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  • Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

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    Mirjalili423316-Accepted.pdf (302.8Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Pham, Quoc-Viet
    Mirjalili, Seyedali
    Kumar, Neeraj
    Alazab, Mamoun
    Hwang, Won-Joo
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
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    Abstract
    Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based ...
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    Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
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    Journal Title
    IEEE Transactions on Vehicular Technology
    Volume
    69
    Issue
    4
    DOI
    https://doi.org/10.1109/TVT.2020.2973294
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Engineering
    Science & Technology
    Engineering, Electrical & Electronic
    Telecommunications
    Transportation Science & Technology
    Publication URI
    http://hdl.handle.net/10072/396276
    Collection
    • Journal articles

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