An Improved Ant Colony Algorithm and Simulation
We demonstrate a novel Ant Colony System with dynamically varied parameters and a penalty-reward function, which is based on the Basic Ant System (BAS) algorithm, also presented is its application to solving complex TSP problem. Our new algorithm has two important features, the first: a perturbation factor formulated by inverse exponent penalty-reward function is developed; the second: a corresponding transition strategy with random selection is designed. Numerical simulation demonstrates that our new algorithm has much higher convergence speed and stability than BAS algorithm, and brings along good effects of reducing CPU time, and preventing search from being in stagnation behavior.
Ant Colony Pheromone Penalty-Reward Function TSP
Li Xin Yu Datai Qin Jin
Information Engineering School, University of Science and Technology in Beijing, Beijing 100083, Chi Information Engineering School, University of Science and Technology in Beijing, Beijing 100083, Chi Information Engineering School, University of Science and Technology in Beijing, Beijing 100083, Chi
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2838-2841
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）