Yazar "Anka, Ferzat" seçeneğine göre listele

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    Advances in Artificial Rabbits Optimization: A Comprehensive Review
    (Springer Science and Business Media B.V., 2024) Anka, Ferzat; Ağaoğlu, Nazım; Nematzadeh, Sajjad; Torkamanian afshar, Mahsa; Gharehchopogh, Farhad Soleimanian
    This study provides an in-depth review and analysis of the Artificial Rabbit Optimization (ARO) algorithm inspired by the survival strategies of rabbits. The ARO tries to find the global solution in the search space according to the rabbits’ detour foraging strategy and searches locally according to their random hiding structure. This algorithm has various advantages such as a simple structure, fast running model, easy adaptation feature, few parameters, independent mechanism in exploration and exploitation phases, transitions between phases with a specific mechanism, reasonable convergence rate, and property of escaping local optima. Therefore, it has been preferred by many researchers to solve various complex optimization problems. ARO-based studies have been published at prestigious international publishers such as Elsevier, Springer, MDPI, and IEEE since its launch in July 2022. The rates of studies in these publishers are 34%, 19%, 18%, and 15%, respectively. The remaining 14% includes papers published by other publishers. Besides, the cited studies on this algorithm are examined in four categories: Improved, hybrid, variants, and adapted. Research trends demonstrate that 27%, 31%, 9%, and 33% of ARO-based studies fall into these categories. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.
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    Advances in sand cat swarm optimization: a comprehensive study
    (Springer science and business media B.V., 2025) Anka, Ferzat; Aghayev, Nazim
    This study provides an in-depth review and analysis of the nature-inspired Sand Cat Swarm Optimization (SCSO) algorithm. The SCSO algorithm effectively focuses on exploring solution areas inspired by sand cat hearing and finding the most suitable solutions for their hunting behavior. This algorithm is easily adaptable to various problems due to its stability, low-cost, flexibility, simple implementation, simplicity, derivative-free mechanism, and reasonable computation time. For these reasons, although it was published recently, it has begun to attract the attention of researchers. SCSO-based research has been presented in prestigious international journals such as Elsevier, Springer, MDPI, and IEEE since its inception in 2022. The studies cited in this paper are examined in three categories: improved, hybrid, and adapted. Research trends show that 39, 21, and 40% of SCSO-based studies fall into these three categories, respectively. Additionally, research on solving various problems inspired by the SCSO algorithm is discussed from two different perspectives: global optimizations and real-world applications. Analysis of the applications shows that 15 and 85% of the studies belong to these two fields, respectively.
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    Efficiency-sustainability models to assess blockchain adoption strategies with uncertainty in the oil and gas sector
    (Springer Science and Business Media B.V., 2024) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Anka, Ferzat
    The Oil and Gas (O&G) supply chain, vital for energy delivery, faces challenges such as excessive paperwork, limited transparency, and sustainability issues due to conventional governance methods. This study develops a decision support framework for evaluating blockchain deployment strategies in the sector, focusing on cost, profit, and income. The framework evaluates blockchain strategies under both deterministic and non-deterministic conditions using Data Envelopment Analysis (DEA) models to assess cost-efficiency, profit-efficiency, and income-efficiency. The analysis provides a comprehensive view of the evaluation landscape, aimed at enhancing supply chain managers' decision-making. Application of the framework to the Norwegian O&G industry revealed notable differences in outcomes between cost-efficiency and profit-efficiency models. The cost-efficiency model favored a single-use strategy, while the profit-efficiency model preferred a substitution strategy. Additionally, the study found that strategies' effectiveness varied under deterministic versus uncertain conditions, with a single-use strategy being more effective in deterministic conditions and a localization strategy in mixed conditions. Statistical analysis indicated significant variance between the cost and profit approaches, highlighting that the developed framework offers a more nuanced perspective for supply chain managers to make informed decisions. To the best of our knowledge, this research is the first attempt to simultaneously consider blockchain adoption strategies under profit, cost, income, uncertainty, and optimization models. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.