| 基于交互式的并行蚁群优化算法 |
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| 引用本文： | 孟鑫,安毅生,张志明. 基于交互式的并行蚁群优化算法[J]. 计算机系统应用, 2015, 24(2): 224-228 |
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| 作者姓名： | 孟鑫 安毅生 张志明 |
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| 作者单位： | 长安大学 信息工程学院,西安,710064 |
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| 摘 要： | 传统蚁群优化算法研究已经取得了很多重要的成果,但是在解决大规模组合优化问题时仍存在早熟收敛,搜索时间长等缺点.为此,将邻域搜索技术与蚁群优化算法进行融合,提出一种新的并行蚁群优化算法,实验结果表明,在解决大规模TSP问题时,该算法求解质量和稳定性更好,在短时间内即可得到较高质量的解.
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| 关 键 词： | 组合优化问题 邻域搜索技术 并行蚁群优化算法 邻域搜索 TSP问题 |
| 收稿时间： | 2014-06-13 |
| 修稿时间： | 2014-07-31 |
Interactive-Based Parallel Ant Colony Optimization |
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| MENG Xin,AN Yi-Sheng and ZHANG Zhi-Ming. Interactive-Based Parallel Ant Colony Optimization[J]. Computer Systems& Applications, 2015, 24(2): 224-228 |
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| Authors: | MENG Xin AN Yi-Sheng ZHANG Zhi-Ming |
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| Affiliation: | College of Information Engineering, Chang'an University, Xi'an 710064, China;College of Information Engineering, Chang'an University, Xi'an 710064, China;College of Information Engineering, Chang'an University, Xi'an 710064, China |
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| Abstract: | Although a lot of important results are achieved in the research of traditional ant colony optimization, and there are many shortcomings in solving large-scale combination optimization problems, such as premature convergence and time consuming. Therefore, a parallelization of ant colony algorithm based on the combination of neighborhood search algorithm and ant colony optimization is proposed and realized. The results of experiment show that the parallel algorithm has much higher quality and stability than that of traditional serial ant colony optimization for solving large-scale TSP problems. |
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| Keywords: | combination optimization problems neighborhood search algorithm parallel ant colony optimization neighborhood search TSP problems |
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