grey wolf optimization algorithm

Edge detection as a pre-processing is the basis of image segmentation, feature extraction, and object recognition processes. So far, many edge detection algorithms have been introduced. However, even the best edge detectors lose their effectiveness in the presence of noise. Therefore, the correct detection of edges in the noisy image is still one of the challenging issues in the image processing. Various algorithms have been presented to solve this challenge, of which the meta-heuristic optimization algorithms are examples. In this research, a method is proposed for the edge detection of the noisy images based on the grey wolf optimization algorithm whose objective function is combining of the homogeneity factor, uniformity factor, and Kirsch edge detector masks. The proposed method has been simulated on the BSDS500 database including 500 images along with their Ground Truth images. In the simulation, two noises of the Gaussian, and salt-and-pepper have been applied. The evaluation has been done according to the mean square error, peak signal-to-noise ratio, precision, F-score, and accuracy criteria. The simulation results show the mean accuracy of the proposed method on the BSDS500 database images has achieved respectively 0.915, and 0.898 with the salt-and-pepper noise with a density of 0.01, and the Gaussian noise with a zero mean, and a variance of 0.01. The average execution time of the proposed method with 80 runs for each image of the BSDS500 database has also obtained at 50.01, and 50.02 seconds in the presence of the mentioned noises respectively.

One of the most important issues of concern to human societies in recent years is urban waste management that is one of the main requirements of each city, and without any notice of it, it can be problematic for it and even residents of the surrounding villages. Urban areas generate the highest amount of waste and consequently, they need an efficient system for collecting and disposing of waste where its determination and stabilization is very difficult and costly. In this regard, this paper examines the multi-trip vehicle routing problem with time windows specific to the urban waste collection, with the goal is to minimize the total cost including routing costs, the earliness and lateness penalty cost for violating the service time windows and the usage costs of vehicles. To solve the problem in practical dimensions, grey wolf optimization (GWO) algorithm is developed where its performance is tested compared to CPLEX solver of GAMS and simulated annealing (SA) algorithm. The obtained results demonstrate that the proposed GWO have an acceptable performance to generate high-quality solutions. Finally, to study the behavior of the objective function versus the real-world demand parameter changes, a sensitivity analysis is performed on this parameter and the optimal management policy is analyzed.ed.