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Perfectionnement de métaheuristiques pour l'optimisation continue

Abstract : Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems. Among representative metaheuristics, Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm. In this PhD thesis, both unconstrained and constrained global optimization problems in a continuous space are considered. New hybrid versions of BBO are proposed as promising solvers for the considered problems. The proposed methods aim to overcome the drawbacks of slow convergence and the lack of diversity of the BBO algorithm. In the first part of this thesis, we present the method we developed, based on an hybridization of BBO with the differential evolution (DE) algorithm, to solve unconstrained optimization problems. We show that the results of the proposed algorithm are more accurate, especially for multimodal problems, which are amongst the most difficult-to-handle class of problems for many optimization algorithms. To solve constrained optimization problems, we propose three new variations of BBO. Our extensive experimentations successfully demonstrate the usefulness of all these modifications proposed for the BBO algorithm. In the second part, we focus on the applications of the proposed algorithms to solve real-world optimization problems. We first address the problem of optimal power scheduling for the decentralized detection of a deterministic signal in a wireless sensor network, with power and bandwidth constrained distributed nodes. The objective is to minimize the total power spent by the whole sensor network while keeping the detection error probability below a required threshold. In a second time, image segmentation of gray-level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the fuzzy entropy. The optimization is conducted by a newly-developed BBO variants (DBBO-Fuzzy). We show the efficiency of the proposed method through experimental results
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Ilhem Boussaid. Perfectionnement de métaheuristiques pour l'optimisation continue. Autre. Université Paris-Est; Université des sciences et de la technologie Houari Boumediene (Alger ; 1974-..), 2013. Français. ⟨NNT : 2013PEST1075⟩. ⟨tel-00952774⟩

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