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Cooperation-based search of global optima

Abstract : A new cooperation-based metaheuristic is proposed for searching global optima of functions. It is based on the assumption that the dynamics of the objective function does not change significantly between iterations. 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 mono-objective 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 : Damien Vergnet Connect in order to contact the contributor
Submitted on : Friday, September 16, 2022 - 11:07:59 AM
Last modification on : Saturday, September 17, 2022 - 3:50:16 AM


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  • HAL Id : hal-03778834, version 1


Damien Vergnet, Elsy Kaddoum, Nicolas Verstaevel, Frederic Amblard. Cooperation-based search of global optima. 5th International Conference in Optimization and Learning (OLA 2022), Jul 2022, Syracuse, Italy. à paraître. ⟨hal-03778834⟩



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