Fuzzy Rough Set-based Link Stability Forecasting Scheme for Improving Data Dissemination in MANETs

Sengathir, J.; Priya, M. Deva; Malar, A. Christy Jeba; Karthik, S.; Ravi, Viraja; Schmidt, Péter

doi:10.1007/978-981-33-4687-1_56

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…In the studies of [33][34][35][36][37][38][39][40], the researchers used basic OBL techniques to improve their exploration of SMA variants.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

“…Liang Xu et al [36] presented an ISMA by adding an improved random OBL to increase convergence ability in the late iteration and prevent local optimizations from occurring. Sengathir J. et al [37] utilized the OBL strategy in the proposed AOLISMA for self-adjusting for exploration purposes. In addition, several modified versions of OBL exist in the literature, namely, adaptive OBL, quasi opposition optimization learning [38], and random opposition-based learning [39], which further increase population diversity and improve populations' capacities to avoid local optimality.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

See 1 more Smart Citation

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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“…In the studies of [33][34][35][36][37][38][39][40], the researchers used basic OBL techniques to improve their exploration of SMA variants.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Liang Xu et al [38]presented an ISMA by imbedding an improved random OBL to increase convergence ability in the late iteration and prevent from local optimization. Sengathir J. et al [39] utilized OBL strategy in the proposed AOLISMA to self-adjusting for exploration.…”

Section: No Strategiesmentioning

confidence: 99%

“…Several papers composed the SMA and its variants for wireless sensor network (WSN). J Sengathir et al [39] proposed an adaptive opposition learning-improved SMA (AOLISMA)based optimization routing to ensure reliable data dissemination among UAVs, extend network lifetime and minimize energy consumption. The experimental results demonstrated that the proposed AOLISMA can enhance the throughput and reduce the control overhead.…”

Section: Networkmentioning

confidence: 99%

Advances in Slime Mould Algorithm: A comprehensive Survey

Wei,

Othman,

Daud

et al. 2023

Preprint

View full text Add to dashboard Cite

Slime Mould Algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime molds during foraging. Numerous researchers have widely applied SMA and its variants in various domains and proved its value by the experiments in literatures. In this paper a comprehensive survey on SMA is introduced, which is based on 130 articles visa Google-scholar between 2022 and July, 2023. Firstly, the theory of SMA is described. Secondly the improved SMA variants are provided and categorized according to the approach that they are applied with. Finally, it also discusses the main applications domains of SMA such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, image segmentation and etc. This review presents some research suggestion for researcher who is interested in this algorithm.

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“…For situations needing quickly deployable communications with resilient, effective dynamic networking, ad hoc systems technology can offer a very flexible technique for establishing communications for fire, safety, and rescue operations [5].…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

R.,

Mohan,

Mahesh

2023

IJRITCC

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Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.

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Fuzzy Rough Set-based Link Stability Forecasting Scheme for Improving Data Dissemination in MANETs

Cited by 5 publications

References 11 publications

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A comprehensive Survey

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

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