非线性连续系统安全性验证的启发式方法

虚拟CPU 我的和启发的面包板计算机，汇编器和基于Web的代码，文档，示意图，说明，均使用编译为WASM的C后端。 结构体 Arduino的 微码EEPROM写入器 DecimalDisplay EEPROM写入器 ESP8266 Wi-Fi程式载入器 启用页面写的EEPROM编写器库（在Greenliant GLS29EE010上测试） 仿真器（C库） SimLib-仿真器核心 SimInst-仿真器核心的单个实例接口 SimWin-库周围的Windows可执行文件（用于测试） SimWasm-Emscripten源代码和脚本来产生WASM输出 笔记 构建面包板计算机时使用的各种文件

如何求解问题——现代启发式方法 PDF版

元启发式算法——蜻蜓算法《Dragonfly algorithm，DA》的MATLAB版本

广度优先搜索BFS、一致代价搜索UCS、深度优先搜索DFS和启发式搜索Ａ＊的详细理解，最重要的是自己创建的例子，并进行详细的分析和算法步骤的图示

启发式扫描和主动防御技术是目前比较先进的反病毒技术，本文档就这两种技术进行了详细的介绍和分析

元启发式多目标优化的评判指标的matlab代码，包括spread\IGD\GD\RNI从多样性、收敛性等角度评价多目标优化算法

狼群优化算法，一种新的启发式算法，很适合初学者学习使用，内部有文档说明，可以参考文档进行学习使用，同时对于深层学习具有很好的作用

以重排九宫问题/八数码问题为例，以启发式搜索方法求解给定初始状态和目标状态的最优搜索路径

启发式算法设计与实现 ISBN: 978-0-470-27858-1 Hardcover 624 pages June 2009 This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.