A Scatter Search Algorithm for the Maximum Clique Problem

Cavique, Luís; Rego, César; Themido, Isabel H.

doi:10.1007/978-1-4615-1507-4_11

Cited by 18 publications

(14 citation statements)

References 14 publications

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“…There are other knowledge-based meta-heuristics as expert systems and neural networks that have been used for store performance forecasting (Coates, Doherty, French and Kirkup, 1995) and spatial analysis (Murnion, 1996). Other promising algorithms as hybrid heuristics could also be used (Cavique, Rego and Themido, 2002), but applications are scarce, maybe because they involve very high technical expertise and data requirements, and so, high costs.…”

Section: Genetic Algorithms In the Optimal Retail Location Problemmentioning

confidence: 99%

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Multi‐outlet retail site location assessment

Mendes

Themido²

2003

Int Trans Operational Res

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ABSTRACT:One of the most important decisions a retailer can make is where to locate a retail outlet. Because convenience is so important to today's consumers, a retail store can prosper or fail solely based on its location. Recently, a changing retail environment is augmenting the location importance as retail economic groups develop multi outlet chains of small stores.The methods used in the development and calibration of location models for commercial spaces and sales forecast are multiple, varying from simple analogy forecast models to very complex spatial interactions models, which may incorporate dependence models in a gravitational or logit structure and many exploratory variables. More recent developments incorporating meta-heuristics such as genetic algorithms for the global problem of the multi outlet chain configuration, or the use of Voronoi diagrams in store trade area delimitation, are also presented. Finally, the Geographical Information Systems' role on the decision support process is equally explored.

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Section: Genetic Algorithms In the Optimal Retail Location Problemmentioning

confidence: 99%

“…Other promissing algorithms as hybrid heuristics could also be used (Cavique et al, 2002), but applications are scarce maybe because they involve very high technical expertise and data requirements, and so, high costs.…”

Section: Genetic Algorithms In the Optimal Retail Location Problemmentioning

confidence: 99%

Multi‐outlet retail site location assessment

Mendes

Themido²

2003

Int Trans Operational Res

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“…Their metaheuristic is capable of finding the best known solution in almost all the DIMACS graphs in reasonable running time. Other recent (meta)heuristics include GRASP [1,12], scatter search [8], ant colony optimization [11], incomplete dynamic backtracking approach [29], annealed replication heuristic [5], continuous optimization heuristics [7], and neural network [13]. These have been proposed for the MCP or strongly related problems, i.e., the maximum independent (stable) set problem and the minimum vertex cover problem.…”

Section: Maximum Clique Problemmentioning

confidence: 99%

An effective local search for the maximum clique problem

Katayama

Akihiro

Narihisa

2005

Information Processing Letters

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“…In Cavique et al (2002) the Primal-Tabu, the DualTabu, and the Primal-Dual-Tabu algorithms are presented to solve de maximum clique problem. The Primal-Tabu is an extension of the algorithm developed by Soriano and Gendreau (1996).…”

Section: Article In Pressmentioning

confidence: 99%

A scalable algorithm for the market basket analysis

Cavique

2007

Journal of Retailing and Consumer Services

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The market basket is defined as an itemset bought together by a customer on a single visit to a store. The market basket analysis is a powerful tool for the implementation of cross-selling strategies. Especially in retailing, it is essential to discover large baskets, since it deals with thousands of items. Although some algorithms can find large itemsets, they can be inefficient in terms of computational time. The aim of this paper is to present an algorithm to discover large itemset patterns for the market basket analysis. In this approach, the condensed data are used and is obtained by transforming the market basket problem into a maximum-weighted clique problem. Firstly, the input data set is transformed into a graph-based structure and then the maximum-weighted clique problem is solved using a metaheuristic approach in order to find the most frequent itemsets. The computational results show large itemset patterns with good scalability properties. r

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A Scatter Search Algorithm for the Maximum Clique Problem

Cited by 18 publications

References 14 publications

Multi‐outlet retail site location assessment

Multi‐outlet retail site location assessment

An effective local search for the maximum clique problem

A scalable algorithm for the market basket analysis

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