基于复杂系统生命周期演化的菌群优化模型、算法及在RFID网络规划中的应用

Nature is an inexhaustible source of inspiration for innovative, intelligent optimization methods. These methods, which are developed based on the intelligent biological behaviors, is the research hotspot of intelligent science. This project studies the optimization model and algortpithms of intelligent bacterial behaviors from three aspects, namely the biological modeling, algorithm design, and engineering application. In biological modeling, in order to build bacterial life-cycle optimization model, this research focus on the abstraction, modeling and simulation of individual bacterial chemotaxis, the exchange of information between individuals, and colony level evolution; in algorithm design, in order to design a series of new dynamic, multi-objective, discrete bacterial optimization algorithms, in-depth study will be conduct on self- replication, extinction, and migration to maintain the diversity of population, adaptive search strategy that balances the global exploration and local exploitation, and the cooperative coevolution strategy based on the division of tasks and collaboration; in engineering application, this study will build large-scale RFID network dynamic and multi-objective planning model, and analyze the convergence, validity and feasibility of the algorithm proposed by solving the model. The project will combine microbial systems modeling research and intelligent computing theory, exploring the nature of bacterial foraging algorithm to improve its ability to solve complex scientific and engineering problems. The research results of this project will give out the foundation for the further application of biological heuristic methods into the field of RFID systems optimization.

自然界是人类创新灵感的不竭源泉，基于生物行为的智能优化方法是目前智能科学的研究热点。本项目从生物建模、算法设计和工程应用三个层面对基于细菌觅食行为的新型优化模型与算法展开研究。在生物建模方面，对细菌个体趋化、个体间信息交流与种群进化模式进行抽象、建模与仿真，构建菌群生命周期优化模型；在算法设计方面，深入研究基于个体复制、消亡、迁徙的多样性保持策略，兼顾全局探索与局部开发的自适应搜索策略，基于任务分工与协作的协同进化策略，设计一系列能够求解高维、离散、动态、多目标问题的新型菌群优化算法；在具体应用方面，建立物联网环境下大规模RFID网络的多目标动态优化模型，通过模型求解分析所提出算法的收敛性、有效性和可行性。本项目研究成果将结合微生物系统建模与智能计算理论，探索菌群觅食算法优化机理，提高其求解复杂科学与工程问题的能力，并为生物启发式计算方法在RFID系统优化领域的进一步应用打下基础。

本项目从生物建模、算法设计和工程应用三个层面对基于细菌觅食行为的新型优化模型与算法展开研究。在生物建模方面，对群体中个体趋化、个体间信息交流与种群进化模式进行抽象、建模与仿真，构建生命周期优化模型；在算法设计方面，深入研究基于个体复制、消亡、迁徙的多样性保持策略，兼顾全局探索与局部开发的自适应搜索策略，基于任务分工与协作的协同进化策略，设计一系列能够求解高维、离散、动态、多目标问题的新型菌群优化算法；在具体应用方面，建立物联网环境下大规模RFID网络的优化模型，通过模型求解分析所提出算法的收敛性、有效性和可行性。本项目研究成果将结合微生物系统建模与智能计算理论，探索菌群觅食算法优化机理，提高其求解复杂科学与工程问题的能力，并为生物启发式计算方法在RFID系统优化领域的进一步应用打下基础。