Yazar "Anka, Fateme Ayşin" seçeneğine göre listele

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    Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications
    (MDPI, 2022) Kiani, Farzad; Seyyedabbasi, Amir; Nematzadeh, Sajjad; Candan, Fuat; Çevik, Taner; Anka, Fateme Ayşin; Randazzo, Giovanni; Lanza, Stefania; Muzirafuti, Anselme
    The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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    PSCSO: enhanced sand cat swarm optimization inspired by the political system to solve complex problems
    (Elsevier, 2023) Kiani, Farzad; Anka, Fateme Ayşin; Erenel, Fahri
    The Sand Cat Swarm Optimization (SCSO) algorithm is a recently introduced metaheuristic with balanced behavior in the exploration and exploitation phases. However, it is not fast in convergence and may not be successful in finding the global optima, especially for complex problems since it starts the exploitation phase late. Moreover, the performance of SCSO is also affected by incorrect position as it depends on the location of the global optimum. Therefore, this study proposes a new method for the SCSO algorithm with a multidisciplinary principle inspired by the Political (Parliamentary) system, which is named PSCSO. The suggested algorithm increases the chances of finding the global solution by randomly choosing positions between the position of the candidate's best solution available so far and the current position during the exploitation phase. In this regard, a new coefficient is defined that affects the exploration and exploitation phases. In addition, a new mathematical model is introduced to use in the exploitation phase. The performance of the PSCSO algorithm is analyzed on a total of 41 benchmark functions from CEC2015, 2017, and 2019. In addition, its performance is evaluated in four classical engineering problems. The proposed algorithm is compared with the SCSO, Stochastic variation and Elite collaboration in SCSO (SE-SCSO), Hybrid SCSO (HSCSO), Parliamentary Optimization Algorithm (POA), and Arithmetic Optimization Algorithm (AOA) algorithms, which have been proposed in recent years. The ob-tained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms.
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    A smart and mechanized agricultural application: From cultivation to harvest
    (MDPI, 2022) Kiani, Farzad; Randazzo, Giovanni; Yelmen, İlkay; Seyyedabbasi, Amir; Nematzadeh, Sajjad; Anka, Fateme Ayşin; Erenel, Fahri
    Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.