Yazar "Ghafouri, Shafi" seçeneğine göre listele

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    Advances in Manta Ray Foraging Optimization: A Comprehensive Survey
    (Springer Singapore Pte Ltd, 2024) Gharehchopogh, Farhad Soleimanian; Ghafouri, Shafi; Namazi, Mohammad; Arasteh, Bahman
    This paper comprehensively analyzes the Manta Ray Foraging Optimization (MRFO) algorithm and its integration into diverse academic fields. Introduced in 2020, the MRFO stands as a novel metaheuristic algorithm, drawing inspiration from manta rays' unique foraging behaviors-specifically cyclone, chain, and somersault foraging. These biologically inspired strategies allow for effective solutions to intricate physical challenges. With its potent exploitation and exploration capabilities, MRFO has emerged as a promising solution for complex optimization problems. Its utility and benefits have found traction in numerous academic sectors. Since its inception in 2020, a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE, Wiley, Elsevier, Springer, MDPI, Hindawi, and Taylor & Francis, as well as at international conference proceedings. This paper consolidates the available literature on MRFO applications, covering various adaptations like hybridized, improved, and other MRFO variants, alongside optimization challenges. Research trends indicate that 12%, 31%, 8%, and 49% of MRFO studies are distributed across these four categories respectively.
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    The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computing
    (Springer Singapore Pte Ltd, 2023) Abdalrahman, Aveen Othman; Pilevarzadeh, Daniel; Ghafouri, Shafi; Ghaffari, Ali
    Fog Computing (FC) provides processing and storage resources at the edge of the Internet of Things (IoT). By doing so, FC can help reduce latency and improve reliability of IoT networks. The energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog environments. Energy consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible resources. To deal with this problem, a binary model based on the combination of the Krill Herd Algorithm (KHA) and the Artificial Hummingbird Algorithm (AHA) is introduced as Binary KHA- AHA (BAHA-KHA). KHA is used to improve AHA. Also, the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling (DVFS) method. The Heterogeneous Earliest Finish Time (HEFT) method is used to discover the order of task flow execution. The goal of the BAHA-KHA model is to minimize the number of resources, the communication between dependent tasks, and reduce energy consumption. In this paper, the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog resources. The results were tested on five different workflows (Montage, CyberShake, LIGO, SIPHT, and Epigenomics). The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA, KHA, PSO and GA algorithms. The BAHA-KHA model has reduced the makespan rate by about 18% and the energy consumption by about 24% in comparison with GA.