中文摘要：

为提高人工免疫优化算法的优化能力,将非基因信息的记忆机制引入智能算法,提出了一种基于非基因信息的免疫记忆优化算法.算法通过对先验知识（非基因信息）的短期记忆并指导后续进化,降低盲目搜索和重复搜索,增加了搜索的智能性和有效性.结合标准测试函数在高维下的仿真实验表明,与其他智能算法相比,新算法在收敛速度、收敛精度和全局收敛性方面均优于对比算法.此外,在超高维下的仿真结果表明新算法具有在大规模维度解空间中的全局寻优能力.

英文摘要：

In order to improve the ability to optimize artificial immune algorithm, the memory mechanism of non-genetic infor- mation is introduced into optimization algorithm. An immune memory optimization algorithm based on the non-genetic information is proposed. Emulating human society education and experiential inheritance mechanism, the algorithm takes, stores and uses non genetic information in the evolutionary process of the population. By setting up a separate memory base, the algorithm stores non genetic information, and guides the subsequent search process. The algorithm uses the short-term memory of the prior knowledge and guides the subsequent evolution, which can increase the intelligence of search and reduce the blind search and repeat the search. The immune memory optimization algorithm based on the non-genetic information includes key operators： muta- tion operator, crossover operator and complement operator. The mutation operator is able to efficiently use non genetic information of grandparents to search, which can speed up the local search efficiency. In addition, the threshold to control the search depth of single dimension can avoid falling into local optimal solution making the evolutionary stand- still. Through calculating comprehensive information about contemporary populations of all antibodies, complementary operator produces new antibodies containing excellent gene fragment in the global solution space. With small probabil- ity rules, crossover operator happens in an interval of multi generation, choosing the optimal antibody and a random antibody to exchange information about a single dimension. Crossover operator and complement operator can both be conducive to jumping out of optimal location. In simulation experiment, the immune memory optimization algorithm based on the non-genetic information uses four standard test functions： Ackley function, Griewank function, Rastrigin function, and transformed Rastrigin func- tion. In order to better compare with contrast algorithm, in the case of