Distributed Localized Bi-objective Search

Abstract : We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and adaptive search. Given a number of computing nodes, we self-coordinate them locally, in order to cooperatively search different regions of the Pareto front. This offers a trade-off between a fully independent approach, where each node would operate independently of the others, and a fully centralized approach, where a global knowledge of the entire population is required at every step. More specifically, the population of solutions is structured and mapped into computing nodes. As local information, every node uses only the positions of its neighbors in the objective space and evolves its local solution based on what we term a 'localized fitness function'. This has the effect of making the distributed search evolve, over all nodes, to a high quality approximation set, with minimum communications. We deploy our distributed algorithm using a computer cluster of hundreds of cores and study its properties and performance on rhoMNK-landscapes. Through extensive large-scale experiments, our approach is shown to be very effective in terms of approximation quality, computational time and scalability.
Type de document :
Article dans une revue
European Journal of Operational Research, Elsevier, 2014, 239, pp.731-743. 〈10.1016/j.ejor.2014.05.040〉
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Contributeur : Sébastien Verel <>
Soumis le : vendredi 6 juin 2014 - 11:51:17
Dernière modification le : jeudi 21 février 2019 - 11:02:54



Bilel Derbel, Jérémie Humeau, Arnaud Liefooghe, Sébastien Verel. Distributed Localized Bi-objective Search. European Journal of Operational Research, Elsevier, 2014, 239, pp.731-743. 〈10.1016/j.ejor.2014.05.040〉. 〈hal-01002520〉



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