Improved ant colony algorithm based on natural selection strategy for solving TSP problem
To solve basic ant colony algorithm's drawbacks of low convergence rate,easiness of trapping in local optimal solution,an improved ant colony algorithm based on natural selection was proposed.The improved algorithm employed evolution strategy of survival the fittest in natural lection to enhanc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2013-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.04.020/ |
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| Summary: | To solve basic ant colony algorithm's drawbacks of low convergence rate,easiness of trapping in local optimal solution,an improved ant colony algorithm based on natural selection was proposed.The improved algorithm employed evolution strategy of survival the fittest in natural lection to enhance pheromones in paths whose random evolution factor was bigger than threshold of evolution drift factor in each process of iteration.It could accelerate convergence rate effectively.Besides the introduction of random evolution factor reduced probability of trapping local optimal solution notably.The proposed algorithm was applied to classic TSP problem to find better solution for TSP.Simulation results depict the improved algorithm has better optimal solution and higher convergence rate. |
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| ISSN: | 1000-436X |