动态环境下自适应免疫优化理论与算法研究

Many real-world optimization problems are changing over time, so being able to optimize in a dynamic environment is important. Immune-based algorithms are effective tools to address optimization problems in dynamic environment due to their inspiration from biological immune system, which have always been adaptive to changing environments. In this project, three strategies, which are adaptive immune recognition and reaction strategy to changing environment, and multi-population mechanism based on immune network and information transfer, and enhanced local search strategy based on building immune operators and mining intermediate information, are developed based on biomimetic principle through mining immune detection, clonal selection, immune response, immune regulation and immune memory from biological immune theory. Through combining machine learning with data mining strategies, methods of adaptively recognizing and reacting to changing environment, and effectively balancing the diversity and convergence of the searching population, and improving search ability are explored. The project will propose an adaptive immune-based optimization theory under dynamic environment, and correspondingly construct algorithms for a large number of benchmark functions and practical problems, provide the theory basis and the key technology for solving complex problems effectively.

现实世界中的许多优化问题随时间发生变化，因此动态环境下的优化研究具有重要意义。免疫优化算法由于受动态环境自适应的生物免疫系统启发，成为求解动态环境下优化问题的有效手段。本项目以生物免疫理论为基础，通过挖掘免疫检测、克隆选择、免疫应答、免疫调节和免疫记忆功能，并利用仿生原理构建免疫自适应变化识别和应对策略，基于免疫网络和信息传递的多种群机制，和通过构建免疫算子和中间信息挖掘的局部强化搜索策略。通过与机器学习和数据挖掘关键技术的有效结合，探索自适应环境变化识别和应对，有效平衡搜索种群的多样性与收敛性，以及提高种群搜索能力的方法。项目将形成一套动态环境下自适应免疫优化理论，并构造相应的算法应用于大量测试问题和实际问题中，为高效解决复杂实际优化问题提供理论依据和核心技术。

本项目主要围绕动态环境下优化问题的免疫优化理论和算法展开，探索动态环境下自适应免疫优化中若干为解决的关键科学问题。获得的主要成果如下：1）动态环境下新的免疫优化算法的设计，提出了免疫启发式优化算法、基于聚类的克隆选择算法等，在与现有先进算法的对比上表现出很好的竞争力。2）免疫算子的构造和改进，提出了基于基因重组的新颖克隆选择算法、多项学习免疫优化算法、基于基因重组和改进的超变异算子的混合克隆选择算法等，通过新的免疫算子的构造与改进提升了经典克隆选择算法的性能。3）求解VRPSDP的变邻域混合遗传算法，针对卸装一体化车辆路径问题，提出一种结合变邻域下降搜索和遗传算法的混合启发式算法，实现了在平均路径长度和最优解质量上的提升。4）对新的应用领域，包括生物信息学，机器学习，图像和视频处理进行了初步探索，实现了对蛋白质亚叶绿体多位置预测的多标记分类算法、基于HEVC的三维视频编码快速算法和多传感器图像融合等领域的应用和探索。本项研究开展以来，已在国内外重要期刊上发表论文14篇，其中SCI检索6篇；软件著作权1项；申请专利4项，培养硕士研究生10人，毕业7人。上述研究成果，为高效解决复杂实际优化问题提供提供了基本理论和技术基础。