Combinatorial optimization problems (COPs) are the most important class of optimization problems, with great practical significance. This class is concerned with identifying the best solution from a discrete set of all available options. The transportation (routing) and distribution (scheduling) systems are considered the most challenging optimization examples of the COPs. Given the importance of routing and scheduling problems, many methods have been proposed to address them. These methods can be categorized into traditional (exact and metaheuristics (MHs) methods) and machine learning (ML) methods. ML methods have been proposed to overcome the problems that traditional methods suffer from, especially high computational time and dependence on the knowledge of experts. Recently, ML methods and MHs have been combined to tackle the COPs, and then the learnheuristics term emerged. This combination aims to guide the MHs toward an efficient, effective, and robust search and improve their performance in terms of solution quality. This work reviews the publications in which the collaboration between MHs and ML has been utilized to propose a guideline for the researchers to put forward new algorithms that have a good ability to solve routing and scheduling problems.

This paper suggested a de-noising algorithm used in grayscale images. As long as the noisy image does not give the desired view of its features, de-noising is required. The algorithm is based on block matching and wavelet transformation. Euclidean distance for blocks similarity is exploited, which demonstrate more accurate in finding similar blocks depending on soft thresholding. Regarding wavelet transform, a combine of hard thresholding is performed for HH and LH sub-bands while soft thresholding is used in LL and HL sub-bands of the decomposed images. Three types of noise is encountered: Gaussian noise, salt & pepper noise and speckle noise. The measurements are employed to evaluate our work is MSE and PSNR and SSIM. Finally a comparison of the results shows that our method outperforms traditional wavelet using hard or soft thresholding.

In this paper a comparison between Harris and FAST (Features from Accelerated Segment Test)corner detection has been presented that is track features within a noisy images where it is a challenging task in the field of image processing. As long as noisy image does not give the desired results in corner detection, de-noising is required. Adaptive non-local means are applied for salt and pepper, Gaussian and speckle noise before applying corner detection. FAST corner detection outperformed Harris in detecting actual and exact number of corners and more robust to noise than Harris, the obtained results shown a good satisfaction in this study especially in the numbers of real detected corners.

This paper suggested a de-noising algorithm used in grayscale images. As long as the noisy image does not give the desired view of its features, de-noising is required. The algorithm is based on block matching and wavelet transformation. Euclidean distance for blocks similarity is exploited, which demonstrate more accurate in finding similar blocks depending on soft thresholding. Regarding wavelet transform, a combine of hard thresholding is performed for HH and LH sub-bands while soft thresholding is used in LL and HL sub-bands of the decomposed images. Three types of noise is encountered: Gaussian noise, salt & pepper noise and speckle noise. The measurements are employed to evaluate our work is MSE and PSNR and SSIM. Finally a comparison of the results shows that our method outperforms traditional wavelet using hard or soft thresholding.