Using Hybrid of Genetic Algorithm and Ant Colony Optimization to Solve Travelling Salesman Problem

• Main Authors: Yi-Chiang Chiu, 邱以強
• Other Authors: Shi-Jim Yen
• Format: Others
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/8hf4g3

碩士 === 國立東華大學 === 資訊工程學系 === 100 === Travelling Salesman Problem (TSP) is a typical problem of combinatorial optimization problem (COP). It has proven to be an NP-complete problem, so the computing time will grow exponentially when the problem size increases. Recently, biological characteristics have inspired various heuristic algorithms like Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Genetic Algorithm (GA) and Ant Colony Optimization (ACO) are both Meta-heuristics and have been successfully applied to several combinatorial optimization problem (COP). They use the characteristic of biological evolutions or organism forage to find the global optimal solution of TSP. The performance is limited for solving TSP by Genetic Algorithm. In that thesis, a hybrid method of GA and ACO is proposed for TSP to surpass other methods.