Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Department: search.filters.department.Çankaya ÜniversitesiPublisher: search.filters.publisher.Elsevier

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  • Article
    AGENT: An Elitism-Guided Evolutionary Framework for Enhanced Task Allocation Performance in Heterogeneous Cloud Systems
    (Elsevier, 2026) Osama, Muhammad; Riaz, Muhammad Bilal; Shahid, Muhammad Farrukh; Qadri, Syed Shah Sultan Mohiuddin
    Cloud Computing (CC) delivers on-demand services through Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) models. This study specializes in the approach to IaaS task scheduling in the heterogeneous data centers, where resource allocation is a critical issue to ensure that makespan is minimized. The NP-complete nature of such a scheduling problem requires sophisticated meta-heuristic solutions as the use of cloud workloads increases exponentially. Although Genetic Algorithms (GA) have received extensive use, available adaptive variants usually vary parameters based on aggregate populations statistics without individual solution tracking and elitism is not commonly implemented with adaptive mechanisms in a size-conserving fashion. This paper presents the Adaptive Genetic Algorithm with Elitism and Nonlinear Tuning (AGENT) that combines three new innovations: (1) size-preserving elitism that guarantees a monotonic improvement without growing the population, (2) feedback-based nonlinear parameter adaptation which is controlled by explicit success/failure counters as indicators of evolutionary progress of a population, unlike fitness-proportional or population-statistics-based methods, and (3) a multi-task-per-VM allocation model that captures real cloud elasticity. Experimental validation of CloudSim Plus simulation with Amazon EC2 VM setups showed makespan improvements of 3.14%-28.89% than baseline algorithms (HAGA, AIGA, SGA, Max-Min, Min-Min) with synthetic workloads. Scalability was tested on workloads of various sizes and was found to perform well with near-optimal results. Reduced makespan is associated with shorter VM operating time, which implies that energy efficiency may be improved and therefore, it should be investigated in the future by taking direct measurements.
  • Erratum
    RETRACTION: Solar Radiation Effect on PCM Performance in the Building Applications: The Collector Energy-Saving Potential Using CF-MWCNTs and CF-GNPs
    (Elsevier, 2026) Mohammad Sajadi, S.; Chen, Liangliang; Baleanu, Dumitru; Alrabaiah, Hussam; Aldabesh, Abdulmajeed; Sajadi, S. Mohammad; Liu, Fenghua
  • Article
    A Meta-Heuristic Stochastic Algorithm for the Numerical Treatment of Cancer Model through the Chemotherapy and Stem Cells
    (Elsevier, 2026) Baleanu, Dumitru; Defterli, Ozlem; Sabir, Zulqurnain; Abdelkawy, M. A.
    Objective: The aim of current research is to present the numerical performances of the cancer treatment model based on chemotherapy and stem cells using one of the heuristic computing neural network procedures. The cancer treatment model through chemotherapy and stem cells is categorized into stem cells, affected cells, tumor cells, and chemotherapy-based concentration drug. Method: A process of artificial neural network is applied using the hybrid optimization of global and local search schemes, which are taken as genetic algorithm (GA) and an active set (AS). An error-based fitness function is designed by using the differential model and then optimized by the hybridization of both global and local search schemes. GA is applied to exploit the global result and give a primary guess to the AS that further improves the results locally. AS is rooted in the GA, where GA produces new populaces and AS optimizes the fitness function for every individual. The hybridization of these two schemes is used iteratively for purifying the results. Ten numbers of neurons and log-sigmoid activation functions has been used to solve the cancer treatment model based on chemotherapy and stem cells. Results: For the correctness of the stochastic solver, the obtained numerical results have been compared with any traditional scheme. Moreover, the reliability and capability of the scheme are performed through the absolute error around 10-05 to 10-07 along with different statistical approaches for solving the mathematical model. Novelty: The proposed artificial neural network structure along with the hybrid optimization of global and local search schemes has never been implemented before to solve the cancer treatment model based on chemotherapy and stem cells.