A comprehensive survey of Crow Search Algorithm and its applications

Meraihi, Yassine; Gabis, Asma Benmessaoud; Ramdane-Chérif, Amar; Acheli, Dalila

doi:10.1007/s10462-020-09911-9

Cited by 71 publications

(28 citation statements)

References 157 publications

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“…The Crow Search Algorithm (CSA) [47] was first proposed in 2016 to solve constrained engineering optimization problems. In [61], Meriahi et al published a new overview paper to present all modified version of CSA system. CSA has been extended in a way to solve MOPs as well.…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

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Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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“…Since cloud computing has made the transfer of data from a server to a client, servers, and networks simpler, there will always be threats and challenges on their third party servers in data centers or a privately owned cloud [19]. These challenges are including; security, scheduling, energy efficiency, reliability, lack of resources, and others [20]. In this review article, the task scheduling issues in cloud computing will be reviewed.…”

Section: Cloud Computing Main Challengesmentioning

confidence: 99%

“…New technology has become a significant influence on our lives. While people actively spend time optimizing their learning styles [20]. Business people often adapt to business conditions and shifts [56].…”

Section: Nature-inspired Algorithmsmentioning

confidence: 99%

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Yahia

Zeebaree

Sadeeq

et al. 2021

AJRCoS

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Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

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“…The two most popular SI algorithms are Particle Swarm Optimization (PSO) (Eberhart and Kennedy 1995;Kennedy 2010), and Ant Colony Optimization (ACO) (Dorigo and Di Caro 1999). Other techniques in this class are: Artificial Bee Colony (ABC) Algorithm (Karaboga and Basturk 2007), Cuckoo Search Algorithm (CS) (Yang and Deb 2009;Shehab et al 2017), Firefly Algorithm (FA) (Yang 2009;Fister et al 2013), Bat Algorithm (BA) (Yang 2010(Yang , 2013, Krill Herd (KH) (Gandomi and Alavi 2012;Wang et al 2019), Fruit Fly Optimization (FFO) algorithm (Pan 2012), Grey Wolf Optimizer (GWO) (Mirjalili et al 2014;Faris et al 2018;Hatta et al 2019), Elephant Search Algorithm (ESA) (Deb et al 2015), Ant Lion Optimizer (ALO) (Mirjalili 2015a;Abualigah et al 2020), Moth-Flame Optimization (MFO) Algorithm (Mirjalili 2015b;Hussien et al 2020), Dragonfly Algorithm (DA) (Mirjalili 2016a;Meraihi et al 2020b), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis 2016;Gharehchopogh and Gholizadeh 2019), Grasshopper Optimization Algorithm (GOA) (Saremi et al 2017;Meraihi et al 2021), Crow Search Algorithm (CSA) (Askarzadeh 2016;Meraihi et al 2020a), and Salp Swarm Algorithm (SSA) (Mirjalili et al 2017;Abualigah et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

Self Cite

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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A comprehensive survey of Crow Search Algorithm and its applications

Cited by 71 publications

References 157 publications

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

A comprehensive survey of sine cosine algorithm: variants and applications

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