Filtreler

2017 - 201912020 - 20221
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Bölüm: Işık University, Faculty of Engineering, Department of Computer Engineering × Konu: Stochastic systems ×

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Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    Modeling and simulation support to the defense planning process
    (Sage Publications Inc, 2017-04-01) Çayırcı, Erdal; Özçakır, Lütfü
    Defense planning is a crucial part of the defense process. It identifies the capabilities required for the future defense environment, analyzes the capability shortfalls, prioritizes them, and provides the fundamental inputs for their development. Modeling and simulation may significantly contribute to the success of defense planning. However, neither the theory nor the tools are mature enough to fulfill the defense planning requirements. Various types of simulation tools, such as static, dynamic, deterministic, stochastic, closed, discrete, continuous, and symbiotic, in multiple levels of resolution and fidelity are needed to support the different stages and phases. The verification and validation of the models and the analysis of the input and output data are critical. Yet another challenge is that the uncertainties related to the contemporary defense scenarios are mostly not in aleatory but in the epistemic domain. In this paper, we briefly present a new computer-assisted defense planning process. Then, we introduce the service-oriented cloud approach for the modeling and simulation support to the process.
  • Küçük Resim
    Yayın
    BinBRO: Binary Battle Royale Optimizer algorithm
    (Elsevier Ltd, 2022-02-04) (Rahkar Farshi), Taymaz Akan; Agahian, Saeid; Dehkharghani, Rahim
    Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.