Guided local search and its application to the traveling salesman problem

Voudouris, Christos; Tsang, Edward

doi:10.1016/s0377-2217(98)00099-x

Cited by 310 publications

(153 citation statements)

References 30 publications

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“…Alsheddy and Tsang [1] propose an extension of PLS that continues the search when a Pareto local optimum set is found without restarting from different solutions. The idea is based on the guided local search [64] strategy in the single-objective case: a penalty is applied to worsen the components of the objective vectors of solutions in the current archive, allowing the algorithm to escape from a Pareto local optimum set. Other strategies to continue the search focus on generating good solution(s) to restart the search.…”

Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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In this chapter, we review metaheuristics for solving multi-objective combinatorial optimization problems, when no information about the decision maker's preferences is available, that is, when problems are tackled in the sense of Pareto optimization. Most of these metaheuristics follow one of the two main paradigms to tackle such problems using metaheuristics. The first paradigm is to rely on Pareto dominance when exploring the search space. The second paradigm is to tackle several single-objective problems to find several solutions that are non-dominated for the original problem; in this case, one may exploit existing, efficient single-objective algorithms, but the performance depends on the definition of the set of scalarized problems. There are also a number of approaches in the literature that combine both paradigms. However, this is usually done in a relatively ad-hoc way. In this paper, we review two conceptually simple methods representative of each paradigm: Pareto local search and Two-phase Local Search. The hybridization of these two strategies provides a general framework for engineering stochastic local search algorithms that can be used to improve over the state-of-the-art for several, widely studied problems.

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Section: Dominance-based Multi-objective Optimizationmentioning

confidence: 99%

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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“…Guided Local Search (GLS) is an explorative metaheuristic based on penalties and was introduced in (Voudouris, 1997;Voudouris and Tsang, 1999). The Guided…”

Section: Guided Local Searchmentioning

confidence: 99%

“…This strategy is based on the definition of solution features, which may be any kind of properties or characteristics that can be used to discriminate between solutions (e.g. in travelling salesman problem they are the arcs between pairs of cities (Voudouris and Tsang, 1999)). An indicator function I i (s) is defined to show whether the feature i is present in a specific solution s, that is:…”

Section: Single-solution Metaheuristicsmentioning

confidence: 99%

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The development and application of metaheuristics for problems in graph theory: a computational study

Consoli¹

2011

4OR-Q J Oper Res

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“…Heuristic search algorithms such as Simulated Annealing [6), Tabu Search [7], Genetic Algorithms [8| or Guided Ijocal Search [9] employ strategies to escape such local extrema and to improve the generated solution even further. Although these advanced search methods can be easily incorporated in FieldPlan using the defined objective function and move operators, we use the more basic hill-climber due to computation time restrictions.…”

Section: Plan Optimisationmentioning

confidence: 99%

FieldPlan: Tactical Field Force Planning in BT

Kern

Anim-Ansah

Owusu

et al. 2006

IFIP International Federation for Information Processing

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Abstract. In a highly competitive market, BT' faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an Al-based planning system for the management of parts of BT's field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.

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Guided local search and its application to the traveling salesman problem

Cited by 310 publications

References 30 publications

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

The development and application of metaheuristics for problems in graph theory: a computational study

FieldPlan: Tactical Field Force Planning in BT

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