全面学习与进化细菌觅食优化方法研究

项目名称： 全面学习与进化细菌觅食优化方法研究

项目编号： No.71271140

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 牛奔

作者单位： 深圳大学

项目金额： 51.5万元

中文摘要： 本项目以个体、群体交流协作机制为突破口，从模型构建、算法设计、应用验证等方面系统研究了具有全面学习与进化功能的新型细菌觅食优化模型与算法。具体内容包括：基于领域拓扑结构模式与多群体协同进化思想，分别建立了面向个体与群体学习与进化的交流协作优化模型；以系统智能理论为指导，分析了复杂适应系统与系统智能模型的映射关系，从而提出具有系统涌现特征的全面学习与进化优化模型；以构建的模型为基础，抽取细菌觅食活动过程中各类进化操作算子，提出全面学习与进化细菌觅食优化算法，并对其进行统计特性分析与性能对比实验；最后，针对电力系统中考虑排放的经济调度问题，构建了动态多目标环境经济调度模型，设计了面向该类问题的扩展全面学习与进化细菌觅食优化算法，并进行应用验证。该研究为设计群体智能优化算法提供了新思路，研究成果拓展了细菌觅食优化理论与实践，具有重要学术价值与创新意义。

中文关键词： 学习；进化；细菌觅食；算法；多目标

英文摘要： This project makes the communication & collaboration of individuals and swarms as a breakthrough to study a novel bacterial foraging optimization model& algorithm with the ability of comprehensive learning and evolution, which from the perspectives of model construction, algorithm design, application verification. Specific contents are described as follows. Firstly, based on the ideas of neighborhood topology structure and multi-swarm cooperative learning scheme individual-oriented and swarm-oriented communication & collaboration optimization model are established, respectively. Then, using system intelligence theory as a guide, we proposed the comprehensive learning and evolutionary the optimization model with emergence characteristics by analyzing the mapping relationship between complex adaptive system and system intelligence model. After that, based on the above mentioned models and extracting the evolutionary operators from the bacterial foraging activities, we present comprehensive learning and evolutionary optimization algorithm. The statistical properties analysis and performance comparisons of the proposed algorithm are also conducted. Finally, considering the economic dispatch (ED) problem with emissions in power system, we present a dynamic multi-objective environmental/economic dispatch (DMEED) mod

英文关键词： Learning；Evolution；Bacterial Foraging；Algorithm；Multiobjective