A Metaheuristic Approach to the C1S Problem

Abo-Alsabeh, Rewayda; Salhi, Abdellah

doi:10.24996/ijs.2021.62.1.20

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…For this purpose, a fitness value, related to the objective of the optimization problem, is the key criterion for ranking the individuals. A chromosome with a larger fitness value has high probability of resulting in an optimal solution to the problem [37,38].…”

Section: Ga-based Power Allocation Schemementioning

confidence: 99%

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Sultan

2023

Applied Sciences

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The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

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Section: Ga-based Power Allocation Schemementioning

confidence: 99%

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Sultan

2023

Applied Sciences

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“…GA can be applied in different ways. 18,19 They are essentially distinguished by the way chromosomes are represented. Concerning simplicity and computation time, some representations can be suitable for a problem more than others.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

On the maximum empty hyper-rectangle problem

Abo-Alsabeh

Daham

Salhi

2023

Journal of Algorithms & Computational Technology

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Given a rectangle [Formula: see text] containing points, we consider the problem of detecting the largest rectangle that is totally contained in [Formula: see text] and does not include any of the points. In other words, we want to find the biggest hole in the dataset that can contain the biggest possible rectangle. A new algorithm for dealing with this problem is, therefore, suggested. Existing algorithms are exact but cannot deal efficiently with problems in high dimensions and large instances. In fact, only computing maximum empty rectangles in a set of points in [Formula: see text] has been well addressed. In high dimensions, the problem is shown to be NP-complete. Our suggested approach is evolutionary in nature and is an innovative implementation of the genetic algorithm. The approach solves a more general form of the stated problem in that it ignores the axis-parallel condition imposed on the hyper-rectangles to be found. This paper includes computational results and their discussions.

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A Metaheuristic Approach to the C1S Problem

Cited by 2 publications

References 13 publications

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

On the maximum empty hyper-rectangle problem

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