Bilgi Güvenliği Teknolojisi - Bildiri Koleksiyonu

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https://hdl.handle.net/20.500.12695/2798

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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.
  • Küçük Resim
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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.