A Statistical comparative analysis of Simulated Annealing and Variable Neighborhood Search for the Geographic Clustering Problem

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Bibliographic Details
Main Author:	Beatriz Bernábe Loranca
Format:	Artículo científico
Language:	en
Published:	Instituto Politécnico Nacional 2011
Subjects:	Computación Design Algorithms Heuristics Experimentation Geographic Clustering Problem
Online Access:	Acceder al recurso
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author	Beatriz Bernábe Loranca
author_facet	Beatriz Bernábe Loranca
contents	A Statistical comparative analysis of Simulated Annealing and Variable Neighborhood Search for the Geographic Clustering Problem Beatriz Bernábe Loranca José E. Espinosa Rosales Javier Ramírez Rodríguez María A. Osorio Lama Computación Design Algorithms Heuristics Experimentation Geographic Clustering Problem This paper describes a factorial statistical study that compares the quality of solutions produced by two heuristics: Simulated Annealing (SA) and Variable Neighborhood Search (VNS). These methods are used to solve the Geographic Clustering Problem (GCP), and the quality of the solutions produced for specific times has been compared. With the goal of comparing the quality of the solutions, where both heuristics participate in an impartial evaluation, time has been the only common element considered for VNS and SA. At this point, two factorial experiments were designed and the corresponding parameters for each heuristic were carefully modeled leaving time as the cost function. In instances of 24 objects, the experiments involved the execution of two sets of tests recording the results of the different response times and the associated values of the objective function for each heuristic and instance conditions. The solution to this problem requires a partitioning process where each group is composed of objects that fulfill better the objective: the minimum accumulated distance from the objects to the centroid of each group. The GCP is a combinatorial NP-hard problem (Bação, Lobo and Painho, 2004). 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520765008 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.14
format	Artículo científico
id	redalyc_61520765008
institution	Redalyc
language	en
publishDate	2011
publisher	Instituto Politécnico Nacional
spellingShingle	A Statistical comparative analysis of Simulated Annealing and Variable Neighborhood Search for the Geographic Clustering Problem Beatriz Bernábe Loranca Computación Design Algorithms Heuristics Experimentation Geographic Clustering Problem A Statistical comparative analysis of Simulated Annealing and Variable Neighborhood Search for the Geographic Clustering Problem Beatriz Bernábe Loranca José E. Espinosa Rosales Javier Ramírez Rodríguez María A. Osorio Lama Computación Design Algorithms Heuristics Experimentation Geographic Clustering Problem This paper describes a factorial statistical study that compares the quality of solutions produced by two heuristics: Simulated Annealing (SA) and Variable Neighborhood Search (VNS). These methods are used to solve the Geographic Clustering Problem (GCP), and the quality of the solutions produced for specific times has been compared. With the goal of comparing the quality of the solutions, where both heuristics participate in an impartial evaluation, time has been the only common element considered for VNS and SA. At this point, two factorial experiments were designed and the corresponding parameters for each heuristic were carefully modeled leaving time as the cost function. In instances of 24 objects, the experiments involved the execution of two sets of tests recording the results of the different response times and the associated values of the objective function for each heuristic and instance conditions. The solution to this problem requires a partitioning process where each group is composed of objects that fulfill better the objective: the minimum accumulated distance from the objects to the centroid of each group. The GCP is a combinatorial NP-hard problem (Bação, Lobo and Painho, 2004). 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520765008 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.14
title	A Statistical comparative analysis of Simulated Annealing and Variable Neighborhood Search for the Geographic Clustering Problem
topic	Computación Design Algorithms Heuristics Experimentation Geographic Clustering Problem
url	https://www.redalyc.org/articulo.oa?id=61520765008