Guided Local Search

Voudouris, Christos; Tsang, Edward; Alsheddy, Abdullah

doi:10.1007/978-1-4419-1665-5_11

Cited by 94 publications

(39 citation statements)

References 57 publications

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“…We shall distinguish metaheuristics that are essentially variants of local search and those that generate solutions using a mechanism that is not necessarily based on an explicit neighborhood structure. Among the first class, which we call neighborhood-based metaheuristics, are methods like simulated annealing (SA) [16,52], tabu search (TS) [36] or guided local search (GLS) [89]. The second class comprises metaheuristics like GRASP [30], ant colony optimization (ACO) [28], evolutionary and memetic algorithms [3,67,69], scatter search [35], variable neighborhood search (VNS) [38,68] and ILS.…”

Section: Relation To Other Metaheuristicsmentioning

confidence: 99%

Iterated Local Search: Framework and Applications

Lourenço

Martin

Stützle

2010

International Series in Operations Research &Amp; Management Science

374

262

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Section: Relation To Other Metaheuristicsmentioning

confidence: 99%

Iterated Local Search: Framework and Applications

Lourenço

Martin

Stützle

2010

International Series in Operations Research &Amp; Management Science

374

262

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“…In 1995, Voudouris and his colleagues [20] suggested GLS algorithm for searching in a searching space and NP-hard solution for the first time. In 2004, Vebster [21] presented it as a strong algorithm and called it GELS algorithm.…”

Section: Local Search Algorithms Following Gravitational Attractionmentioning

confidence: 99%

Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combination of Imperialist Competition Algorithm (ICA) and Gravitational Emulation Local Search (GELS)

Alizadeh¹,

Harounabadi²,

Sadeghzadeh³

2016

IJCATR

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Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve scheduling. In this paper, a combination of imperialist competition algorithm (ICA) and gravitational attraction is used for to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms.

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“…Guided local search (GLS) is a metaheuristic which was designed for constraint satisfaction and combinatorial optimisation problems [26]. Like tabu search, GLS makes use of information gathered during the search to guide it and enable it to escape locally optimal regions rather than cycling between a few locally good solutions.…”

Section: Guided Local Searchmentioning

confidence: 99%

Tabu assisted guided local search approaches for freight service network design

Bai

Kendall

et al. 2012

Information Sciences

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The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the characteristics of the corresponding services, subject to various constraints. The scale of the problem in real-world applications is usually very large, especially when the network contains both the geographical information and the temporal constraints which are necessary for modelling multiple service-classes and dynamic events. The development of time-efficient algorithms for this problem is, therefore, crucial for successful real-world applications. Earlier research indicated that guided local search (GLS) was a promising solution method for this problem. One of the advantages of GLS is that it makes use of both the information collected during the search as well as any special structures which are present in solutions. Building upon earlier research, this paper carries out in-depth investigations into several mechanisms that could potentially speed up the GLS algorithm for the SNDP. Specifically, the mechanisms that we have looked at in this paper include a tabu list (as used by tabu search), short-term memory, and an aspiration criterion. An efficient hybrid algorithm for the SNDP is then proposed, based upon the results of these experiments. The algorithm combines a tabu list within a multi-start GLS approach, with an efficient feasibility-repairing heuristic. Experimental tests on a set of 24 well-known service network design benchmark instances have shown that the proposed algorithm is superior to a previously proposed tabu search method, reducing the computation time by over a third. In addition, we also show that far better results can be obtained when a faster linear program solver * Corresponding author is adopted for the sub-problem solution. The contribution of this paper is an efficient algorithm, along with detailed analyses of effective mechanisms which can help to increase the speed of the GLS algorithm for the SNDP.

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Guided Local Search

Cited by 94 publications

References 57 publications

Iterated Local Search: Framework and Applications

Iterated Local Search: Framework and Applications

Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combination of Imperialist Competition Algorithm (ICA) and Gravitational Emulation Local Search (GELS)

Tabu assisted guided local search approaches for freight service network design

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