DocumentCode :
1781596
Title :
A comparison of multiple objective evolutionary algorithms for solving the multi-objective node placement problem
Author :
Masri, Hela ; Abdelkhalek, Ons ; Krichen, Saoussen
Author_Institution :
LARODEC Lab., Univ. of Tunis, Le Bardo, Tunisia
fYear :
2014
fDate :
3-5 Nov. 2014
Firstpage :
152
Lastpage :
157
Abstract :
The multi-objective node placement (MONP) problem involves the extension of an existing heterogeneous network while optimizing three conflicting objectives: maximizing the communication coverage, minimizing active nodes and communication devises costs, and maximizing of the total capacity bandwidth in the network. Multiple devices´ types are to be deployed in order to ensure networks´ heterogeneity. As the MONP problem is NP-Hard, heuristic approaches are necessary for large problem instances. In this paper, we compare the ability of three different sorting-based multiple objective genetic algorithms to find an optimal placement and connection between the potential placed nodes. The empirical validation is performed using a simulation environment called Inform Lab and based on real instances of maritime surveillance application. Results and discussion on the performance of the algorithms are provided.
Keywords :
computational complexity; genetic algorithms; Inform Lab; MONP problem; NP-hard problem; empirical validation; heuristic approach; maritime surveillance application; multiobjective node placement problem; multiple objective evolutionary algorithm; sorting-based multiple objective genetic algorithm; Bandwidth; Cascading style sheets; Evolutionary computation; Genetic algorithms; Mobile computing; Planning; Surveillance; Genetic algorithm; Multi-objective optimization; NSGA2; Node placement problem; PAES; SPEA2;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Decision and Information Technologies (CoDIT), 2014 International Conference on
Conference_Location :
Metz
Type :
conf
DOI :
10.1109/CoDIT.2014.6996885
Filename :
6996885
Link To Document :
بازگشت