| 改进的遗传蚁群混合算法在 TSP 中的应用 |
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| 引用本文： | 蒋腾旭.改进的遗传蚁群混合算法在 TSP 中的应用[J].计算机与现代化,2013(12):30-33. |
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| 作者姓名： | 蒋腾旭 |
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| 作者单位： | 九江职业大学,江西九江332000 |
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| 基金项目： | 江西省科技支撑计划项目（20112BBE50044） |
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| 摘 要： | 针对遗传算法和蚁群算法的不足，提出一种改进的遗传蚁群混合算法。该混合算法通过判定最优解的改良情况，将遗传算法和蚁群算法动态串行融合，以充分利用遗传算法的全局搜索能力和蚁群算法的正反馈机制。同时，依据信息素在正反馈过程中的重要作用，提出一种改进的带奖惩项的信息素更新机制。仿真计算结果表明，本文提出的混合算法在求解TSP方面，收敛速度和求解质量均较传统的遗传算法及蚁群算法要好。
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| 关 键 词： | 遗传算法 蚁群算法 串行融合 改良情况 奖惩项 信息素更新 |
Application of Improved Genetic Ant Colony Hybrid Algorithm in TSP |
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| JIANG Teng-xu.Application of Improved Genetic Ant Colony Hybrid Algorithm in TSP[J].Computer and Modernization,2013(12):30-33. |
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| Authors: | JIANG Teng-xu |
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| Affiliation: | JIANG Teng-xu (Jiujiang Vocational University, Jiujiang 332000, China) |
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| Abstract: | Aimed at the shortcomings of genetic algorithm （ GA） and ant colony algorithm （ ACA） , an improved genetic ant colo-ny hybrid algorithm is presented .By determining the improved situation of the optimal solution , the hybird algorithm actualizes dynamic serial fusion for GA and ACA , which makes full use of global search ability of GA and positive feedback mechanism of ACA.Meanwhile , according as the importance of pheromone in positive feedback process , an improved pheromone update mech-anism with encouragement or penalty item is proposed .Computing simulation examples show the hybrid algorithm is of much high-er convergence speed and much better quality of solutions than that of classical GA or ACA . |
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| Keywords: | genetic algorithm ant colony algorithm serial fusion improved situation encouragement or penalty item phero-mone update |
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