SciELO - Scientific Electronic Library Online

 
vol.16 issue3Building General Hyper-Heuristics for Multi-Objective Cutting Stock ProblemEfficient Hybrid Grouping Heuristics for the Bin Packing Problem author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Abstract

BERNABE LORANCA, María Beatriz  and  GUILLEN GALVAN, Carlos. Multi-Objective Variable Neighborhood Search to Solve the Problem of Partitioning of Spatial Data with Population Characteristics. Comp. y Sist. [online]. 2012, vol.16, n.3, pp.335-347. ISSN 1405-5546.

The problem of partitioning is NP hard and has been studied extensively for several reasons including vulnerability to obtain local optima. For partitioning problems in combinatorial optimization, several works have proposed the inclusion of heuristics in order to achieve global optima. There have been made many efforts to solve the partitioning problem and find good solutions when the discrete optimization process optimizes a single objective. However, the partitioning problem with more than one goal has not been addressed due to the difficulty of obtaining the set of efficient optimal and non-dominated solutions. This paper presents the multi-objective partitioning problem with two objectives: minimization of distances and of census variables. The designed partitioning algorithm is an extension of the geographic cluster that optimizes only one objective. In this work, we used Variable Neighborhood Search (VNS) to escape local optima, and to obtain the set of non-dominated solutions, our methodology takes advantage of the properties of the set Maxima.

Keywords : Heuristics algorithms; Maxima; multi-objective partitioning.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License