Constrained Single-Objective Optimization Using Differential Evolution

Zielinski, K.; Laur, R.

doi:10.1109/cec.2006.1688312

Cited by 60 publications

(22 citation statements)

References 9 publications

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“…The purpose of these examples is to test the ability of MSOEA to handle highly constrained optimization problems, the ability to handle large search spaces (i.e., many design parameters), its comparison to other optimization algorithms, and its robustness. MSOEA is compared with four other algorithms: (1) one of the most common algorithms in modern synthesis tools: the genetic algorithm, combined with penalty functions to handle constraints (GAPF); (2) the differential evolution algorithm as search engine and the same penalty-based method to handle constraints (DEPF); (3) the competitive co-evolutionary differential evolution algorithm CODE [Liu et al 2009]; and (4) an algorithm that combines the selection-based method for constrained optimization proposed by Deb [2000] and differential evolution (denoted as SBDE) [Zielinski and Laur 2006]. As discussed above, there are different techniques to select the base vector in the mutation operator of the DE algorithm.…”

Section: Resultsmentioning

confidence: 99%

A memetic approach to the automatic design of high-performance analog integrated circuits

Liu

Fernández²,

Gielen

et al. 2009

ACM Trans. Des. Autom. Electron. Syst.

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This article introduces an evolution-based methodology, named memetic single-objective evolutionary algorithm (MSOEA), for automated sizing of high-performance analog integrated circuits. Memetic algorithms may achieve higher global and local search ability by properly combining operators from different standard evolutionary algorithms. By integrating operators from the differential evolution algorithm, from the real-coded genetic algorithm, operators inspired by the simulated annealing algorithm, and a set of constraint handling techniques, MSOEA specializes in handling analog circuit design problems with numerous and tight design constraints. The method has been tested through the sizing of several analog circuits. The results show that design specifications are met and objective functions are highly optimized. Comparisons with available methods like genetic algorithm and differential evolution in conjunction with static penalty functions, as well as with intelligent selection-based differential evolution, are also carried out, showing that the proposed algorithm has important advantages in terms of constraint handling ability and optimization quality.

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Section: Resultsmentioning

confidence: 99%

A memetic approach to the automatic design of high-performance analog integrated circuits

Liu

Fernández²,

Gielen

et al. 2009

ACM Trans. Des. Autom. Electron. Syst.

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“…In Zielinski and Laur (2006), the final value is taken as the average between the nearest bound and the current value. In this paper we adopted the technique described in Onwubolu (2004), because in many practical cases the optimum value is located at one of the bounds of a given design variable.…”

Section: A Brief Overview Of Dementioning

confidence: 99%

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

Silva

Barbosa²,

Lemonge³

2010

Optim Eng

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Differential Evolution is a simple and efficient stochastic population-based heuristics for global optimization over continuous spaces. As with other nature inspired techniques, there is no provision for constraint handling in its original formulation, and a few possibilities have been proposed in the literature. In this paper an adaptive penalty technique (APM), which has been shown to be quite effective within genetic algorithms, is adopted for constraint handling within differential evolution. The technique, which requires no extra parameters, is based on feedback obtained from the current status of the population of candidate solutions, and automatically defines, for each constraint, its corresponding penalty coefficient. Equality as well as inequality constraints can be dealt with. In this paper we additionally introduce a mechanism for dynamically selecting the mutation operator, according to its performance, among several variants commonly used in the literature. In order to assess the applicability and performance of the proposed procedure, several test-problems from the structural and mechanical engineering optimization literature are considered.

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“…e is a small tolerance for equality constraint violation allowed when user transforms equalities to inequalities, and 8d is the tolerance allowed for the distance in Eq. (14) in ADE. The default values for them in ADE are both set to 0.00 1.…”

Section: Parameters Of Adementioning

confidence: 99%

“…Montes et al [13] have proposed a DE-based approach by allowing each parent to generate more than one offspring and using three selection criteria based on feasibility to deal with the constraints, but the approach was not able to solve problems with a dimensionality higher than 22 and more than 11 nonlinear equality constraints. Zielinski and Laur [14] have handled constraints with a modified selection procedure based on a modified selection procedure, but the method failed to reach the best known solutions for four functions of the given 24 test problems.…”

Section: Introductionmentioning

confidence: 99%

An Archived Differential Evolution Algorithm for Constrained Global Optimization

Huang

Wang

2008

2008 International Conference on Smart Manufacturing Application

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Many practical engineering problems are naturally global optimization problems with complex constraints and local optimums. However, practicable and effective approaches for those constrained global optimization problems are still insufficient. A modified differential evolution algorithm is put forward for constrained global optimization problems in this work, using a special constraint-handling mechanism based on dynamic penalty functions and fitness calculation of individuals. An archive of solutions is maintained in the evolutionary process so that the best information of previous local optimums can be kept and utilized for the quality estimate of new solutions. Based on the archive of solutions, an iterative control operation is designed in the algorithm to guide the evolutionary process towards a promising space and avoid unnecessary and worthless search processes. Finally, numerical experiments based on a set of eight well-known constrained optimization problems are carried out to investigate the performance of the proposed method, and the experimental results reveal that the proposed algorithm is robust, effective and efficient in solving constrained global optimization problems.

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Constrained Single-Objective Optimization Using Differential Evolution

Cited by 60 publications

References 9 publications

A memetic approach to the automatic design of high-performance analog integrated circuits

A memetic approach to the automatic design of high-performance analog integrated circuits

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

An Archived Differential Evolution Algorithm for Constrained Global Optimization

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