Optimal control of a service system with emergency vacation using bat algorithm

Shekhar, Chandra; Varshney, Shreekant; Kumar, Amit

doi:10.1016/j.cam.2019.06.048

Cited by 21 publications

(7 citation statements)

References 25 publications

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“…11 ), most of the BA applications under electrical and power system research domain. In this domain, the optimization problems addressed by BA are economic/emission dispatch [ 40 , 173 , 267 , 292 ], power economic dispatch [ 210 ], reactive power dispatch [ 209 , 214 ], optimal power flow [ 326 ], electronics, control, and communication [ 1 , 38 , 132 , 226 , 257 , 266 ], solar PV parameter estimation and solar radiation forecasting [ 3 , 310 , 337 ], photovoltaic energy system [ 76 , 94 – 96 , 145 , 176 , 194 , 225 , 259 ], forecasting of petroleum consumption [ 58 ], load frequency control [ 19 , 92 , 93 ], energy consumption [ 104 , 219 , 261 ], proportional-integral-derivative (PID) controller [ 60 , 122 , 164 , 187 , 188 , 193 , 224 , 233 , 330 ], wind speed forecasting [ 67 , 174 , 222 , 309 , 311 ], power system [ 30 , 31 , 36 , 70 , 81 , 91 , 106 , 121 , 162 , 258 , 278 , 284 ], dam–reservoir operation [ 99 ], and others [ 136 ]. These applications are presented in Fig.…”

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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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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“…However, there is a problem in updating the solution in this way. The generation of the new solution is random, and the updating of the solution has no directionality.Therefore, the mutation factor θ is introduced in the local search, which makes the directivity of the solution more random and enhances the diversity of the descendant population [31].namely…”

Section: Improved Cuckoo Search Algorithmmentioning

confidence: 99%

Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

Wang

wang

Zhu

et al. 2020

Preprint

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In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an Improved Cuckoo Search (ICS) algorithm is proposed. This algorithm is based on the traditional Cuckoo Search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird ’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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“…Recently, Banik and Ghosh [11] analyzed the finite-buffer single processor computing system with vacation(s) with the assumptions that the arrivals followed a batch Markovian arrival process (BMAP) and got the processing according to a non-exhaustive type gated-limited service discipline. Shekhar et al [12] used metaheuristic scheme to device optimal strategies for emergency vacationing queueing model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…)s P 0,n (s) = − λβ + nη + θ P 0,n (s) + λβ P 0,n−1 (s) + (n + 1) η P 0,n+1 (s); n ≥ 2(12) s P 1,1 (s) = − (ξµ + λ) P 1,1 (s) + θ P 0,1 (s) + ξµ P 1,2 (s)(13) s P 1,n (s) = − (ξµ + λ) P 1,n (s) + λ P 1,n−1 (s) + ξµ P 1,n+1 (s) + θ P 0,n (s); n ≥ 2(14) …”

mentioning

confidence: 99%

Transient Solution of Multiple Vacation Queue with Discouragement and Feedback

et al. 2020

Self Cite

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The congestion problems with processor vacations have confronted with increasing intricacy, and their explicit transient solutions are exceptionally hard to compute. The transient solution is more significant for studying the dynamical behavior of computing systems over a finite period and predominantly utilizes within the state-of-the-art design architect for a real-time I/O system. Motivated from this, we adopt the mathematical concepts, namely continued fractions and generating function, to derive explicit expressions for transient-state probabilities. Transient-state probabilities of the processing delay problem with a single processor which adopts the multiple vacations policy to save power consumption and thermal trip error with discouragement and feedback are obtained in terms of modified Bessel functions using the properties of the confluent hypergeometric function. Due to the inaccessibility of processor, discouragement behaviors balking and reneging of the job requests are prone to exhibit. Routing back for the service feedback for the processed job request is also critical to maintaining the quality of service (QoS). For the glance of the I/O system performance, the expected value of the state of the computing system using stationary queue-size distribution is also derived.

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Optimal control of a service system with emergency vacation using bat algorithm

Cited by 21 publications

References 25 publications

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

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

Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

Transient Solution of Multiple Vacation Queue with Discouragement and Feedback

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