A comprehensive survey of Crow Search Algorithm and its applications
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A comprehensive survey of Crow Search Algorithm and its applications Yassine Meraihi1 · Asma Benmessaoud Gabis2 · Amar Ramdane‑Cherif3 · Dalila Acheli4
© Springer Nature B.V. 2020
Abstract Crow Search Algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows for hiding food. It has been widely used to solve a large variety of optimization problems in several fields and areas of research and has proved its efficiency compared to several state-of-the-art optimization algorithms available in the literature. This paper presents a comprehensive overview of Crow Search Algorithm and its new variants categorized into modified and hybridized versions. It also describes the several applications of CSA in various domains such as feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems. In addition, the paper suggests some interesting research areas related to CSA enhancement, CSA hybridization, and possible new applications. Keywords Crow Search Algorithm · Optimization · Nature-inspired algorithm · Swarm intelligence · Meta-heuristics
* Yassine Meraihi y.meraihi@univ‑boumerdes.dz Asma Benmessaoud Gabis [email protected] Amar Ramdane‑Cherif [email protected] Dalila Acheli d.acheli@univ‑boumerdes.dz 1
LIST Laboratory, University of M’Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
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Laboratoire de Méthodes de Conception de Systèmes, Ecole nationale Supérieure d’Informatique, Algiers, Algeria
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LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10‑12 Avenue of Europe, 78140 Velizy, France
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Automation Department, University of M’Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
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1 Introduction In recent years, nature-inspired (NI) meta-heuristic algorithms have been used successfully to solve a wide variety of optimization problems such as scheduling, image segmentation, feature selection, economic load dispatch, and many other engineering applications. As shown in Fig. 1, these optimization algorithms can be classified into four main
Fig. 1 Classification of nature-inspired meta-heuristic algorithms
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A comprehensive survey of Crow Search Algorithm and its…
classes: evolutionary-based, physics-based, human-based, and swarm intelligence-based algorithms. The first class of nature-inspired algorithms includes Evolutionary Algorithms (EA) that imitate the evolutionary behaviors of creatures in nature using the operators of selection, crossover, mutation, and reproduction to find better candidate solutions. Genetic Algorithm (GA) (Holland 1992) is regarded as the best evolutionary algorithm. Some other popular evolutionary algorithms are: Differential Evolution (DE) (Storn and Price 1997), Evolutionary Programming (EP) (Yao et al. 1999), Evolution Strategy (ES) (Beyer and Schwefel 2002), Genetic Programming (GP) (Koza 1997), Probability-Based Incremental Learning (PBIL
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