Authors:
Ons Abdelkhalek
1
;
Saoussen Krichen
2
and
Adel Guitouni
3
Affiliations:
1
University of Tunis, Tunisia
;
2
University of Jendouba, Tunisia
;
3
University of Victoria, Canada
Keyword(s):
Heterogeneous Network Management, Tabu Search Algorithm, Multi-Objective Optimization Problem, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Decision Support Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mathematical Modeling
;
Mathematical Programming
;
Methodologies and Technologies
;
Network Optimization
;
Operational Research
;
Optimization
;
Pattern Recognition
;
Simulation
;
Software Engineering
;
Symbolic Systems
Abstract:
The Multi--objective Node Placement (MONP) problem focuses on extending an existing communication infrastructure with new wireless heterogeneous network components while achieving cost effectiveness and ease of management. This extention aims to broaden the coverage and handle demand fluctuations. In this paper, the MONP problem is modeled as a multi--objective optimization problem with three objectives: maximizing the communication coverage, minimizing active nodes and communication devices costs and maximizing of the total capacity bandwidth in the network. As the MONP problem is ${\cal NP}$--Hard, we present a meta--heuristic based on the Tabu Search approach specifically designed for multi--objective problems in wireless networks. An empirical validation of the model is defined based on a selection of a real and large set of instances and supported by a performance comparison between the suggested algorithm and a multi--objective genetic algorithm (MOGA). All tests are performed
on a real simulation environment for the maritime surveillance application. We show empirically that the proposed approach is more relevant to solve the MONP problem regarding each objective in term of cardinality-based performance index.
(More)