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Subject: search.filters.subject.Dragonfly algorithm

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    AI-driven optimization of Congo red photo degradation using the spinel CdCr₂O₄ photocatalyst: From sol-gel synthesis to DT_LSBOOST predictive modeling coupled with the dragonfly algorithm
    (Elsevier, 2025) Chelabi, Kahina; Bouallouche, Rachida; Nasrallah, Noureddine; Boudraa, Reguia; Merzeg, Farid Ait; Djermoune, Atmane; Amrane, Abdeltif; Tahraoui, Hichem
    In this study, a nanostructure CdCr₂O₄ spinel photocatalyst was successfully synthesized via a low-cost sol–gel combustion route and thoroughly characterized by XRD, TGA-DTA, SEM-EDS, FTIR, and UV–Vis spectroscopy. The catalyst exhibited a well-defined spinel structure, high crystallinity, and nanometric grain size (∼29 nm), with strong visible-light absorption (band gap ≈ 1.97 eV). Photocatalytic performance was evaluated using Congo red (CR) as a model pollutant under visible LED light. Optimal degradation conditions (pH 6, [CR] ₀ = 10 mg/L, 1 g/L catalyst, 150 min) led to an outstanding removal efficiency of 98.45 %, with a kinetic constant of 2.11 × 10−2 min−1. Mechanistic studies revealed that hydroxyl (•OH) and superoxide (•O₂−) radicals played dominant roles in the degradation process. To model and optimize the system, a hybrid machine learning approach combining Decision Tree with Least Squares Boosting (DT_LSBOOST), optimized using the Dragonfly algorithm, was implemented. The model demonstrated excellent prediction accuracy (R = 0.9998, RMSE = 0.66) and successfully identified optimal operating conditions with <1 % deviation from experimental results. Stability and reusability tests confirmed the photocatalyst retained >90 % efficiency after five successive cycles, with no significant structural degradation. Compared to state-of-the-art materials, CdCr₂O₄ proved highly competitive in visible-light-driven photocatalysis, establishing its suitability for advanced wastewater treatment applications
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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