引导型免疫算法研究

Study on Guiding Immune Algorithm

基于免疫系统机理提出的免疫算法是一种新型的智能系统，在优化计算方面表现出巨大的潜力，具有多样性好、搜索成功率高的优点．但免疫算法在局部搜索中存在一定盲目性，搜索效率不高．本文提出引导型免疫算法，通过增强免疫算法中抗体的社会性，为免疫算法的搜索过程提供引导性，加快算法收敛速度，并对引导型免疫算法中新引入的算法参数进行了深入讨论．算法分析和仿真结果表明，引导型免疫算法在保持算法高搜索成功率的前提下，有效地提高了算法搜索效率．

Immune algorithm is an intelligent system, and shows great potential in optimization. However, as the local search of immune optimizer is of some blindness, its efficiency is limited. Guiding immune optimization algorithm is proposed, which introduces sociality to immune optimization algorithm, and accordingly improves convergence speed. The related parameters are also discussed. Analysis and simulation results show that guiding immune optimizer effectively improves the searching speed of immune algorithm as well as ensures the high succeed probability.