| 摘要: |
| 从理论上分析了影响多级算法性能的因素,并以此为依据构造了求解TSP问题的自适应归约免疫算法.该算法借助归约集的进化使归约集规模自适应增长,归约边的预测精度不断提高,从而提高了算法在归约后找到全局最优解的概率.实验结果表明,该算法比其他算法获得了质量更高的解. |
| 关键词: TSP 人工免疫 克隆选择 自适应归约 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60575037, 60502043 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2006AA01Z107 (国家高技术研究发展计划(863)) |
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| Immune Algorithm with Selfadaptive Reduction for Large-Scale TSP |
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QI Yu-Tao,LIU Fang,JIAO Li-Cheng
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| Abstract: |
| Analysises on factors which impact the performance of the multi level algorithms have been made, and on the basis of which an immune algorithm with selfadaptive reduction has been proposed for the TSP problems. By using an evolutionary reduction set, the proposed algorithm refines the reduction edges which gradually increase in the number and enhance in the forecasting accuracy. As a result, the probability that the refined algorithm finds the global optimal solution can be improved. Experimental results show that the proposed algorithm can achieve better solutions than other approaches. |
| Key words: TSP artificial immune clonal selection selfadaptive reduction |
