| 用改进的人工鱼群算法求解TSP问题 |
| Solving TSP with Improved Artificial Fish Swarm Algorithm |
| 投稿时间：2010-12-25 |
| 中文关键词:组合优化问题 人工鱼群算法 嗅觉 旅行商问题 |
| 英文关键词:combinatorial optimization problem artificial fish swarm algorithm smell traveling salesman problem |
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| 中文摘要: |
| 针对人工鱼群算法在寻优过程中存在的不足，结合嗅觉在自然界鱼类捕食过程中的重要作用，在基本人工鱼群算法的基础上，提出了具有嗅觉特征的人工鱼群算法。最后,利用改进的人工鱼群算法成功解决了旅行商问题，并且通过比较基本人工鱼群算法与改进人工鱼群算法的实验结果，得出结论，改进后的人工鱼群算法在算法搜索时间、全局最优值精确度方面都有了显著的提高。 |
| 英文摘要: |
| ]In view of the weakness of artificial fish swarm algorithm in the process of optimization, an improved artificial swarm algorithm with the characteristics of smell based on artificial fish swarm algorithm is proposed in the dissertation, which integrates the important role of the fish smell in the predatory process. Finally, Traveling Salesman Problem is solved with the improved artificial fish swarm algorithm. Comparing the experiment results of two algorithms, the improved artificial fish swarm algorithm is better in search time and the precision of optimal value than the original artificial fish swarm algorithm. |
| 李跃松,樊金生,张巧迪.用改进的人工鱼群算法求解TSP问题[J].石家庄铁道大学学报(自然科学版),2011,(2):103-. |
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