Local Search Methods for the Optimal Winner Determination Problem in Combinatorial Auctions

Boughaci, Dalila; Benhamou, Belaïd; Drias, Habiba

doi:10.1007/s10852-010-9127-z

Cited by 29 publications

(14 citation statements)

References 10 publications

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“…Given that there are 500 instances, we show only some results of each group, like in some recent papers [4]. For each presented instance, the following computational statistics are indicated: the maximum revenue obtained by the TSX_WDP algorithm over the 40 independent trials (Rbest), the average revenue over the 40 trials (Ravg), the worst revenue over the 40 trials (Rworst) and the average CPU time in seconds (AvgTime).…”

Section: Resultsmentioning

confidence: 99%

“…In order to further show the effectiveness of the TSX_WDP algorithm, we present a comparative study with five state of the art algorithms from the literature: Casanova [10], SAGII [8], SLS [3], TS [3], MA [4].…”

Section: Comparative Results For the Wdpmentioning

confidence: 99%

“…On the other hand, several stochastic methods were proposed for the WDP. They include a local search method named Casanova [10], a hybrid algorithm combining simulated annealing with Branch-and-Bound (SAGII) [8], and more recently a tabu search method [3] and a memetic algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

Sghir¹,

Hao²,

Jaâfar³

et al. 2014

Lecture Notes in Computer Science

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Abstract. We propose a dedicated tabu search algorithm (TSX_WDP) for the winner determination problem (WDP) in combinatorial auctions. TSX_WDP integrates two complementary neighborhoods designed respectively for intensification and diversification. To escape deep local optima, TSX_WDP employs a backbone-based recombination operator to generate new starting points for tabu search and to displace the search into unexplored promising regions. The recombination operator operates on elite solutions previously found which are recorded in an global archive. The performance of our algorithm is assessed on a set of 500 well-known WDP benchmark instances. Comparisons with five state of the art algorithms demonstrate the effectiveness of our approach.

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Section: Resultsmentioning

confidence: 99%

Section: Comparative Results For the Wdpmentioning

confidence: 99%

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A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

Sghir¹,

Hao²,

Jaâfar³

et al. 2014

Lecture Notes in Computer Science

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“…SLS is a local search meta-heuristic which has been already studied for several optimization problem such as satisfiability and optimal winner determination problem (WDP) in combinatorial auctions [8,9]. SLS starts with an initial solution generated randomly.…”

Section: Stochastic Local Search Methodsmentioning

confidence: 99%

Three local search-based methods for feature selection in credit scoring

Boughaci

Alkhawaldeh

2018

Vietnam J Comput Sci

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Credit scoring is a crucial problem in both finance and banking. In this paper, we tackle credit scoring as a classification problem where three local search-based methods are studied for feature selection. The feature selection is an interesting technique that can be launched before the data classification task. It permits to keep only the relevant variables and eliminate the redundant ones which enhances the classification accuracy. We study the local search method (LS), the stochastic local search method (SLS) and the variable neighborhood search method (VNS) for feature selection. Then, we combine these methods with the support vector machine (SVM) classifier to find the best described model from a dataset with the correct class variable. The proposed methods (LS+SVM, SLS+SVM and VNS+SVM) are evaluated on both German and Australian credit datasets and compared with some well-known classifiers. The numerical results are promising and show a good performance in favor of our methods.

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“…In general, the inexact methods are based on heuristics or metaheuristics and they are helpful for finding model of very large instances. The current well-known inexact algorithms for the WDP are: Hybrid Simulated Annealing SAGII [12,13], Casanova [15], stochastic local search [5,3] and memetic algorithms [4].…”

Section: Review Of Related Workmentioning

confidence: 99%

Metaheuristic Approaches for the Winner Determination Problem in Combinatorial Auction

Boughaci

2013

Studies in Computational Intelligence

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Abstract. Many problems in combinatorial optimization are NP-Hard. This has forced researchers to explore meta-heuristic techniques for dealing with this class of complex problems and finding an acceptable solution in reasonable time.In this chapter, we are interested in the winner determination problem (WDP) in combinatorial auction (CA). CA is an auction that allocates a set of many goods to bidders in the presence of substitutes and complements. The winner determination problem is a complex problem. It is the problem of finding winning bids that maximize the auctioneers revenue under the constraint that each good can be allocated to at most one bidder.This chapter presents a computational experience regarding four well-known meta-heuristics (stochastic local search, tabu search, genetic algorithms and memetic algorithms) for solving the winner determination problem (WDP). The purpose of this study is to evaluatethe performance of each one of the different techniques to solve the WDP in combinatorial auctions. The different methods are evaluated on various benchmark problems, and compared with the hybrid simulated annealing (SAGII) and Casanova. The computational experiments show that in general the metaheuristic approaches provide competitive results and find good quality solutions.

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Local Search Methods for the Optimal Winner Determination Problem in Combinatorial Auctions

Cited by 29 publications

References 10 publications

A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

Three local search-based methods for feature selection in credit scoring

Metaheuristic Approaches for the Winner Determination Problem in Combinatorial Auction

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