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Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

Mostepha Redouane 1 El-Ghazali Talbi 1 Laetitia Jourdan 1 Briseida Sarasola 2 Enrique Alba 2
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.
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Submitted on : Friday, March 29, 2013 - 4:24:35 PM
Last modification on : Thursday, January 20, 2022 - 5:27:51 PM

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Mostepha Redouane, El-Ghazali Talbi, Laetitia Jourdan, Briseida Sarasola, Enrique Alba. Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems. Journal of Supercomputing, Springer Verlag, 2013, 63 (3), pp.836-853. ⟨10.1007/s11227-012-0774-x⟩. ⟨hal-00806249⟩



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