动态环境的人工免疫网络多Agent优化策略

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．

Artificial immune network multi-agent optimization strategy for dynamic environment

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.