Seamless Vertical Handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks

Velmurugan, T.; Хара, Сибарам; Nandakumar, S.; Saravanan, B.

doi:10.1016/j.asej.2015.11.020

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…Compared to other benchmark results the developed algorithm performs well for this problem. Invasive weed optimization algorithm and variations of it have also been applied to optimising heterogeneous wireless network issues [86] and economic dispatch of power systems [83] amongst others, demonstrating strong performances throughout.…”

Section: Applications Of Iwomentioning

confidence: 99%

“…IWO is a widely applicable and powerful meta-heuristic algorithm, which has been customised successfully for many different problem spaces [15,[83][84][85][86] . The algorithm requires that hyper-parameters are initialised to appropriate values before the iterative process begins, and if they are not it is possible that the algorithm will converge to a local minimum or a never converge.…”

Section: Problems With Iwomentioning

confidence: 99%

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Review and Classification of Bio-inspired Algorithms and Their Applications

et al. 2020

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Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the life forms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: (1)evolutionary based bio-inspired algorithms, (2) swarm intelligencebased bioinspired algorithms, (3) ecology-based bio-inspired algorithms and (4) multi-objective bioinspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in Evolutionary based Bio-

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Section: Applications Of Iwomentioning

confidence: 99%

Section: Problems With Iwomentioning

confidence: 99%

Review and Classification of Bio-inspired Algorithms and Their Applications

et al. 2020

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“…How to a optimum capacity for FACTS devices is to be discussed in the accompanying dia numerical stochastic and subsidiary free optimiser both are IWO calculation (Nagib et Propagation, spatial dispersal and competitive prohibition are the procedures derived colonising behaviour (shown in Figure2) (Yahyatabar and Najafi, 2017). The sketch te the essential IWO can be summarised as follows (Velmurugan et al, 2016):…”

Section: Iwo Algorithm For Optimal Capacity Selectionmentioning

confidence: 99%

Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation Algorithm

Shanmugam¹,

Rajesh²

2020

HKIE Transactions

205

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This paper proposes a hybrid methodology to improve the dynamic strength of frameworks by optimally locating and sizing the unified power flow controller (UPFC). The proposed method combines both the antlion optimiser (ALO) and invasive weed optimisation (IWO) techniques. The advantages of the proposed methodology are enhanced searching ability, better identification of optimal solutions and greater reliability. Initially, to locate UPFCs, using the ALO technique, the most extreme power loss bus is recognised as the optimal location. The capacity required to resolve the situation is determined by the IWO technique based on the minimum power loss, voltage deviation and establishment cost of the UPFCs. The proposed system is actualised and aims at the matrix laboratory (MATLAB) environment, while the power flow security and voltage stability are evaluated on the basis of the Institute of Electrical and Electronics Engineers (IEEE) standard benchmark system. At this point, voltage stability, power loss, overloading and UPFC costs are analysed through changing the system load and generation. The proposed working method is then assessed and compared with traditional techniques.

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“…An Invasive Weed Optimization (IWO) algorithm was designed in [6] to perform the seamless handoff with probable attachment points for a particular mobile node. But the traffic-aware seamless handoff decision making was not performed.…”

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confidence: 99%

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

D.¹,

Dr.²

2020

INDJCSE

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Seamless mobility management is an ability to provide the various services during the communication in wireless heterogeneous networks. Due to the random mobility of the mobile terminals, the connectivity between different mobile devices gets lost. In order to provide the lossless connectivity between the mobile devices, the handover from the point of current attachment to another point is necessary. To improve the Seamless mobility management and traffic control, an efficient model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is introduced in the heterogeneous network. When a mobile node in the network moves out of its communication range, the signal strength of the nodes is calculated. Based on the signal strength estimation, the Generalized Light Gradient Boost Decision Tree classifier categorizes the mobile nodes into the weak and strong signal strength with the threshold value. The boosting algorithm initially constructs' weak learners i.e. binary decision tree to identify the weak signal strength of the mobile node. Then the ensemble classifier combines the results of weak learners and minimizes the generalization error. This helps to perform the handover only with the weak signal strength of the node resulting in minimizes the redundant handover. In addition, the weak signal strength of the mobile node from the current attachment point handover towards the nearest available attachment point to improve the continuous data delivery. Followed by, bandwidth availability is measured for reducing the packet loss due to thenetwork traffic resulting in improves the seamless data delivery between the nodes. The simulation is carried out to evaluate the performance of the GLGBDT-TASM model with two related approaches. The results show that the GLGBDT-TASM model effectively improved traffic-aware seamless mobility in a heterogeneous network with minimum delay and packet loss as well as a higher data delivery rate as compared to state-of-the-art methods.

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Seamless Vertical Handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks

Cited by 14 publications

References 12 publications

Review and Classification of Bio-inspired Algorithms and Their Applications

Review and Classification of Bio-inspired Algorithms and Their Applications

Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation Algorithm

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

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