使用群智能模拟OMNET网络路由

模糊数学建摸课件。讲述的是数学建模中模糊数学的概念，给初学者一个清晰地认识。-Fuzzy modeling courseware. About the mathematical concept of fuzzy mathematical modeling to give beginners a clear understanding of.

Fuzzy logic controller that uses mamdani logic.

用MATLAB语言编写。三十三座城市的蚂蚁算法。解决TSP问题。-Used MATLAB language. 33 cities in the ant algorithm. Solve the TSP problem.

遗传算法在路由优化中的应用:多服务质量的最小路径的算法.-Genetic Algorithm in Routing Optimization: Multi-service quality of the minimum path algorithm.

多步预测,设计简单规范,基于信息建模,具有很强的抗干扰性和鲁棒性.-Multi-step prediction, the design of simple norms, based on information modeling, which has strong anti-jamming and robustness.

用matlab语言编写的,该程序名为模拟退火算法m 源程序-Matlab language used, the procedure called simulated annealing algorithm m source

tsp问题用神经网络方法解决-tsp uses neural network solution

这是书上的常用搜索算法，有8数码游戏，黑白棋，A star算法类的实现。-This is a book commonly used search algorithm, there are 8 digital game, Reversi, A star algorithm category realize.

3。《遗传算法与工程设计》，[日]玄光男着，程润伟着 科学出版社 2000年第一版 本书着重从计算方法和应用技术的角度对遗传算法作了深入浅出的讨论。书中介绍了遗传算法的基本概念和结构，解决约束优化和组合优化问题的方法，以及遗传算法在生产调度、可靠性设计、交通运输、设备布局等一系列工业工程问题中的应用。本书可作为高等院校的工业工程、管理科学、运等学、计算机科学和人工智能专业的本科生和研究生的教科书。对于系统分析员、运行研究员、管理科学家以及在工业工程与运筹学领域里从事最优化问题研究和应用的专业技术人员，本书也不失为一本内容丰富且实用性较强的参考书。-3. "Genetic Algorithms and Engineering Design", [] to Yuen-man, Guilin-a 520-531 2000 version of the first book to focus on calculation methods and application of a technical point of view for Genetic Algorithm a simple discussion. The book on genetic algorithms, the basic concepts and structures, optimization and constraint solving combinatorial optimization problems. and the genetic algorithm in production scheduling, reliability design, transportation, equipment and layout of a series of industrial engineering problems applications. The book can be used as colleges in the industrial engineering, management science, transport and other science, computer science and artificial intelligence professi

麻省理工大学遗传算法 -Massachusetts Institute of Technology Massachusetts Institute of Technology genetic algorithm genetic algorithm