Ant Colony Optimization Improved by Heuristic Methods for Traveling Salesman Problem

• Main Authors: Bai-fong Cheng, 鄭百峰
• Other Authors: Nai-jing Wang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/wv4946

碩士 === 國立臺灣科技大學 === 電機工程系 === 94 === The Traveling Salesman Problems(TSP) are well known as NP-hard problems which are difficult and time-consuming. Ant Colony Optimization(ACO) is a new proposed metaheuristic algorithm that has been successfully applied to solving Combinatorial Optimization Problems(COP). ACO is biologically inspired by observing the behavior of real ants, and it simulates the process of ants searching for food. When ants forage the food, they depend on the amount of pheromone deposited on the traverse path. Although ACO algorithm has very good searching capability in optimization problems, it still has some drawbacks such as stagnation behavior, needing longer computing time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this thesis, we propose two heuristic methods to improve the efficiency of ACO in solving the Traveling Salesman Problem. First, we apply the elitism to the probability candidates for the ants to construct the tours. Second, we are inspired by observing the route maps to develop a heuristic method which makes the edges not cross and shortens the route. In our experimental results, the first method of choosing the elitism list from ten to fifteen percentage of the city size for the ants to forward the next city achieves better performance than ACO under the condition of spending the same time. The second method of the solving crossing routes can be applied well to all scale of data set and only cost a short time to improve.