Quantum Genetic Algorithm for Highly Constrained Optimization Problems

Sabaawi, Abdulbasit M. A. and Almasaoodi, Mohammed R. and El Gaily, Sara and Imre, Sándor (2023) Quantum Genetic Algorithm for Highly Constrained Optimization Problems. INFOCOMMUNICATIONS JOURNAL : A PUBLICATION OF THE SCIENTIFIC ASSOCIATION FOR INFOCOMMUNICATIONS (HTE), 15 (3). pp. 63-71. ISSN 2061-2079


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Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.

Item Type: Article
Uncontrolled Keywords: genetic algorithm, quantum computing, quantum extreme value searching algorithm, blind quantum computing
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA76.527 Network technologies / Internetworking / hálózati technológiák, hálózatosodás
Depositing User: MTMT SWORD
Date Deposited: 17 Nov 2023 12:40
Last Modified: 17 Nov 2023 12:40

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