A population-based iterated greedy algorithm for the minimum weight vertex cover problem

Bouamama, Salim; Blum, Christian; Boukerram, Abdellah

doi:10.1016/j.asoc.2012.02.013

Cited by 69 publications

(23 citation statements)

References 9 publications

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“…The IG algorithm is presented in Ruiz and Stützle [43], which has successful applications in discrete/combinatorial optimization problems such as in [7], [13], [15], [18], [24][25], [29], [35], [37], [41], [44], [49], [51], [53]. The IG algorithm is fascinating in terms of its conceptual simplicity, which makes it easily tunable and extendible to any combinatorial optimization problem.…”

Section: Iterated Greedy Algorithmmentioning

confidence: 99%

A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

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et al. 2013

Swarm, Evolutionary, and Memetic Computing

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Abstract. In this study, we propose a populated iterated greedy algorithm with an Inver-Over operator to solve the traveling salesman problem. The iterated greedy (IG) algorithm is mainly based on the central procedures of destruction and construction. The basic idea behind it is to remove some solution components from a current solution and reconstruct them in the partial solution to obtain the complete solution again. In this paper, we apply this idea in a populated manner (IGP) to the traveling salesman problem (TSP). Since the destruction and construction procedure is computationally expensive, we also propose an iteration jumping to an Inver-Over operator during the search process. We applied the proposed algorithm to the well-known 14 TSP instances from TSPLIB. The computational results show that the proposed algorithm is very competitive to the recent best performing algorithms from the literature.

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Section: Iterated Greedy Algorithmmentioning

confidence: 99%

A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

Taşgetiren

Büyükdağlı

Kızılay

et al. 2013

Swarm, Evolutionary, and Memetic Computing

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“…Our computational experiments are carried out on the standard benchmark instances provided by Bouamama et al (2012). Each instance consists of an undirected and vertex-weighted graph with n vertices and m edges.…”

Section: Benchmark Instances and Experimental Protocolsmentioning

confidence: 99%

“…Afterwards, Jovanovic and Tuba (2011) improved this algorithm by introducing a pheromone correction heuristic strategy that adopts information about the bestfound solution to exclude suspicious elements from it to avoid trapping into local optimum trap. Recently, Bouamama et al (2012) addressed the MWVCP with a population-based iterated greedy algorithm (PBIG) which maintains the solutions of the population and applies, at each iteration, the basic steps of an iterated greedy algorithm to each individual of the population.…”

Section: Introductionmentioning

confidence: 99%

Multi-start iterated tabu search for the minimum weight vertex cover problem

Zhou

Wang

et al. 2015

J Comb Optim

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The minimum weight vertex cover problem (MWVCP) is one of the most popular combinatorial optimization problems with various real-world applications. Given an undirected graph where each vertex is weighted, the MWVCP is to find a subset of the vertices which cover all edges of the graph and has a minimum total weight of these vertices. In this paper, we propose a multi-start iterated tabu search algorithm (MS-ITS) to tackle MWVCP. By incorporating an effective tabu search method, MS-ITS exhibits several distinguishing features, including a novel neighborhood construction procedure and a fast evaluation strategy. Extensive experiments on the set of public benchmark instances show that the proposed heuristic is very competitive with the state-of-the-art algorithms in the literature.

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“…Prime examples include those to various scheduling problems such as [11,6]. The first PBIG approach was proposed in the context of the minimum weight vertex cover problem in [1]. Later, PBIG was also applied to the delimitation and zoning of rural settlements [9] and, as mentioned above, to the minimum weight dominating set problem [2].…”

Section: Related Workmentioning

confidence: 99%

The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches

Pinacho-Davidson

Blum

Lozano

2017

Evolutionary Computation in Combinatorial Optimization

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This work deals with the so-called weighted independent domination problem, which is an N P -hard combinatorial optimization problem in graphs. In contrast to previous theoretical work from the literature, this paper considers the problem from an algorithmic perspective. The first contribution consists in the development of an integer linear programming model and a heuristic that makes use of this model. Second, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy algorithm that takes profit from the better one of the two developed greedy heuristics. The results of the compared algorithmic approaches show that small problem instances based on random graphs are best solved by an efficient integer linear programming solver such as CPLEX. Larger problem instances are best tackled by the population-based iterated greedy algorithm. The experimental evaluation considers random graphs of different sizes, densities, and ways of generating the node and edge weights.

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A population-based iterated greedy algorithm for the minimum weight vertex cover problem

Cited by 69 publications

References 9 publications

A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

Multi-start iterated tabu search for the minimum weight vertex cover problem

The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches

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