多智能算法生命周期融合优化模型与方法研究及在微波滤波器优化设计中的应用

This project mainly studies the multi-algorithm fusion optimization from the following four aspects: biological modeling, theoretical analysis and engineering application. For biological modeling, according to Life-cycle evolution and complex system theory, optimization fusion model of multiple intelligent algorithms will be constructed, which dynamically adjusts the sizes of sub-populations and adaptively selects the suitable algorithm. For theoretical analysis, multiple intelligent algorithms fusion optimization model framework, evolution process and dynamic characteristics should be thoroughly researched by using the methods of statistics and random process. Convergence analysis, life cycle, the topological structure and performance analysis of the operators of multi-method coordination optimization with dynamical population sizes are the key research contents. For engineering application, the multiple intelligent model is applied in the optimization field multi-evanescent-mode microwave device with single-objective, multi-objective optimization and mixed variables. The innovation of this paper lies in: it provides a new way to solve the no free lunch theorem in the field of intelligent computing, which can deepen and enrich the existing intelligent optimization theory, and further drives biological heuristic algorithm in the further promotion and application of practical complex engineering fields.

本课题从生物建模、理论分析和实际工程应用三个层面展开对多智能算法融合优化的系统研究。在生物建模方面，基于生命周期演化与复杂系统理论，构建能够在运行时动态调整种群规模、自适应选择最优进化模式的多算法智能融合优化模型；在理论分析方面，采用统计学和随机过程等方法，研究多智能算法融合优化的模型框架与动力特性，特别是动态群体下多算法协同优化的收敛性分析、生命周期、拓扑结构与学习算子的性能分析；在工程应用方面，在具有单目标、多目标、混合变量特性的消逝模微波器件优化设计领域进行验证研究。本课题的创新点在于，研究成果为突破智能计算领域的没有免费午餐定理提供了新的解决途径，从而深化和丰富已有的智能优化理论，驱动生物启发式算法在实际复杂工程领域的进一步推广和应用。

本课题从生物建模、理论分析和实际工程应用三个层面展开对多智能算法融合优化的系统研究。在生物建模方面，基于生命周期演化与复杂系统理论，构建能够在运行时动态调整种群规模、自适应选择最优进化模式的多算法智能融合优化模型；在理论分析方面，采用统计学和随机过程等方法，研究多智能算法融合优化的模型框架与动力特性，特别是动态群体下多算法协同优化的收敛性分析、生命周期、拓扑结构与学习算子的性能分析；在工程应用方面，在具有单目标、多目标、混合变量特性的消逝模微波器件优化设计领域进行验证研究。本课题的创新点在于，研究成果为突破智能计算领域的没有免费午餐定理提供了新的解决途径，从而深化和丰富已有的智能优化理论，驱动生物启发式算法在实际复杂工程领域的进一步推广和应用。