Optimized Open Shortest Path First Algorithm Based On Moth Flame Optimization

Garg, Pertik; Gupta, Ashu

doi:10.17485/ijst/2016/v9i48/104255

Cited by 11 publications

(5 citation statements)

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“…This is an effective method for traveling in a straight line. Similar methods can also be used by humans [ 38 , 39 , 40 ]. Suppose if a human wants to go towards the east and the moon is in the southern side of sky, the person can travel in a straight line if the person keeps the moon to his/her left side while moving.…”

Section: Moth Flame Optimizermentioning

confidence: 99%

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Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)

Durrani

Tariq

Aadil

et al. 2019

Sensors

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Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms.

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Section: Moth Flame Optimizermentioning

confidence: 99%

“…Therefore, with each search operation the moth flies around a flame and will update it if it finds a better position. The moth will always find the best solution by adopting this mechanism [ 39 , 40 , 41 ].…”

Section: Moth Flame Optimizermentioning

confidence: 99%

Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)

Durrani

Tariq

Aadil

et al. 2019

Sensors

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show abstract

“…The simplicity and efficiency of the algorithm prompted its application to problems in various fields; for example, Yousri et al [32] utilized MFO to extract diode parameters in polycrystalline silicon solar modules, and Taher et al [33] used an improved MFO to treat optimal power flow problems. There are numerous other benchmarking studies and applications of MFO, such as in the open shortest path first algorithm for routing in computer networks [34], in feature selection of machine learning [35], and to address loss minimization in optimal reactive power dispatch (ORPD) problems [36]. The results suggest that the MFO algorithm provides advantages in convergence speed and robustness.…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

Wang

Ма

Wang

et al. 2021

IEEE Trans. Evol. Computat.

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Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy.Here we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. Experimental results on five real-world social networks, comparing our proposed method against several alternatives in current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

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“…Their going after moonlight with a fixed angle keeps them in a straight line. Humans adopted the same method for traveling in a straight line at night [14,15]. For example, at night, if a man wants to walk toward the west, the moon position must be on the northern side of the sky.…”

Section: Introductionmentioning

confidence: 99%

IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

Tariq

Iqbal

Aadil

2020

Wireless Communications and Mobile Computing

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Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid.

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Optimized Open Shortest Path First Algorithm Based On Moth Flame Optimization

Cited by 11 publications

References 0 publications

Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)

Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

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