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本文在深入分析现有人工免疫算法模型优缺点的基础上,提出了一种基于免疫记忆机制的改进人工免疫算法模型ARTIA.该模型融合了由生物免疫系统启发而来的免疫记忆机制,包括联想记忆和迭代记忆两种,采用了多种策略以保持群体多样性,进而在数值试验的基础上对ARTIA算法模型的性能进行了分析和讨论.最后通过本质上可以归结为旅行商问题(TSP)的多目标组合优化工程实例——岩石钻孔机路径选择问题,验证了该算法的有效性.结论部分对全文作了总结并对今后研究工作进行了展望.
Based on the deep analysis of the advantages and disadvantages of the existing artificial immune algorithm models, this paper proposes an improved artificial immune algorithm model ARTIA based on immune memory mechanism, which integrates the immune memory mechanism inspired by the biological immune system, including Lenovo Memory and iterative memory, a number of strategies are adopted to maintain the population diversity, and then the performance of the ARTIA algorithm model is analyzed and discussed on the basis of numerical experiments.Finally, through the theory of traveling salesman problem (TSP) The multi-objective combinatorial optimization project example - rock drilling machine path selection problem, validates the effectiveness of the algorithm.The conclusion part summarizes the full text and prospects for future research work.