Constraint handling in genetic algorithms using a gradient-based repair method

Chootinan, Piya; Chen, Anthony

doi:10.1016/j.cor.2005.02.002

Cited by 210 publications

(92 citation statements)

References 12 publications

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“…Although it is very easy to implement, it performs bad for problems where the ratio between the sizes of feasible region and search region is small and an initial population consists of infeasible individuals only. (3) Repair method The repair method is another class of commonly used constraint-handling methods [15], [20], [21]. Unlike the penalty-based method tolerating infeasible candidate solutions, the repair method, just as its name implies, repairs them instead.…”

Section: Constraint-handling Methodsmentioning

confidence: 99%

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

Liu

Odaka

Kuroiwa

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYA new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding highquality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems. key words: artificial fish swarm algorithm, multiple knapsack problem, constraint boundary, search region

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Section: Constraint-handling Methodsmentioning

confidence: 99%

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

Liu

Odaka

Kuroiwa

et al. 2014

IEICE Trans. Inf. & Syst.

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“…Orvosh and Davis [52] evaluate the performance of both techniques in GA algorithms in terms of returning the repaired or the original chromosome to populations. In summary, there are no standard heuristics for designing a repair algorithm [53], and a repair method is usually problem-dependent [54].…”

Section: Related Workmentioning

confidence: 99%

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Liu

Jin

Price

2018

Int J Adv Manuf Technol

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Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today's mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. The maximum completion time of the new order

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“…A generalized repair method proposed in [26] involves the first order development of the constraint violation vector V, according to x, which represents a tiny variation in the optimisation variables x:…”

Section: Other Techniquesmentioning

confidence: 99%

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Ponsich

Azzaro‐Pantel

Domenech

et al. 2008

Chemical Engineering and Processing: Process Intensification

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Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming (MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.

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Constraint handling in genetic algorithms using a gradient-based repair method

Cited by 210 publications

References 12 publications

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

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