A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem

Angel, Éric; Bampis, Evripidis; Gourvès, Laurent

doi:10.1007/978-3-642-17144-4_6

Cited by 42 publications

(72 citation statements)

References 14 publications

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“…Secondly, despite the fact that the small instances of the single-objective TSP can be solved in a few seconds to optimality by exact algorithms such as concorde (http://www.tsp.gatech.edu/concorde), there are two facts that limit their use under fixed time constraints: the typically large variability in the computation times and the potentially very large number of solutions in the efficient set [15]. Thirdly, significant research efforts have been targeted towards applying SLS algorithms to this problem and it has been studied from several different perspectives: from an approximation [17,18], local search [19][20][21], theoretical [15] and experimental [22] point of view; some related problems have also been studied in the literature [23,24].…”

Section: Multiobjective Optimization and The Mtspmentioning

confidence: 99%

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Paquete

Stützle

2009

Computers & Operations Research

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Stochastic local search (SLS) algorithms are typically composed of a number of different components, each of which should contribute significantly to the final algorithm's performance. If the goal is to design and engineer effective SLS algorithms, the algorithm developer requires some insight into the importance and the behavior of possible algorithmic components. In this paper, we analyze algorithmic components of SLS algorithms for the multiobjective travelling salesman problem. The analysis is done using a careful experimental design for a generic class of SLS algorithms for multiobjective combinatorial optimization. Based on the insights gained, we engineer SLS algorithms for this problem. Experimental results show that these SLS algorithms, despite their conceptual simplicity, outperform a well-known memetic algorithm for a range of benchmark instances with two and three objectives.

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Section: Multiobjective Optimization and The Mtspmentioning

confidence: 99%

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Paquete

Stützle

2009

Computers & Operations Research

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“…PLS resembles some other approaches to MOCO such as BLS (Angel et al, 2004b), SEMO and FEMO (Laumanns et al, 2002), PAES (Knowles and Corne, 1999), the local search algorithm mentioned in (Ehrgott and Gandibleux, 2004), as well as the post-processing steps applied in (Hamacher and Ruhe, 1994;Jörnsten et al, 1996;Talbi, 2003). This section shows that PLS examines a polynomial size subset of neighbors at each iteration and that it outputs a maximal Pareto local optimum set.…”

Section: Soundness and Completeness Of Pareto Local Searchmentioning

confidence: 96%

“…One common approach to solve MOCO problems is to use the (weak) componentwise ordering when comparing neighboring solutions (Knowles and Corne, 2000;Angel et al, 2004b;Paquete et al, 2004). In that case, the acceptance criterion is to accept a neighbor if it is non (weakly) dominated; since in the local search process we can expect to have more than one solution for which this holds, an additional data structure, which is usually called archive, is used to maintain the current set of solutions.…”

Section: A Basic Iterative Improvement Algorithm For Mocomentioning

confidence: 99%

On local optima in multiobjective combinatorial optimization problems

2007

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Abstract. In this article, local optimality in multiobjective combinatorial optimization is used as a baseline for the design and analysis of two iterative improvement algorithms. Both algorithms search in a neighborhood that is defined on a collection of sets of feasible solutions and their acceptance criterion is based on outperformance relations. Proofs of the soundness and completeness of these algorithms are given.

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“…For instance, [24] has extended simulating annealing algorithm, [13] did the same for tabu search algorithm and [2,22] extended the steepest descent algorithm to its multi-objective version. Moreover, one field that has been very active during the last two decades is the evolutionary multi-objective optimisation (EMO).…”

Section: Multi-objective Local Searchmentioning

confidence: 99%

“…In this paper we implement the Pareto local search proposed by [2]. This method is deterministic, i.e.…”

Section: Multi-objective Local Searchmentioning

confidence: 99%

Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

Lagos¹,

Moreno-Vega²,

Guerrero³

et al. 2016

SIC

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Problems in operations research are usually modelled as single objective ones even though there exist several goals that should be attained. Multiple reasons of why inherently multi-objective problems are modelled as single objective can be identified: better understanding of the problem features, simplification of the mathematical formulation of the problem, among others. However, summarising (usually conflicting) objectives into one objective function can be a sign of over-simplification in the problem modelling process. In this paper we extend a single objective inventory location problem formulation to a multi-objective one. We consider two conflicting objectives, namely, location cost and inventory cost. As a result, we obtain a complex multi-objective non-linear integer programming problem. Like its single objective formulation, this multi-objective problem cannot be solved by exact methods as the number of decision variables increases. Thus, a simple yet effective multi-objective local search algorithm is implemented in this paper.

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A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem

Cited by 42 publications

References 14 publications

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

On local optima in multiobjective combinatorial optimization problems

Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

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