Publications Scientifiques

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Subject: search.filters.subject.Heuristic algorithms

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    A Comprehensive Survey of Manta Ray Foraging Optimization: Theory, Variants, Hybridization, and Applications
    (Springer Science and Business Media, 2025) Yahia, Selma; Taleb, Sylia Makhmoukh; Ait Saadi, Amylia; Meraihi, Yassine; Bhuyan, Bikram Pratim; Mirjalili, Seyedali; Ramdane-Cherif, Amar
    The Manta Ray Foraging Optimization (MRFO) algorithm is a recent Swarm-based meta-heuristic optimization algorithm inspired by the foraging behavior of manta rays in catching and hunting their prey, utilizing three main techniques (i.e.: chain foraging, somersault foraging, and cyclone foraging). Since its development by Zhao et al. (Neural Comput Appl 32:9777–9808, 2020; Eng Appl Artif Intell 87:103300, 2020), the MRFO algorithm has garnered significant attention among researchers and has been applied across various fields to solve real-world optimization problems. This is due to its simple structure, flexibility, ease of implementation, and reasonable convergence rate. This paper provides an extensive and in-depth survey of the MRFO algorithm including modification, multi-objective, and hybridized versions. It also examines the various applications of the MRFO algorithm in several domains of problems such as classification, feature selection, scheduling, robotics, photovoltaic power systems, optimal parameter control, and clustering. Furthermore, the results of the MRFO algorithm are compared with some well-regarded optimization meta-heuristics such as Differential Evolution (DE), Harmony Search (HS), Bat Algorithm (BA), Multi-Verse Optimizer (MVO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Henry Gas Solubility Optimization (HGSO), and White Shark Optimizer (WSO). Finally, the paper proposes some potential future research directions to further advance the MRFO’s capability and applicability
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    Towards a Blockchain and Software-Defined Vehicular Networks Approaches to Secure Vehicular Social Network
    (IEEE, 2018) Yahiatene, Youcef; Rachedi, Abderrezak
    In this paper, we propose a new framework based on two main concepts: Software-Defined Vehicular Networks (SDVN) and Blockchain to efficiently manage and secure Vehicular Social Network (VSN). Using SDVN makes the network programmable, virtualized, and partitionable, but also it creates a well-known vulnerability named single-point of failure. Hence we propose to introduce a Blockchain paradigm that enables to certify the transactions and provide anonymity of data in distributed way using miners nodes. To this end, we introduce three levels of controllers: Principal controller (PC), Road Side Units (RSU) and miners. The PC has a global overview of the network like network topology. The RSU is an intermediate between the PC and the miners. We select local controllers acting as miners due to safety and performance. In order to select miners, we propose a Distributed Miners Connected Dominating Set algorithm (DM-CDS). The DM-CDS is a distributed algorithm with a single phase that supports dynamic topology. The selection of miners is based on a function called miner-score which depends on trust parameter particularly trust metric and network parameters such as: the connectivity degree, the average link quality indicator and the rank. The performance of the proposed DM-CDS is evaluated using many scenarios with different parameters like trust metric, node density, node mobility and radio range. The obtained results show the importance of the proposed architecture in terms of number of miners (CDS size) and robustness with different scenarios.
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    Performance analysis of stand-alone six-phase induction generator using heuristic algorithms
    (Elsevier, 2019) Bouhadjraa, Dyhia; Kheldoun, Aissa; Zemouche, Ali
    The paper exhibits the performance analysis of six-phase self-excited induction generator for stand-alone wind energy generation system. The analysis is based essentially on solving the nonlinear equivalent circuit of the SP-SEIG, which is to find the per-unit frequency F and the magnetizing reactance Xm minimizing the determinant of the nodal admittance matrix Y instead of solving two non-linear equations with two unknowns. Hence, the equation-solving problem is converted to an optimization problem. The obtained minimum yields the adequate magnetizing reactance and frequency which will be used subsequently to compute the self-excitation process requirements in terms of the prime mover speed, the excitation capacitance and the load impedance on the one hand and to predict the generator steady state performance parameters on the other. In this work, the analysis is performed using three different global search algorithms, the genetic algorithm (GA), the particle swarm optimization (PSO) technique and the Taguchi optimization method (TM). A study of some simulation results is carried out using MatLab to compare between these three algorithms in terms of accuracy and guaranteed convergence in finding the minimum of the admittance
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    A revised BROGO algorithm for leader election in wireless sensor and IoT networks
    (IEEE, 2017) Bounceur, Ahcène; Bezoui, Madani; Euler, Reinhardt; Lalem, Farid; Lounis, Massinissa
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    A wait-Before-Starting algorithm for fast, fault-Tolerant and low energy leader election in WSNs dedicated to Smart-Cities and IoT
    (IEEE, 2017) Bounceur, Ahcène; Bezoui, Madani; Euler, Reinhardt; Lalem, Farid