Kapadokya Üniversitesi Kurumsal Akademik Arşivi

DSpace@KÜNASİS, Kapadokya Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.




 

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Introducing Party Competition: An Efficient High Speed Metaheuristic for Solving the Binary Knapsack Problem
(IEEE, 17.12.2025) yashar
With the growing demand for high-speed optimization algorithms, particularly for complex and time sensitive problems, developing an effective high-speed metaheuristic approach presents a significant challenge in this field. This paper introduces a novel metaheuristic method aimed at enhancing both speed and efficiency in solving optimization problems, especially the binary knapsack problem. The simulation results reveal that the proposed algorithm significantly outperforms Genetic Algorithms (GA) and Imperialist Competitive Algorithms (ICA) in addressing the binary knapsack problem. Notably, the high convergence speed of this new method not only enhances its effectiveness but also establishes it as a highly efficient option for tackling complex and time-critical optimization challenges.
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Introducing the Hundredth Monkey Effect: An Efficient Metaheuristic for Fast Convergence in the Least Squares Minimization Problem
(17.12.2025) yashar salami
—In the face of escalating demand for high-speed optimization algorithms, particularly for complex and time sensitive problems, developing a rapid metaheuristic method is a significant challenge. This paper introduces the Hundredth Monkey Effect as a new metaheuristic approach focusing on enhancing speed and convergence efficiency in solving optimization problems, specifically the Least Squares Minimization Problem. Simulations show that the Hundredth Monkey Effect algorithm outperforms Genetic Algorithms (GA), Simulated Annealing (SA), and Bee Colony Optimization (BCO) in terms of rapid convergence. This substantial improvement in both speed and convergence underscores the practicality and efficiency of the Hundredth Monkey Effect in addressing complex, time-sensitive optimization problems.
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Dialectic optimization algorithm (DOA): a novel metaheuristic inspired by dialectical philosophy
(26/06/2025) yashar salami
Efficient optimization methods are essential for addressing large-scale and real-time problems in supercomputing environments. This paper presents the Dialectic Opti mization Algorithm (DOA), a novel population-based metaheuristic inspired by Hegelian and Marxist dialectical philosophy. DOA simulates the ideological dynam ics of three subpopulations: supporters, opponents, and neutrals—using logistic growth equations, influence matrices, contradiction analysis, and synthesis mecha nisms. These components form a structured and adaptive search process that pro motes diversity, mitigates premature convergence, and drives the population toward global optima. A formal algorithm analysis is also provided, including first-order logical axioms, lemmas on population dynamics, and convergence theorems that mathematically validate its soundness and stability. The proposed method is empiri cally evaluated on twelve standard benchmark functions and compared against eleven widely used metaheuristics, including GA, ACO, PSO, WOA, GWO, HHO, SSA, and others. Based on 100 independent runs per function, the DOA consistently outperformed all eleven comparative algorithms in accuracy, robustness, and con vergence speed. A comprehensive statistical evaluation using Kolmogorov–Smirnov with p < 0.01, Mann–Whitney showing no statistical inferiority, Kruskal–Wallis with χ2 > 1 000 and a Friedman test yielding a mean rank of 1.08 confirmed DOA’s superior solution quality, efficiency and consistency across 12 benchmark functions, underscoring its philosophically grounded, formally validated framework for solving complex, multimodal optimization problems.
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SOBV-FEF: secure lightweight data offloading base in blockchain technology for internet of vehicles enabled handover UAVs within a Fog Edge federation
(04.12.2025) yashar salami
The Internet of Things (IoT) has improved efficiency and quality of life by connecting devices to the internet. It has seen success in areas such as smart vehicles and Unmanned Aerial Vehicles (UAVs), but faces processing limitations due to the need to send large amounts of data to other devices for processing. When heavy processing is required, it uses offloading techniques to send the data to other devices for processing. Secure data offloading transmission remains a fundamental challenge in this field. This paper presents an innovative authentication and key exchange method that uses Elliptic Curve Cryptography (ECC) and incorporates Handover for secure offloading, offering a safe, lightweight solution within a blockchain network. To evaluate the resistance of the proposed scheme against active and passive attacks, we employed the AVISPA tool to apply both formal and informal methods. Subsequently, to demonstrate the scheme's lightweight nature, we examined it with respect to computation and communication costs, the number of bits used, and security requirements. Additionally, we simulated the proposed scheme using the NS3 tool in two scenarios: urban and highway, with varying numbers of vehicles. The results indicate that the proposed scheme performs acceptably in urban and highway scenarios.
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SO-ITS: a secure offloading schemefor intelligent transportation systems in federated fog-cloud
(03/08/2025) yashar salami
Intelligent driving technologies have significantly reduced traffic congestion and road accidents, enhancing overall safety by enabling vehicles to communicate with their surroundings, thus keeping drivers informed of traffic conditions and critical events. Adequate data security demands mutual authentication for secure exchanges and robust offloading procedures to guard against potential attacks. This paper presents the SO-ITS scheme, tailored for safe data offloading in intelligent driving systems. The scheme’s robustness is validated using the AVISPA tool, confirming its resilience to known threats. Comparative analysis with existing schemes assesses communication overhead, computational cost, and bit complexity. At the same time, performance is evaluated through NS-3 simulations, measuring PDR, throughput, and EDD across various scenarios. Results demonstrate that the SO-ITS scheme provides strong security, low communication overhead, and moderate computational complexity, establishing it as a promising solution for secure intelligent transportation systems.