| 引用本文: | 李中华,张雨浓,谭洪舟,陈卓怡.一类具有精英学习能力的增强型人工免疫网络优化算法[J].控制理论与应用,2009,26(3):283~290.[点击复制] |
| LI Zhong-hua,ZHANG Yu-nong,TAN Hong-Zhou,CHEN Zhuo-yi.An enhanced artificial immune network with elitist-learning capability for optimization problems[J].Control Theory and Technology,2009,26(3):283~290.[点击复制] |
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| 一类具有精英学习能力的增强型人工免疫网络优化算法 |
| An enhanced artificial immune network with elitist-learning capability for optimization problems |
| 摘要点击 1662 全文点击 1360 投稿时间：2008-03-12 修订日期：2009-01-19 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.3.011 |
| 2009,26(3):283-290 |
| 中文关键词 人工免疫系统 精英学习 亲和力学习 微粒群优化 PID控制 |
| 英文关键词 artificial immune network elitist-learning affinity-learning particle swarm optimization PID controller |
| 基金项目 教育部高等学校博士学科点科研基金资助项目(200805581047); 广东省自然科学基金博士启动基金资助项目(8451027501001203/2008–259). |
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| 中文摘要 |
| 提出了一种用于求解优化问题的具有精英学习能力的增强型人工免疫网络(Enhanced aiNet–EL)算法. 该算法集成了亲和力学习和精英学习, 改进了免疫进化的克隆、变异和抑制算子. 通过对两个经典函数的优化实验,结果表明本文提出的Enhanced aiNet–EL算法在最优解质量和收敛速度上都要优于传统aiNet和EaiNet算法. 作为应用实例, 工业PID控制器被用于测试算法的优化性能. 实验所得的阶跃响应表明, 使用Enhanced aiNet-EL得到的系统性能要优于使用其他4种方法得到的系统 |
| 英文摘要 |
| This paper proposes an new enhanced artificial immune network with elitist-learning (Enhanced aiNet-EL) for optimization problems. The proposed new algorithm integrates affinity-learning with elitist-learning and its three immune operators, i.e., cloning, mutation and suppressor. The simulation results on two classical benchmarks indicate that the proposed enhanced aiNet-EL optimization outperforms the traditional aiNet optimization and EaiNet optimization in both the final solution and convergence speed. In applying the proposed algorithm to an industrial PID control system, the step response shows a performance better than those under other four approaches. |