An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization

Silva, Eduardo K. da; Barbosa, Hélio J. C.; Lemonge, Afonso C. C.

doi:10.1007/s11081-010-9114-2

Cited by 45 publications

(51 citation statements)

References 16 publications

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“…In [28] a self-adaptive fitness formulation is used. The results reported in [29] were obtained with an adaptive penalty method with dynamic use of DE variants, while in [30] a self-adaptive penalty based genetic algorithm is used. Table 3 reports the best results found by these methods and by our proposed best implementations FA1 and FA2.…”

Section: Resultsmentioning

confidence: 99%

Extensions of Firefly Algorithm for Nonsmooth Nonconvex Constrained Optimization Problems

Francisco

Costa

Rocha

2016

Computational Science and Its Applications – ICCSA 2016

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Firefly Algorithm (FA) is a stochastic population-based algorithm based on the flashing patterns and behavior of fireflies. Original FA was created and successfully applied to solve bound constrained optimization problems. In this paper we present extensions of FA for solving nonsmooth nonconvex constrained global optimization problems. To handle the constraints of the problem, feasibility and dominance rules and a fitness function based on the global competitive ranking, are proposed. To enhance the speed of convergence, the proposed extensions of FA invoke a stochastic local search procedure. Numerical experiments to validate the proposed approaches using a set of well know test problems are presented. The results show that the proposed extensions of FA compares favorably with other stochastic population-based methods.

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Section: Resultsmentioning

confidence: 99%

Extensions of Firefly Algorithm for Nonsmooth Nonconvex Constrained Optimization Problems

Francisco

Costa

Rocha

2016

Computational Science and Its Applications – ICCSA 2016

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“…Here, the stochastic ranking technique is denoted by SRES and the global competitive ranking technique is GRES. An adaptive penalty scheme for constraint handling with dynamic use of variants of differential evolution (DUVDE) (Silva et al, 2011) is also used in the comparison. We set the same value of maximum number of generations according to (Runarsson and Yao, 2000;Runarsson and Yao, 2003).…”

Section: Comparing With Other Methodsmentioning

confidence: 99%

“…of the best objective functions among 30 runs are shown in Table 4. These results are taken from the cited papers (Silva et al, 2011), (Runarsson and Yao, 2000) and (Runarsson and Yao, 2003) respectively. In m-CDE1, we use the population size N dependent on the dimension of test problem and use the maximum number of generations according to (Runarsson and Yao, 2000;Runarsson and Yao, 2003), although in DUVDE the authors used the population size 50 and the maximum number of generations 3684 for all the tested problems.…”

Section: Comparing With Other Methodsmentioning

confidence: 99%

“…(Barbosa and Lemonge, 2003) proposed a parameter-less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems. In the very recent paper the authors proposed this adaptive penalty scheme for differential evolution (Silva et al, 2011). (Hedar and Fukushima, 2006) proposed a filter simulated annealing method for constrained continuous global optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Global Competitive Ranking for Constraints Handling With Modified Differential Evolution

Azad¹,

Fernandes

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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Constrained nonlinear programming problems involving a nonlinear objective function with inequality and/or equality constraints introduce the possibility of multiple local optima. The task of global optimization is to find a solution where the objective function obtains its most extreme value while satisfying the constraints. Depending on the nature of the involved functions many solution methods have been proposed. Most of the existing population-based stochastic methods try to make the solution feasible by using a penalty function method. However, to find the appropriate penalty parameter is not an easy task. Population-based differential evolution is shown to be very efficient to solve global optimization problems with simple bounds. To handle the constraints effectively, in this paper, we propose a modified constrained differential evolution that uses self-adaptive control parameters, a mixed modified mutation, the inversion operation, a modified selection and the elitism in order to progress efficiently towards a global solution. In the modified selection, we propose a fitness function based on the global competitive ranking technique for handling the constraints. We test 13 benchmark problems. We also compare the results with the results found in literature. It is shown that our method is rather effective when solving constrained problems.

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“…For the handling of multi-constraints in manufacturing problems, refer to recent literature [10,34,57,58,59,60,61,62,63,64].…”

Section: Handling Of Multi-constraintsmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases. To improve the problem solving capability, research in intelligent optimization algorithm based on different domain characteristics never stopped. From the view of manufacturing, this chapter classified and comprehensively analyzed all kinds of manufacturing optimization problems and their general methods, illustrated the application features and challenges of intelligent optimization algorithm in manufacturing, and summarized the development needs and trends of intelligent optimization algorithm in the field of manufacturing system.

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An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization

Cited by 45 publications

References 16 publications

Extensions of Firefly Algorithm for Nonsmooth Nonconvex Constrained Optimization Problems

Extensions of Firefly Algorithm for Nonsmooth Nonconvex Constrained Optimization Problems

Global Competitive Ranking for Constraints Handling With Modified Differential Evolution

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

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