2007
DOI: 10.1016/j.comcom.2007.04.018
|View full text |Cite
|
Sign up to set email alerts
|

A hybrid grouping genetic algorithm for the registration area planning problem

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
27
0

Year Published

2011
2011
2016
2016

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 41 publications
(27 citation statements)
references
References 18 publications
0
27
0
Order By: Relevance
“…In order to define the problem formally, we have followed the notational conventions used in [38,19]. Suppose page i is the average number of incoming calls to cell i per unit of time and PB is the maximum number of incoming calls permitted to any RA per unit of time, i.e., PB is the paging bound.…”
Section: Problem Definitionmentioning
confidence: 99%
See 1 more Smart Citation
“…In order to define the problem formally, we have followed the notational conventions used in [38,19]. Suppose page i is the average number of incoming calls to cell i per unit of time and PB is the maximum number of incoming calls permitted to any RA per unit of time, i.e., PB is the paging bound.…”
Section: Problem Definitionmentioning
confidence: 99%
“…This version will be referred to as GGA. HGGA is the best approach designed so far for the RAP problem followed by GGA and GGARAP [19].…”
mentioning
confidence: 99%
“…The grouping genetic algorithm was introduced by Falkenauer in Falkenauer (1992), as a robust algorithm to manage problems in which items must be assigned to different groups. Since then, this approach has been successfully applied to a large amount of assignment and location problems (Agustín-Blas, Salcedo-Sanz, Ortiz-García, Portilla-Figueras, & Pérez-Bellido, 2009;Brown & Vroblefski, 2004, 2005De Lit, Falkenauer, & Delchambre, 2000;Falkenauer, 1992Falkenauer, , 1998Hung, Sumichrast, & Brown, 2003;James, Brown, & Keeling, 2007a, James, Vroblefski, & Nottingham, 2007b, in very different application fields. In the present case, the grouping genetic algorithm is hybridized with a repairing procedure to ensure finding feasible solutions, and with a local search to improve its performance for the case of the WiFiDP.…”
Section: Introductionmentioning
confidence: 98%
“…Similarly, in [29] a group-based differential mutation is used to remove the elements of an individual that have the same encoding as other given individual from its groups, and then reassign the free elements through a repair heuristic. In [19] and [20] there are no mutation operators, instead local-search operators are used (that can be seen as a mutation operator embedded in a search technique), respectively a hill climbing technique and a Tabu Search, where in both of them an informed operator tries to swap elements between groups.…”
Section: Grouping Problem Approachmentioning
confidence: 99%
“…This has led us to design nine mutation operators. We have also designed a grouping crossover to work over the group-number encoding representation, following the common structure of a crossover in a GGA, as described in [10] and used in many GGA applications [8,19,20,26,29,31]. This operator chooses markets from one parent to copy over the other parent and then uses a repair heuristic (in our case the SHA routine).…”
Section: Group-based Operatorsmentioning
confidence: 99%