[PDF] Top 20 A Comparative Study Of Nearest Neighbour Algorithm and Genetic Algorithm in Solving Travelling Salesman Problem

... The Nearest Neighbor algorithm was one of the first algorithms used to find a solution for the travelling salesman ...this problem, the salesman starts in a random city and ... See full document

... These genetic algorithms are comparatively fresh examples in the field of artificial intelligence, which work on the typical principles of natural ...of Genetic Algorithms has pushed them as one of the most ... See full document

... of genetic algorithm. One of the methods is Memetic algorithm that has been proposed ...Initially nearest neighbor heuristic is used to generate initial population, after which 2-opt local ... See full document

... optimized genetic algorithm using hybrid initial population generation is ...proposed algorithm makes the initial population ...and nearest neighbour ...(b) Nearest ... See full document

... knowledge-augmented genetic operators which guide the genetic algorithm more directly towards better quality of the population but are not trapped in local optima ...The algorithm applies a ... See full document

... Abstract—Travelling Salesman Problem (TSP) is one of the oldest combinatorial problems that are classified as ...Hence, solving this type of problems requires a tremendous number of ... See full document

... TSP problem is to find the shortest path of tour starting from a given city, passing once an n city and returning back to the city of take ...research problem lies in what order should a traveling ... See full document

... The results in Table 2 indicate that the average best tour lengths and the final tour lengths found by HGA are better than those obtained the GA [9] on the majority of in- stances. Meanwhile, the relative error of the ... See full document

... the study proposed by Philip et ...search algorithm for solving TSP through generating a present number of random tours and then improving the population until its stop condition is ... See full document

... Presented crossover in [14] is unnamed and operates heuristically so here we name it UHX (unnamed heuristic crossover). UHX starts with random current city and copies it to child. It puts four pointers to right and left ... See full document

... representing Genetic algorithm shows that time complexity increases exponentially with increasing number of ...the algorithm takes a lot of time to complete (about 20000ms or ...In genetic ... See full document

... and study of systems that learns from ...for solving NP problems more ...(MACO) algorithm which uses the peculiarity of Elitist Ant System (EAS) and Ant Colony System ... See full document

... ACO algorithm give the elementary structure for the design of MACO algorithm (refer ...the algorithm and to increase its exploration for the arcs that have not been visited yet, a local pheromone ... See full document

... (CPGI) Algorithm is implemented [4] using convex hull as tool to find the shortest cycle to a ...and genetic algorithm and the performance is ...exact algorithm which gave the base to solve ... See full document

... From October 2018, I began to think about this, and a fresh idea suddenly appeared when I deleted the longest one among the total 15 paths (which connect 6 cities separately) by a pen on a paper (see Figure 1). Since the ... See full document

... The genetic algorithm when applied tries to find many solutions and consider anly the optimal one,so they are lttle more expensive as compared to OR ... See full document

... (FS) algorithm is a process of choosing a reduced related features that improves classification by identifying for the feature subset, from the database of original features according to a given classification ... See full document

... This leads us to the first method of encoding a tour of the travelling salesman problem. We do have a graph such as the one described above and we can encode it in the same way, only our matrix will ... See full document

... colony algorithm are, the number of food sources which is equal to the number of employed/onlooker bees (CS-Colony Size), the working to onlooker bee rate, the value of the limit (L) and the number of cycles or ... See full document

... Here high population size is not set and parallel version of algorithms to obtain exact solution is not considered, as was done by Whitley et al. [7]. Also, highest probability of crossover is used to show the exact ... See full document