فهرس المقالات Whale algorithm

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  1 - Image Resolution Increasing using Segmentation
  Zahra Ghanbari Vahid Ghods
  Increasing the image resolution is very important and is used in various fields such as medicine, photography, and machine vision. It is possible to see more details of the image and analyze it better by increasing the image resolution. However, increasing the image res أکثر

  Increasing the image resolution is very important and is used in various fields such as medicine, photography, and machine vision. It is possible to see more details of the image and analyze it better by increasing the image resolution. However, increasing the image resolution has also been associated with some challenges. Increase in noise, increase in artificial details, and high processing time are among the typical challenges. In addition, interaction with image complexities such as images with repetitive patterns and non-textured noises creates other challenges either. Image is divided into smaller parts using segmentation. Then, the images are combined with each other using the support vector regression algorithm and new images are created. A multi-stage process has been used to increase the image quality in this research and the pre-processing operation has been carried out in the first stage in order to improve the image quality. Three phases of training, testing, and parameter adjustment have been used after the pre-processing operation in order to increase the image quality. In the training section, the images are first converted to lower levels, and the color segmentation operation takes place at the lower levels. After the image classification operation in terms of color, the gradient is used to extract the image properties. The support vector regression algorithm was used to predict the image pixels, and this algorithm was improved by a meta-heuristic algorithm called whale algorithm. Evaluation parameters including PSNR and SSIM criteria have been used in this research that yielded promising results. تفاصيل المقالة
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  2 - Use whale algorithm and neighborhood search metaheuristics with fuzzy values to solve the location problem
  مهدی فضلی فرزین مدرس خیابانی بهروز دانشیان
  In this paper, a facility location model with fuzzy value parameters based on the meta-heuristic method is investigated and solved. The proposed method and model uses fuzzy values to investigate and solve the problem of location allocation. The hypotheses of the problem أکثر

  In this paper, a facility location model with fuzzy value parameters based on the meta-heuristic method is investigated and solved. The proposed method and model uses fuzzy values to investigate and solve the problem of location allocation. The hypotheses of the problem in question are considered as fuzzy random variables and the capacity of each facility is assumed to be unlimited. This article covers a modern, nature-inspired method called the whale algorithm and the neighborhood search method. The proposed method and related algorithm are tested with practical optimization problems and modeling problems. To evaluate the efficiency and performance of the proposed method, we apply this method to our location models in which fuzzy coefficients are used. The results of numerical optimization show that the proposed method performs better than conventional methods. تفاصيل المقالة