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Recherche coopérative d'optimum global

Abstract : This paper proposes a new cooperation-based metaheuristic for searching global optima of optimization functions. It relies on a local search process coupled with a cooperative semi-local search process. Its performances are compared against four other metaheuristics on unconstrained monoobjective optimization problems. Results show that the proposed metaheuristic is able to find the global minimum of the tested functions faster than the compared methods while reducing the number of iterations and the number of calls of the objective function.
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Contributor : Maxime Guériau Connect in order to contact the contributor
Submitted on : Wednesday, August 31, 2022 - 11:12:29 AM
Last modification on : Thursday, September 1, 2022 - 3:53:32 AM
Long-term archiving on: : Thursday, December 1, 2022 - 6:38:37 PM


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  • HAL Id : hal-03765420, version 1


Damien Vergnet, Elsy Kaddoum, Nicolas Verstaevel, Frederic Amblard. Recherche coopérative d'optimum global. 20èmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA 2022), Jun 2022, Saint-Etienne, France. pp.92-98. ⟨hal-03765420⟩



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