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INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as subproblems in many fields apart from computer science and also in the field of computer science.OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant ColonyOptimization.METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP).The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, itsmean, median, variance and the standard deviation.RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and thedeviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have beenused.CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results areanalysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has alsobeen discussed along with the experimental results.

Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate MDVRP, but none of the existing technique hasimproved the fitness of the solution at the time of initial population generation. This motivates to propose an enhanced ODV based population initialization for Genetic Algorithm (GA) to solve MDVRP effectively. The Ordered Distance Vector (ODV) based population seeding method is a current and effective population initialization method for Genetic Algorithm to produce an early population with quality, individual diversity and randomness. In the proposed model, the customers are first grouped based on distance to their nearest depots and then routes are scheduled and optimized usingenhanced ODV based GA. The experiments are performed based on different types of instances of Cordeau. From the experimental results, it is very clear that the proposed technique outperforms the existing techniques in terms of convergence rate, error rate and convergence diversity.

The usage of the internet has increased rapidly from the past few years. Thus it automatically increases the internet traffic. In the internet, a web server always responds to the client or web browser's requests. The major feature of a web server is to be available, scalable and predictable. So the core part to be concentrated is web server load balancing. Load balancing plays a vital role in allocating the jobs to the web server based on its status. There are various policies available for web server load balancing. Each of these policies came into existence based on the certain needs. In this paper, we have examined the latest policies of web server load balancing.

Edge detection is an essential computer vision phenomenon that is useful for contour detection and in turn it is useful for acquiring vital information. There are a variety of images, including medical, satellite, industrial and general-purpose images. An X-ray is a form of medical imaging that is created by transmitting electronic radiation into a patient's body to get an image of the human body's internal structures. This allows orthopedic surgeons and radiologists to make more accurate diagnoses of illness. Due to its versatility, soft computing approaches are regarded as effective edge detection tools. This study provides a novel fuzzy logic-based technique for edge detection in which the quality of edges is governed by a sharpening-guided filter. The proposed model holds four different patterns for extracting the edges from an image namely 2 × 2 mask, 3 × 2 mask, 2 × 3 mask and 3 × 3 mask. These patterns of masks target the edge pixels of an image along with the other pixels. The proposed model provides an enhanced approach for detecting edges in X-ray pictures of humans. Various statistical measurements are used to evaluate the proposed model to prove its significance. By adjusting the smoothing parameters appropriately, it has been discovered that the detected edges are significantly enhanced. The proposed model is implemented and evaluated under four different metrics namely Correlation Coefficient, Figure of Merit, Structure Similarity Image Metrics and Euclidian Distance. The proposed model results are compared five different state-of-the-art algorithms to prove the significance, and the result shows that the proposed model identifies the edge in a well-structured manner. [ABSTRACT FROM AUTHOR]