Efficient traffic control and lifetime maximization in mobile ad hoc network by using PSO–BAT optimization

Sridevi, N.; Nagarajan, V.; Sankaran, Kris

doi:10.1007/s11276-019-02173-6

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…In the wireless and network system domain, the BA is able to provide a promising solution to various application problem in wireless sensor networks [ 48 , 49 , 53 , 184 , 191 , 218 , 219 , 253 , 333 , 341 ], networking and cloud computing [ 169 , 198 , 211 , 298 ], antenna array optimization [ 72 , 83 , 115 , 150 , 272 , 297 , 300 ], networks security and privacy [ 113 , 142 , 148 , 206 , 215 , 268 , 275 , 276 , 340 ], distribution networks [ 331 , 332 ], and VANET, Ad-hoc, and IoT [ 24 , 68 , 276 ]. Figure 13 presented BA applications in the wireless and network system 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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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

View full text Add to dashboard Cite

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“…Its parameter settings are negligible. On account of its simple realization and high efciency, PSO has been successfully applied to various real-world problems such as wireless sensor networks [3], feature selection [4], trafc control [5], road identifcation [6], task allocation [7], and crowd user selection [8].…”

Section: Introductionmentioning

confidence: 99%

PSO with Mixed Strategy for Global Optimization

Pang,

Li,

Han

2023

Complexity

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Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.

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“…However, the performance in the highly dynamic environment needs to be further verified. In order to avoid problems such as network connectivity failures caused by dynamic uncertainties, such as rapid network topology changes [ 22 ], based on node power control [ 23 ], the use of inter-node transmission power maximization will quickly generate dense network areas, making the network performance decrease [ 24 , 25 ], increasing the packet loss rate of network communication and reducing network throughput [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network

Wei

Zhao

Chen

2023

Sensors

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An enhanced self-assembling network routing algorithm is proposed for the problem of weak connectivity of communication networks caused by factors such as movement or environmental interference in the construction and operation, and the maintenance of construction robot clusters. Firstly, the dynamic forwarding probability is calculated based on the contribution of nodes joining routing paths to network connectivity, and the robust connectivity of the network is achieved by introducing the connectivity feedback mechanism; secondly, the influence of link quality evaluation index Q balanced hop count, residual energy, and load on link stability is used to select appropriate neighbors for nodes as the subsequent hop nodes; finally, the dynamic characteristics of nodes are combined with the topology control technology to eliminate low-quality links and optimize the topology by link maintenance time prediction and to set robot node priority. The simulation results show that the proposed algorithm can guarantee a network connectivity rate above 97% under heavy load, reduce the end-to-end delay, and improve the network survival time, providing a theoretical basis for achieving stable and reliable interconnection between building robot nodes.

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Efficient traffic control and lifetime maximization in mobile ad hoc network by using PSO–BAT optimization

Cited by 12 publications

References 12 publications

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

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

PSO with Mixed Strategy for Global Optimization

Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network

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