO approach [11] called approach "B" (they reported results just in seven problems), Lu & Chen approach [13] named approach "C" and finally Cagnina et al approach [12] called approach "D".…”

Section: Experimental Designmentioning

confidence: 99%

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Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

Mendoza¹,

Mezura-Montes²

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-In this paper, we propose a novel approach to solve constrained optimization problems based on particle swarm optimization (PSO). First, an empirical comparison of the most popular PSO variants is presented as to select the most convenient among them. After that, the PSO variant chosen is improved in: (1) its parameter control with a dynamic proposal as to promote a better exploration of the search space and to avoid premature convergence and (2) its constraint-handling mechanism by using multiobjective concepts as to promote a better approach to the feasible region. The algorithm is tested on a set of 13 well-known benchmark problems and the obtained performance is compared against some PSO variants and stateof-the-art approaches. Based on the results presented some conclusions are drawn and the future work is established.

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Section: Pso Conceptsmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

Mendoza¹,

Mezura-Montes²

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…There exits a tradeoff between the computation cost and storage capacity of nondominated solutions. The simplest way could customize an archive large enough and adopt elitist policy to update the archive [43]. e.g.…”

Section: (2) External Archivementioning

confidence: 99%

“…The nearest neighbor particles is organized together according to the calculated distance of the first objective function's fitness value, which could be also considered as a cluster operation, furthermore, the local optima among the neighbors in terms of the fitness value of the second objective function. In [42,43,59], Hu used a extended memory as the candidate pool which has the same selection mechanism.…”

Section: Best Particle Selectionmentioning

confidence: 99%

RhoGTPase Involvement in Breast Cancer Migration and Invasion

Simpson¹

2008

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“…For that sake, our approach contains a constraint-handling technique as well as a mechanism to update the velocity and position of the particles [2], which is extended by adding to it a bi-population and a shake-mechanism as a way to avoid premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A bi-population PSO with a shake-mechanism for solving constrained numerical optimization

Cagnina

Esquivel

Coello

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical constrained optimization problems. The approach uses a single method to handle different types of constraints (linear, nonlinear, equality or inequality) and it incorporates a shakemechanism and a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions taken from the specialized literature and is compared with respect to algorithms representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a highly competitive alternative to solve constrained optimization problems.

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A Particle Swarm Optimizer for Constrained Numerical Optimization

Cited by 26 publications

References 4 publications

Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

RhoGTPase Involvement in Breast Cancer Migration and Invasion

A bi-population PSO with a shake-mechanism for solving constrained numerical optimization

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