A comprehensive survey: Whale Optimization Algorithm and its applications

Gharehchopogh, Farhad Soleimanian; Gholizadeh, Hojjat

doi:10.1016/j.swevo.2019.03.004

Cited by 521 publications

(230 citation statements)

References 110 publications

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“…Metaheuristic algorithms such as the WOA are now becoming powerful, simple, and robust approaches to solve this problem. A good rate of convergence and rapid discovery of good solutions that have higher probability and efficiency in finding global optima and are easy in concept and coding implementation compared to other heuristic optimization techniques are the main reasons for selection of the WOA [39,40]. These advantages cause the WOA to be an appropriate algorithm for solving different constrained or unconstrained optimization problems for practical applications without structural reformation in the algorithm.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

Kalantari¹,

Ebrahimnejad²,

Motameni³

2020

Turk J Elec Eng & Comp Sci

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In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services.

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Section: The Proposed Methodsmentioning

confidence: 99%

Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

Kalantari¹,

Ebrahimnejad²,

Motameni³

2020

Turk J Elec Eng & Comp Sci

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“…Algorithms based on biology include the Genetic algorithm [24], Artificial Immune Systems [25], and Biogeography-based optimization [26] while one example of a chemical-based algorithm is the Artificial Chemical Reaction optimization algorithm [27]. Music is the basis for the Harmony search algorithm [28] while biology forms the inspiration for the Ant Colony optimization [29], Particle Swarm optimization [30], Cat Swarm optimization [31], Monarch Butterfly optimization [32], Cuckoo Search [33] and Whale optimization algorithm [34]. The Matheuristic [35] and Base Optimization algorithms [36] are inspired by mathematics, while metaheuristic algorithms can sometimes be based on more than one underlying type, such as the Cultural algorithm [37] which has both social and biological bases, as is the case for Colonial Competitive difference evolution [38] and the two-phased approximation for the bat algorithm [39].…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Sujaree¹

2020

IJMO

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In this research study, a novel metaheuristic approach using nanotechnology is proposed, known as Artificial Carbon Nanotube Synthesis Optimization (ACNSO), in order to develop a vaccine cold chain network in north of Thailand. The scope of the study emphasizes Area 1 of the Office Disease Prevention and Control in the Chiang Mai region. Vaccines must be transported both to the Provincial HealthOffices and hospitals in the region. This study seeks to arrange the transportation routes involved in order to achieve the shortest possible total distance. The algorithm must first of all assess the travel conditions between each point in the network, and then generate the starting solution. Efficient solutions to this problem will cut the total processing time. The study then made a comparison between the results produced by ACNSO algorithm and those of other algorithms used in earlier studies. Full factorial design was the statistical approach used to evaluate the optimal parameters for the algorithm. The experiment was designed to examine the various factors which influence the algorithm performance. The results showed that ACNSO algorithm found the best solution in experimental algorithms and 3 rd processing time.Index Terms-Vaccine cold chain network, metaheuristic approach, full factorial design, nanotechnology, artificial carbon nanotube synthesis optimization.

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“…To have more information on the metaphor inspiring these equations, their formulations and their role in driving the research within the algorithm framework, one can see the survey article in [6]. A detailed scheme describing the coordination logic of the three previously described search mechanism is reported in Algorithm 1.…”

Section: The Whale Optimization Algorithmmentioning

confidence: 99%

“…Based on recent literature, metaheuristics for black-box optimisation have been greatly adopted in traditional static data clustering [6]. These algorithms have a general purpose application domain and often display self-adaptive capabilities, thus being able to tackle the problem at hand, regardless of its nature and formulation, and return near-optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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A comprehensive survey: Whale Optimization Algorithm and its applications

Cited by 521 publications

References 110 publications

Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

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