Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Meraihi, YassineSubject: search.filters.subject.Dragonfly algorithm

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    Solving graph coloring problem using an enhanced binary dragonfly algorithm
    (2019) Baiche, Karim; Meraihi, Yassine; Hina, Manolo Dulva; Ramdane-Cherif, Amar; Mahseur, Mohammed
    The graph coloring problem (GCP) is one of the most interesting classical combinatorial optimization problems in graph theory. It is known to be an NP-Hard problem, so many heuristic algorithms have been employed to solve this problem. In this article, the authors propose a new enhanced binary dragonfly algorithm to solve the graph coloring problem. The binary dragonfly algorithm has been enhanced by introducing two modifications. First, the authors use the Gaussian distribution random selection method for choosing the right value of the inertia weight w used to update the step vector (∆X). Second, the authors adopt chaotic maps to determine the random parameters s, a, c, f, and e. The aim of these modifications is to improve the performance and the efficiency of the binary dragonfly algorithm and ensure the diversity of solutions. The authors consider the well-known DIMACS benchmark graph coloring instances to evaluate the performance of their algorithm. The simulation results reveal the effectiveness and the successfulness of the proposed algorithm in comparison with some well-known algorithms in the literature
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    Dragonfly algorithm: a comprehensive review and applications
    (Springer, 2020) Meraihi, Yassine; Ramdane-Cherif, Amar; Acheli, Dalila; Mahseur, Mohammed
    Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic andstatic swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared toseveral well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and itsnew variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in severalfields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the papersuggests some possible interesting research on the applications and hybridizations of DA for future works