جستجوی مقالات مرتبط با کلیدواژه « greedy algorithm » در نشریات گروه « عمران »

The railway network design problem deals with how to allocate a limited budget for the development of the railway network infrastructure. Among the objectives used in this problem, we can mention the minimization of the total travel time in the network, the minimization of the development costs, or the maximization of the income. Considering that the two groups of decision-makers are influential in this problem, the general form of the network design problem will be a Bi-level problem that belongs to the category of NP-Hard problems, which cannot be solved using precise optimization methods in even small scales. In this article, an innovative approximate greedy algorithm is presented to solve the problem of rail network design. In this algorithm, the focus is on developing blocks of the rail network that have the greatest effect in reducing the average travel time in the network. The process of adding blocks in the network continues until the entire level of incoming demand can be transferred from the network. This algorithm was implemented with Java language and railway of Iran has been selected as a case study. The results show that with the development of the network up to the demand of 57 million tons, the average travel time will reach its lowest value; and for the higher demand values, the average travel time will increase.

Transportation issues are categorized into three strategic, tactical, and operational levels, each of which has a different level of influence, required budget, decision makers, and time period. The issue of developing the rail transportation network is one of the key issues at the strategic level. In short, network design deals with the solution of allocating a limited budget to a feasible subset of the set of projects, in such a way that specific goals: such as minimizing the total travel time in the network, the developing costs of the network, maximizing revenue from freight transportation, or maximizing the attraction of freight demand to the rail mode should be taken into account. In this issue, two stakeholders are considered. On one side, the operators make the macro decisions to meet the criteria; such as maximization of benefit, maximization of travel coverage, minimization of development costs, minimization of casualties and minimization of total travel time. On the other side users who try to maximize their benefits such as finding the shortest route through the network.The general form of the network design problem is a two-level problem in the category of NP-hard problems, which is difficult to solve in even small scales. To solve this problem, the solution algorithms are classified into two general categories: exact and approximate. The exact solution algorithm give the best global solution among the possible solutions, they are so-called intractable in terms of memory usage and solution time with the increase in the size of the problem. Therefore, the second category of so-called approximate algorithms was presented to solve network design problem. Greedy algorithms are classified in the category of approximate algorithms. In the greedy algorithm, reaching the goal in each step is independent of the previous step. That is, at each step to reach the solution, regardless of what choices was made in the previous stages.In this article, the greedy algorithm is presented to solve the problem of network design trying to reduce network development costs. The proposed algorithm is designed to develop the blocks with priority of the lowest cost, and this process continues until the entire level of incoming demand can be transferred through the network. This algorithm is implemented with Java language and the railway of Iran is used as a case study. Considering the nature of two objectives in the problem, freight demand passing through and development cost in the network, "pseudo-pareto" solutions with different percentages of the importance of two mentioned objectives are discussed. The analysis has shown that with the increasing importance of the development cost, fewer blocks are developed and as a result, less demand is passed through the network. Also, with the increasing importance of freight demand, the algorithm leads to solutions that have caused extensive development in the network. The proposed greedy algorithm has a light computational load, and it achieves its solutions in less than 1 hour. Also, the algorithm is implemented for two demand levels of 70 million tons per year and 110 million tons per year and the results are analyzed.

Camouflage is the art of disguising or blending objects into a natural background so as to make them more difficult for viewers to see. Traditional camouflage is usually based on the designer's experience and includes macro patterns spots and stripes irregular whose outlines or boundaries are sharp and are easier to see. To overcome this main drawback, digital camouflage combines macro and micro patterns with computer assistance. Most works related to the digital camouflage are in the field of color pattern design. However, designing a suitable color pattern that can best match the target in terms of shape and color characteristics with the background is a major challenge in the field of digital camouflage. Nowadays, the digital camouflage is based on the principles of visual psychology and uses digital image processing techniques to characterize background features. The common digital camouflage techniques are based on the fuzzy, the neural network and the greedy methods. The main problem to use these methods is that the number of main colors is chosen manually or experimentally, while it is different in each image. Therefore, the optimal colors cannot be obtained for appropriate blending targets into their backgrounds.
The main objective of this study is to provide a novel method of designing a digital template which automatically extracts the number of original colors based on the specific features of each image. The proposed method is based on the conventional greedy algorithm. The greedy algorithm tries to minimize the difference between the shape perceived by the viewer and the shape patterned on the target. The proposed method first uses the minimum description length (MDL) criterion for determining the number of optimum clusters of the image. Then, it uses the well-known K-means clustering method to extract the original colors from the image. Finally, the proposed method uses the greedy algorithm to obtain an optimal distribution or arrangement of the combination of pattern templates stored in a database.
In this study, the proposed method is compared to the color similarity algorithm proposed by yang and Yin (2015). The quantitative and qualitative assessments of both the methods are based on the saliency map, which is a common criterion for the camouflage assessment. The saliency map is originally intended to model covert attention. It attaches a value to each location in the visual field given the visual input and the current task, with regions of higher salience being more likely to be fixated.
For our comparison, 11 different images captured in different conditions have been used in this study. The images used are in different times (spring, summer, autumn, and winter seasons) and different location (desert, forest, sea, urban, etc.) conditions. Experimental results show that, the mean value of the saliency measure in the 11 images are, respectively, 53% and 42% for the color similarity algorithm method and the proposed method. This indicates that the proposed method is superior to the color similarity algorithm for distinguishing the targets in their backgrounds.