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Author: search.filters.author.Mohammad KhalilzadehSubject: search.filters.subject.Algorithm

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    An approach for managing the Internet of things’ resources to optimize the energy consumption using a nature-inspired optimization algorithm and Markov model
    (Elsevier Inc., 2022-12-01)
    The Internet of Things (IoT) is a developed communication idea in which connectivity and intelligence are added to nearly every device in a real-world context. From a technological standpoint, IoT provides a computational and communications capability to an item to connect to the Internet. Resource management policies are integral to IoT systems to ensure service quality and prevent energy loss and resource fragmentation. The resource management architecture includes some parts that coherently control resources in a way that minimizes resource loss in addition to offsetting the application service quality limitations. To this aim, planning and scheduling components are critical in determining the condition of available resources and the best candidates for hosting a program module. We proposed a method to handle IoT's resources utilizing a nature-inspired optimization algorithm and a Markov model to solve these restrictions. The performance of the proposed method was evaluated to current IoT access management systems in this article. In comparison to earlier approaches, the results demonstrated the efficacy of the new approach in terms of execution time and energy consumption.
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    An analysis of the operation factors of three PSO-GA-ED meta-heuristic search methods for solving a single-objective optimization problem
    (Hindawi Limited, 2022-01-01)
    In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in optimization of mathematical equations. A comparison is then made between the results and the efficiency, which is determined by the number of iterations, the observed accuracy, and the overall run time. Additionally, the optimization employs 12 functions from Easom, Holder table, Michalewicz, Ackley, Rastrigin, Rosen, Rosen Brock, Shubert, Sphere, Schaffer, Himmelblau's, and Spring Force Vanderplaats. Furthermore, the crossover rate, mutation rate, and scaling factor are evaluated to determine the effectiveness of the following algorithms. According to the results of the comparison of optimization algorithms, the DE algorithm has the lowest time complexity of the others. Furthermore, GA demonstrated the greatest degree of temporal complexity. As a result, using the PSO method produces different results when repeating the same algorithm with low reliability in terms of locating the optimal location.
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    A New Method for Solving the Mobile Payment Scheduling Problem Using Harris Hawks Optimization Algorithm During the COVID-19 Pandemic
    (Springer Science and Business Media Deutschland GmbH, 2023-01-01)
    The Coronavirus Disease 2019 (COVID-19) epidemic is causing once-in-a-century upheavals in global civilization. Payment systems have advanced lately, from simple cash or credit card transactions to various forms of mobile payment systems. This transformation is occurred due to COVID 19 and shifts in the economy, the growth of social networks, technical advancements on the Internet, and the increased usage of mobile devices. Throughout COVID19, this article offers a unique approach to the payment scheduling issue, which seeks out a timetable that enhances the project's stakeholders' benefit. Both the sponsor and the contractor in a project want to have a strong payment plan on their own. To create an equal schedule between the sponsor and the development team, the timing of payments and the completion periods of project activities are decided concurrently. The Harris hawks optimization method is designed to tackle the problem because of its high NP-hardness. Harris hawks optimization is a novel meta-heuristic nature-inspired optimizer inspired by how Harris hawks hunt food in nature. By comparing the suggested Harris hawks optimization optimizer to existing nature-inspired methods, the efficacy of the suggested Harris hawks optimization optimizer is determined. The Harris hawks optimization algorithm appears to be highly promising based on the statistical findings and comparisons. The MATLAB simulator's simulation findings confirm the algorithm's superiority over earlier efforts regarding energy, cost, delay time, and net value.
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