Integration of ACO in a Constraint Programming Language

Khichane, Madjid; Albert, Patrick; Solnon, Christine

doi:10.1007/978-3-540-87527-7_8

Cited by 21 publications

(17 citation statements)

References 17 publications

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“…It is a novel structural search approach that achieved a sequence of resource limited tree searches where search start either from the vacant solution or from the solution that come across the search some no of selected solution is selected during the search [59]. Khichane et al (2008) proposed a hybrid algorithm that includes ACO and constraint programming to solve car sequencing problem. In this approach, the goal is to find any complete assignment satisfying all the constraints [76].…”

Section: Hybridizing Metaheuristic With Constraint Programmingmentioning

confidence: 99%

“…Khichane et al (2008) proposed a hybrid algorithm that includes ACO and constraint programming to solve car sequencing problem. In this approach, the goal is to find any complete assignment satisfying all the constraints [76]. Prestwich et al (2009) proposed the integration of evolutionary computation and neural network with constraint programming which result in a hybrid stochastic constraint programming structure that aims to find a policy tree which indicate the decision assignment at any possible situation [105].…”

Section: Hybridizing Metaheuristic With Constraint Programmingmentioning

confidence: 99%

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Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

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This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.

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Section: Hybridizing Metaheuristic With Constraint Programmingmentioning

confidence: 99%

Section: Hybridizing Metaheuristic With Constraint Programmingmentioning

confidence: 99%

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

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show abstract

“…Ce dernier, est un problème de référence utilisé au sein de la communauté PPC pour tester les performances et l'efficacité de leurs algorithmes. Cette première contribution à été publiée dans [KAS08a,KAS07,KAS08b,KAS08c].…”

Section: Intégration D'aco à Un Langage De Ppc Pour Résoudre Des Cspunclassified

Strong Combination of Ant Colony Optimization with Constraint Programming Optimization

Khichane

Albert²,

Solnon

2010

Lecture Notes in Computer Science

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“…Such approaches have been described first by Meyer and Ernst [84] and in some more detail in a book chapter [83]. More recently, the integration of ACO into constraint programming languages has been considered [66].…”

Section: Other Approachesmentioning

confidence: 99%

Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms

Dumitrescu

Stützle

2009

Matheuristics

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Exact mathematical programming techniques such as branch-andbound or dynamic programming and stochastic local search techniques have traditionally been seen as being two general but distinct approaches for the effective solution of combinatorial optimization problems, each having particular advantages and disadvantages. In several research efforts true hybrid algorithms, which exploit ideas from both fields, have been proposed. In this chapter we review some of the main ideas of several such combinations and illustrate them with examples from the literature. Our focus here is on algorithms that have the main framework given by the local search and use exact algorithms to solve subproblems.

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Integration of ACO in a Constraint Programming Language

Cited by 21 publications

References 17 publications

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Strong Combination of Ant Colony Optimization with Constraint Programming Optimization

Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms

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