A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs

Alba, Enrique; Dorronsoro, Bernabè; Luna, Francisco; Nebro, Antonio J.; Bouvry, Pascal; Hogie, Luc

doi:10.1016/j.comcom.2006.08.033

Cited by 78 publications

(36 citation statements)

References 13 publications

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“…Thus, this model allows a flexible population size, combining the ideas of cellular and spatially distributed populations. Finally, Alba et al proposed in [12] cMOGA, the unique cellular multiobjective algorithm based on the canonical cGA model before this work, at the best of our known. In that work, cMOGA was used for optimizing a broadcasting strategy specifically designed for mobile ad hoc networks.…”

Section: Introductionmentioning

confidence: 99%

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MOCell: A cellular genetic algorithm for multiobjective optimization

Nebro

Durillo

Luna

et al. 2009

Int. J. Intell. Syst.

261

156

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Abstract. This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-theart evolutionary multiobjective optimizers. For the used benchmark, preliminary experiments indicate that MOCell obtains competitive results in terms of convergence, and it clearly outperforms the other two compared algorithms concerning the diversity of solutions along the Pareto front.

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Section: Introductionmentioning

confidence: 99%

“…Our proposal is called MOCell, and it is, like in the case of [12], an adaptation of a canonical cGA to the multiobjective field. MOCell uses an external archive to store the non-dominated solutions found during the execution of the algorithm, like many other multiobjective evolutionary algorithms do (e.g., PAES, SPEA2, or cMOGA).…”

Section: Introductionmentioning

confidence: 99%

MOCell: A cellular genetic algorithm for multiobjective optimization

Nebro

Durillo

Luna

et al. 2009

Int. J. Intell. Syst.

261

156

View full text Add to dashboard Cite

show abstract

“…These metaheuristics are: NSGA-II [4] and SPEA2 [5] (genetic algorithms), MOPSO [6] (particle swarm optimization), AbYSS [7] (scatter search), cMOGA [8] (cellular genetic algorithm), and two adaptations of Evolution Strategies (ES) [9] and Differential Evolution (DE) [10] to the multiobjective optimization field. We have been able to address this extensive study on such a complex problem thanks to the collaboration developed among the different research teams of the OPLINK project (http://oplink.lcc.uma.es).…”

Section: Fig 1 An Example Of Metropolitan Manetmentioning

confidence: 99%

“…We have configured the simulator for modelling a mall environment according to three main parameters: the size of the simulation area, the density of mobile stations, and the type of environment. For our experiments, we have used a simulation area of 40,000 square meters, a density of 2,000 devices per square kilometer, and a mall-like environment (see [8] for further details). We consider that the broadcasting is completed when either the coverage reaches 100% or it does not vary in a reasonable period of time (set to 1.5 seconds after some preliminary experimentation).…”

Section: Dfcnt: Problem Statementmentioning

confidence: 99%

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Metaheuristic Approaches for Optimal Broadcasting Design in Metropolitan MANETs

Alba

Cervantes

Gómez

et al.

Computer Aided Systems Theory – EUROCAST 2007

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Abstract. Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.

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