Breakout Local Search for maximum clique problems

Benlic, Una; Hao, Jin‐Kao

doi:10.1016/j.cor.2012.06.002

Cited by 83 publications

(72 citation statements)

References 26 publications

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“…To validate the generality of BLS, in addition to the MSCP presented in this paper, we have applied BLS to solve several other classical combinatorial optimization problems (quadratic assignment, maximum clique [2], and maximum cut) and observed very competitive performances on benchmark instances.…”

Section: Discussionmentioning

confidence: 99%

A Study of Breakout Local Search for the Minimum Sum Coloring Problem

Benlic

Hao

2012

Lecture Notes in Computer Science

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Abstract. Given an undirected graph G = (V, E), the minimum sum coloring problem (MSCP) is to find a legal assignment of colors (represented by natural numbers) to each vertex of G such that the total sum of the colors assigned to the vertices is minimized. In this paper, we present Breakout Local Search (BLS) for MSCP which combines some essential features of several well-established metaheuristics. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Tested on 27 commonly used benchmark instances, our algorithm shows competitive performance with respect to recently proposed heuristics and is able to find new record-breaking results for 4 instances.

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Section: Discussionmentioning

confidence: 99%

A Study of Breakout Local Search for the Minimum Sum Coloring Problem

Benlic

Hao

2012

Lecture Notes in Computer Science

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“…Nevertheless, TPS is distinguished by its local optima exploring phase and its perturbation mechanisms which share similarities with breakout local search (Benlic & Hao, 2013a;2013c;Fu & Hao, 2014). …”

Section: General Frameworkmentioning

confidence: 99%

“…The local optima exploring phase is based on two directed perturbation operators (denoted by Directed Perturb). Inspired by the idea of breakout local search (Benlic & Hao, 2013a;2013c), these directed perturbation operators rely on the tabu search principle (Glover & Laguna, 1997), which favors the moves with the weakest deterioration of the objective function. Precisely, Directed Perturb takes one of the following two forms.…”

Section: Local Optima Exploring Phasementioning

confidence: 99%

A three-phase search approach for the quadratic minimum spanning tree problem

Hao

2015

Engineering Applications of Artificial Intelligence

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Given an undirected graph with costs associated with each edge as well as each pair of edges, the quadratic minimum spanning tree problem (QMSTP) consists of determining a spanning tree of minimum total cost. This problem can be used to model many real-life network design applications, in which both routing and interference costs should be considered. For this problem, we propose a three-phase search approach named TPS, which integrates 1) a descent-based neighborhood search phase using two different move operators to reach a local optimum from a given starting solution, 2) a local optima exploring phase to discover nearby local optima within a given regional search area, and 3) a perturbation-based diversification phase to jump out of the current regional search area. Additionally, we introduce dedicated techniques to reduce the neighborhood to explore and streamline the neighborhood evaluations. Computational experiments based on hundreds of representative benchmarks show that TPS produces highly competitive results with respect to the best performing approaches in the literature by improving the best known results for 31 instances and matching the best known results for the remaining instances only except two cases. Critical elements of the proposed algorithms are analyzed.

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“…In this paper, we investigate the contribution of a new adaptive diversification strategy (ADS) employed by a recent breakout local search (BLS) metaheuristic [3,4,5,6]. BLS is a variation of iterated local search (ILS) [9] since it combines a descent-based local search with a perturbation mechanism.…”

Section: Introductionmentioning

confidence: 99%

A Study of Adaptive Perturbation Strategy for Iterated Local Search

Benlic¹,

Hao²

2013

Evolutionary Computation in Combinatorial Optimization

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Abstract. We investigate the contribution of a recently proposed adaptive diversification strategy (ADS) to the performance of an iterated local search (ILS) algorithm. ADS is used as a diversification mechanism by breakout local search (BLS), which is a new variant of the ILS metaheuristic. The proposed perturbation strategy adaptively selects between two types of perturbations (directed or random moves) of different intensities, depending on the current state of search. We experimentally evaluate the performance of ADS on the quadratic assignment problem (QAP) and the maximum clique problem (MAX-CLQ). Computational results accentuate the benefit of combining adaptively multiple perturbation types of different intensities. Moreover, we provide some guidance on when to introduce a weaker and when to introduce a stronger diversification into the search.

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Breakout Local Search for maximum clique problems

Cited by 83 publications

References 26 publications

A Study of Breakout Local Search for the Minimum Sum Coloring Problem

A Study of Breakout Local Search for the Minimum Sum Coloring Problem

A three-phase search approach for the quadratic minimum spanning tree problem

A Study of Adaptive Perturbation Strategy for Iterated Local Search

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