Publications Scientifiques
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Item Grasshopper Optimization Algorithm: Theory, Variants, and Applications(IEEE, 2021) Meraihi, Yassine; Benmessaoud Gabis, Asma; Seyedali, Mirjalili; Amar Ramdane, CherifGrasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.Item Dragonfly algorithm: a comprehensive review and applications(Springer, 2020) Meraihi, Yassine; Ramdane-Cherif, Amar; Acheli, Dalila; Mahseur, MohammedDragonfly 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 worksItem A cooperative learning strategy with multiple search mechanisms for improved artificial bee colony optimization(IEEE, 2015) Harfouchi, Fatima; Habbi, HaceneArtificial bee colony (ABC) optimization is a swarm based stochastic search strategy inspired by the foraging behavior of honeybees. Due to its simplicity and promising optimization capability, the ABC concept has devoted special interest with an increasing number of applications to scientific and engineering optimization problems. As an open research field, many researchers attempted to improve the performance of ABC algorithm through new algorithmic frameworks or by introducing modifications on the basic model. This paper presents an improved version of ABC algorithm based on a cooperative learning strategy with modified search mechanisms incorporated at both employed and onlooker levels. The proposed approach referred to as CLABC (Cooperative learning ABC) is tested on benchmark functions for numerical optimization. The results demonstrate the good performance and convergence of the proposed algorithm over other existing ABC variantsItem A cooperative learning artificial bee colony algorithm with multiple search mechanisms(IOS Press, 2016) Harfouchi, Fatima; Habbi, HaceneItem Modified multiple search cooperative foraging strategy for improved artificial bee colony optimization with robustness analysis(Springer, 2017) Harfouchi, Fatima; Habbi, H.; Ozturk, C.; Karaboga, D.