| 引用本文: | 戴朝华, 朱云芳, 陈维荣.云自适应遗传算法[J].控制理论与应用,2007,24(4):646~650.[点击复制] |
| DAI Chao-hua, ZHU Yun-fang, CHEN Wei-rong.Adaptive genetic algorithm based on cloud theory[J].Control Theory and Technology,2007,24(4):646~650.[点击复制] |
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| 云自适应遗传算法 |
| Adaptive genetic algorithm based on cloud theory |
| 摘要点击 2043 全文点击 1933 投稿时间：2005-08-07 修订日期：2006-07-12 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.4.026 |
| 2007,24(4):646-650 |
| 中文关键词 自适应遗传算法 云理论 云自适应遗传算法 |
| 英文关键词 adaptive genetic algorithm cloud theory cloud-based adaptive genetic algorithm |
| 基金项目 西南交通大学博士生创新基金资助项目(2007-3). |
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| 中文摘要 |
| 传统自适应遗传算法(AGA)虽能有效提高收敛速度, 却难以增强算法的鲁棒性. 以当代种群平均适应度为期望Ex, 根据云模型“3En”规则确定熵En, 由X条件云发生器自适应调整交叉变异概率, 提出云自适应遗传算法(CAGA). 由于云模型云滴具有随机性和稳定倾向性特点, 使交叉变异概率值既具有传统AGA的趋势性, 满足快速寻优能力;又具有随机性, 且当种群适应度最大时并非绝对的零概率值, 有利于提高种群多样性, 从而大大改善避免陷入局部最优的能力. 典型函数优化实验表明, 与标准遗传算法(SGA)和AGA相比, CAGA具有更好的收敛速度和鲁棒性. |
| 英文摘要 |
| Traditional adaptive genetic algorithm (AGA) has higher convergence speed, but it still easily gets stuck at a local optimum. A novel algorithm called cloud-based adaptive genetic algorithm (CAGA) is introduced, which is based on cloud model with the properties of randomness and stable tendency. In the CAGA, the probabilities of crossover and mutation, pc and pm , are adaptively varied depending on X-conditional cloud generator. In X-conditional cloud generator, the average fitness of the current population is used as expected value Ex, and entropy En is specified based on the “3En”rule of cloud model. CAGA can improve its convergence capacity because of the stable tendency of cloud model. Meanwhile, it can remarkably avoid a local minimum using the randomness of cloud model to maintain diversity in the population. Finally, the performance of the CAGA is compared with that of the standard GA (SGA) and AGA in optimizing several nontrivial multimodal functions with varying degrees of complexity. In all cases studied, CAGA is greatly superior to SGA and AGA in terms of robustness and efficiency. |
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