| 基于启发式变异的蚁群算法 |
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| 引用本文： | 龚本灿,李腊元.基于启发式变异的蚁群算法[J].计算机工程,2008,34(8):35-37. |
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| 作者姓名： | 龚本灿 李腊元 |
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| 作者单位： | 武汉理工大学计算机科学与技术学院,武汉,430063 |
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| 摘 要： | 提出一种基于启发式变异的蚁群算法，结合传统蚁群算法和遗传变异算法的优点，利用蚁群算法找到一条全局近优解，采用启发式变异进行路径优化，并将优化信息以信息素的方式传递给下一代，从而快速得到全局最优解。以旅行商问题为例进行仿真实验，结果表明该算法比其他同类算法具有更好的性能。
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| 关 键 词： | 蚁群算法 启发式变异 旅行商问题 |
| 文章编号： | 1000-3428(2008)08-0035-03 |
| 修稿时间： | 2007年4月15日 |
Ant Colony Algorithm Based on Heuristic Mutation |
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| GONG Ben-can,LI La-yuan.Ant Colony Algorithm Based on Heuristic Mutation[J].Computer Engineering,2008,34(8):35-37. |
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| Authors: | GONG Ben-can LI La-yuan |
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| Affiliation: | (College of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063) |
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| Abstract: | This paper proposes an Ant Colony Algorithm based on Heuristic Mutation(ACAHM), combining with the advantage of the traditional Ant Colony Algorithm(ACA) and the genetic mutation algorithm. ACA is used to find a globally near optimal solution, and then it is optimized by the heuristic mutation. The optimized route information is passed to the next generation with the pheromone to quickly get a globally optimal solution. Simulation results for solving the Traveling Salesman Problem(TSP) show that ACAHM is superior to other congeneric algorithms in terms of solution quality and computation speed. |
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| Keywords: | ant colony algorithm heuristic mutation Traveling Salesman Problem(TSP) |
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