Browsing by Subject "Metaheuristic methods"

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A comprehensive state-of-the-art survey on the recent modified and hybrid analytic hierarchy process approaches
(Elsevier, 2023-11-16) Ashour, Mojtaba; Mahdiyar, A.
Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making (MCDM) problems. Over time, numerous hybridizations, improvements, and modifications have been proposed to address the shortcomings of traditional AHP. Considering the sheer number of the AHP-based methods, scholars/practitioners are faced with certain challenges when selecting a suitable method due to: (i) lack of adequate knowledge on pros and cons of different AHP approaches, (ii) difficulties and limitations in the application and analysis, and (iii) uncertainties about the suitability of the method. As a result, there is a need for a comprehensive review functioning as a guidance when choosing the best-suited approach considering the specific features of the problem at hand. This paper, therefore, reviews articles published between 2010 and 2023 that have proposed a hybrid, improved, or modified AHP and classifies them based on three main categories of contributions: (A) consistency improvements, (B) reducing the difficulties or limitations, and (C) increasing the accuracy of the results. These categories are further discussed based on the nature of variation (hybridizing with fuzzy sets, metaheuristic algorithms, modification of AHP structure, and hybridization with other approaches). A comprehensive summary table is provided to showcase the strengths and weaknesses of each method, and a roadmap is put forward for scholars and industry experts assisting them in the selection of the appropriate method considering various aspects of problems. Finally, directions for future research are discussed.
Open Access
Sum rate maximization for hybrid relay-RIS-assisted MU-MISO systems: multiple access techniques
(2024-09) Hokmabadi, Ayda Nodel
Space Division Multiple Access (SDMA) is a pivotal multiple access technique in modern wireless communication systems. Additionally, Reconfigurable Intelligent Surfaces (RIS) is recognized as fundamental technology for next-generation wireless communications. This work investigates a multiuser downlink Multiple-Input Single-Output (MISO) system with an underloaded or critically loaded network. Here, a multiantenna base station (BS) communicates with multiple single-antenna users by leveraging a combination of a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. In this study, the aim is to maximize the sum rate by joint design of active beamforming at the BS and the DF relay in addition to passive beamforming at the RIS under maximum power constraints, minimum SINR constraints at the relay, and unit-modulus constraint for the RIS elements. The complex design problem is addressed using Lagrangian Dual Transformation (LDT), Quadratic Transformation (QT), and Semidefinite Relaxation (SDR). Also, an alternating optimization algorithm is proposed. In the context of performance evaluation, benchmarks such as Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) are considered. Furthermore, Particle Swarm Optimization (PSO) is utilized for RIS phase profile optimization as a benchmark for the model-based proposed algorithm. This comparative analysis provides insights into the effectiveness of the proposed SDMA-enabled hybrid RIS-Relay communication system and the efficiency of the metaheuristic PSO algorithm for large sizes of the RIS.