A bat algorithm for SDN network scheduling

Lin, Rongheng; Ye, Zezhou

doi:10.1186/s13638-018-1145-y

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…For benchmarking, the author has chosen the well-known Rosenbrock's function with an efficient result. Considering this concept, we have realized that BA has been accepted widely for addressing optimization problems including network scheduling [83], numerical optimization [84], power dispatch [85], power flow [86], and so on. Furthermore, BA has been adopted in solving combinatorial t-way optimization problem [87] [88] [89].…”

Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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“…Lastly, in the scheduling domain, BA advanced promising and powerful solutions for most challenging problems such as job Shop scheduling [ 42 , 71 , 186 , 263 , 315 ], production scheduling [ 157 ], energy-aware workflow [ 116 ], network and cloud scheduling [ 159 , 179 , 338 ], and water resources management [ 124 ]. Figure 21 plots the applications of BA in the scheduling domain.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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“…For example, Liu applied the genetic algorithm to network QoS optimization and obtained the scheduling sequence with the shortest task span and average task calculation time, which greatly improved the performance of the network [6]; Wang et al put forward the bandwidth and capacity optimization method of elastic optical networks (eon) based on mixed-integer linear programming model [7]. e results show that this method can not only improve the overall network capacity but also minimize the number of allocated time slots and service request blocking rate; Rongheng Lin and others proposed a bat SDN network scheduling method, which transforms the resource reservation problem into a multiknapsack problem [8]; Liang et al used ant colony algorithm to schedule network resources. Experiments show that this method can e ectively reduce the time of network resource scheduling [9]; Lalitha Devi et al and Yi et al proposed a joint beam and power scheduling scheme and solved the scheduling scheme by Bayesian algorithm [10,11]; Praveena and Vijayarajan proposed that the energy consumption of mobile cloud network through the neural network is the lowest [12].…”

Section: Related Workmentioning

confidence: 99%

ODL Centralized Control of Power Communication Network Based on Bio-Inspired Algorithms and SDN

Min

2022

Mathematical Problems in Engineering

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An ODL centralized control strategy is designed to study the problem of heavy load in the power communication network, in which the machine learning method is introduced. SDN technology is used to establish SDN cluster control structure, and different algorithms, such as genetic algorithm, are utilized to optimize resource scheduling. The results show that the improved algorithm obtains the shortest link path through 28 iterations. At the same time, AHP is used to switch the network spectrum. Moreover, the application effect of the control strategy is simulated and analyzed, and its effect on network communication application is verified.

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A bat algorithm for SDN network scheduling

Cited by 9 publications

References 19 publications

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Recent advances of bat-inspired algorithm, its versions and applications

ODL Centralized Control of Power Communication Network Based on Bio-Inspired Algorithms and SDN

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