A combined constraint handling framework: an empirical study

Si, Chengyong; Hu, Junjie; Lan, Tian; Wang, Lei; Wu, Qidi

doi:10.1007/s12293-016-0221-2

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…The authors of [86] introduced an operational efficient model based on Data Envelopment Analysis (DEA) and introduced DE along with the feasibility rules to optimize the mentioned model. The authors of [87] proposed a combined constraint handling framework, known as CCHF, for solving constrained optimization problems, in which the features of two well-known CHTs (i.e. feasibility rules and multi-objective optimization) were addressed in three different situations (feasible situation, infeasible situation, and semi-feasible situation).…”

Section: Feasibility Rules In Differential Evolution (De)mentioning

confidence: 99%

A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

Rahimi

Gandomi

Chen

et al. 2022

Arch Computat Methods Eng

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Most real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for analysis. The results indicate that the constraint-handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” and “ Mathematics” were identified as the top three research fields in which future research work is anticipated to increase.

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Section: Feasibility Rules In Differential Evolution (De)mentioning

confidence: 99%

A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

Rahimi

Gandomi

Chen

et al. 2022

Arch Computat Methods Eng

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show abstract

“…is the estimated ratio between the feasible region and the search space, LI, NI, LE, NE is the number of linear inequality constraints, nonlinear inequality constraints, linear equality constraints and nonlinear equality constraints respectively, a is the number of active constraints at the optimal solution and * () fx is the objective function value of the best known solution. We also classify these benchmark functions into different groups [9] as shown in Table III.…”

Section:  mentioning

confidence: 99%

“…Based on the basic CHTs, many concepts like cooperative coevolution [4], [5] and ensemble [6], [7] have been proposed. And some researchers tried to solve the problems from some other aspects, e.g., problem characteristics [8], [9].…”

Section: Introductionmentioning

confidence: 99%

On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

2020

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Cooperation between Evolutionary Algorithms (EAs) and Constraint Handling Techniques (CHTs) plays an important role in Constrained Optimization Evolutionary Algorithms (COEAs). A constrained composite differential evolution (C 2 oDE) sets a good example on the cooperation. This paper tries to further study the inner mechanism, i.e., the effect of different methods on generating and selecting solutions. In the solution generating part, a new method, which adopts stochastic ranking in selecting the best individual in the DE mutation operators, is proposed. In the solution selecting part, a new constraint handling technique combination is added. The experiments on benchmark functions from IEEE CEC2006 verify the effect. During the experiment, it is found that a new cooperation manner performs better than C 2 oDE, which reflects the importance of cooperation. INDEX TERMS constrained optimization, constraint handling techniques, differential evolution, cooperation manner.

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Comparison of Differential Evolution Algorithms on the Mapping Between Problems and Penalty Parameters

Shen

Zou

et al. 2017

Lecture Notes in Computer Science

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A combined constraint handling framework: an empirical study

Cited by 12 publications

References 28 publications

A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

Comparison of Differential Evolution Algorithms on the Mapping Between Problems and Penalty Parameters

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