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grasshopper optimization algorithm (goa)

در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه grasshopper optimization algorithm (goa) در مقالات مجلات علمی
  • Bahar Ghaderi, Hamid Azad *, Hamed Agahi

    The image super-resolution (ISR) process provides a high-resolution (HR) image from an input low-resolution (LR) image. This process is an important and challenging issue in computer vision and image processing. Various methods are used for ISR, that learning-based methods are one of the most widely used methods in this field. In this approach, a set of training images is used in various learning based ISR methods to reconstruct the input LR image. To this end, appropriate reconstruction weights for the image must be computed. In general, the least-squares estimation (LSE) approach is used for obtaining optimal reconstruction weights. The accuracy of SR depends on the effectiveness of minimizing the LSE problem. Therefore, it is still a challenge to obtain more accurate reconstruction weights for better SR processing. In this study, a Grasshopper Optimization Algorithm (GOA)-based ISR method (GOA-ISR) is proposed in order to minimize the LSE problem more effectively. A new formulation for the upper bound and the lower bound is introduced to make the search process of the GOA algorithm suitable for ISR. The simulation results on DIVerse 2K (DIV2K) dataset, URBAN100, BSD100, Set 14 and Set 5 datasets affirm the advantage of the proposed GOA-ISR approach in comparison with some other basic Neighbor Embedding (NE), Sparse Coding (SC), Adaptive Sparse, Iterative Kernel Correction (IKC), Second-order Attention Network (SAN), Sparse Neighbor Embedding and Grey Wolf Optimizer (GWO) methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results of the experiments show the superiority of the proposed method comparing to the best compared method (DWSR) increases 8.613 % PSNR.

    Keywords: Super Resolution (SR), High-Resolution (HR), Low-Resolution (LR), Learning-Based Methods, Grasshopper Optimization Algorithm (GOA)
  • Zahra Zanjani Foumani, Ensieh Ghaedy heidary, Amir Aghsami, Masoud Rabbani

    This paper proposes a bi-objective model for a green closed-loop supply chain network design. Four levels for forward and five levels for reverse flow were considered, including plants, distribution centers, online retailers, traditional retailers and customers for forward flow and customers, collecting centers, disposal centers, repair centers and plants for the reverse flow. The objectives are minimizing the GHG emission and maximizing profit by considering defective products and a second market for these products. Also, online retailers were considered alongside with traditional ones, since the Covid-19 pandemic has led to increase in the amount of online shopping. GAMS software and the Lpmetric technique were used to solve the model in the small and medium sizes. However, for the large size, we used Grasshopper Optimization Algorithm (GOA) as a meta-heuristic approach since solving the large size problem with GAMS is a complicated and time-consuming process. We provided Numerical and computational results to prove the efficiency and feasibility of the presented model. Finally, the managerial insights and future works were provided.

    Keywords: CO2 emission, Defective product, Grasshopper Optimization Algorithm (GOA), Green Closed-loop supply chain, Mixed-integer programming
  • MohammadAli Labbaf Khaniki, MohammadBehzad Hadi, Mohammad Manthouri

    One of the essential pieces of equipment in the power system is the Automatic Voltage Regulator (AVR) or synchronous generator excitation. The system's goal is to maintain the terminal voltage of the synchronous generator at the desired level. AVR is inherently uncertain. Hence, the proposed controller should be able to handle the problem. In this paper, Fractional Order Fuzzy PID (FOFPID) controller has been employed to control the system. To enhance the controller's performance, the Grasshopper Optimization Algorithm (GOA) is used to tune the controller's parameters. Unlike other methods, the FOFPID controller gains are not constant and alter in different operatingconditions. The robustness of the controller has been investigated, and the comparative results show that the proposed controller has a better performance against other methods

    Keywords: Automatic Voltage Regulator (AVR), Synchronous Generator Excitation, Fractional-order Fuzzy PID (FOFPID) Controller, Grasshopper Optimization Algorithm (GOA), Uncertainty
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