| 引用本文: | 史旭华,钱锋.动态环境的人工免疫网络多Agent优化策略[J].控制理论与应用,2011,28(7):921~930.[点击复制] |
| SHI Xu-hua,QIAN Feng.Artificial immune network multi-agent optimization strategy for dynamic environment[J].Control Theory and Technology,2011,28(7):921~930.[点击复制] |
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| 动态环境的人工免疫网络多Agent优化策略 |
| Artificial immune network multi-agent optimization strategy for dynamic environment |
| 摘要点击 1833 全文点击 1049 投稿时间：2010-01-04 修订日期：2010-10-04 |
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| DOI编号 |
| 2011,28(7):921-930 |
| 中文关键词 免疫网络 多Agent 动态环境 优化 |
| 英文关键词 immune network multi-agent dynamic environment optimization |
| 基金项目 国家杰出青年科学基金资助项目(60625302); 国家“973”计划资助项目(2009CB320603); 国家科技支撑计划资助项目(2007BAF22B05); 国家自然科学基金资助项目(20876044); 宁波市自然科学基金资助项目(2011A610173); 浙江省自然科学基金资助项目(Y1090548). |
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| 中文摘要 |
| 基于生物免疫网络的核心思想及多Agent技术, 提出了动态环境下的人工免疫网络多Agent优化策略(Dmaopt-aiNet). 该策略以搜索动态环境中的全局最优解为目标, 引入了邻域克隆选择、邻域竞争和协作操作, 并同时对Agent自信度状态作自动调整, 在优化策略中采用了双重Agent网络结构、双重变异及动态环境检测策略. 理论分析了Dmaopt-aiNet算法具有全局收敛性, 实验结果表明该算法对高维动态优化问题具有较突出的优越性, 能准确定位动态环境下的最优解, 具有较好的搜索效果和效率. |
| 英文摘要 |
| Based on the idea of biological immune network and multi-agent technology, an artificial immune network multi-agent optimization strategy for dynamic environment(Dmaopt-aiNet) is proposed. The strategy with the target of global optimization introduces neighborhood clonal selection, neighborhood competition and neighborhood collaborative operators. Simultaneously, self-confidence of each agent can be automatically adjusted. In the optimizing process, some strategies such as double-agent network structure, double-mutation strategy and dynamic environmental monitoring are involved. Theoretical analysis shows that Dmaopt-aiNet algorithm is global convergence. Experimental results and comparison illustrate that Dmaopt-aiNet in dealing with high-dimensional dynamic optimization problems is more superior and can accurately determines the location of the optimum with good effectiveness and efficiency. |