Topology optimization in structural and continuum mechanics George I.N. Rozvany, Tomasz Lewinski, editors （CISM courses and lectures, 549） Springer , CISM, c2014

个人整合了火鹰优化算法fire hawk optimization algorithm 源代码及其原文，更多算法可进入空间查看

An Introductionto Optimization最优化导论电子版讲义

The fusion between graph theory and combinatorial optimization has led to theoretically profound and practically useful algorithms, yet there is no book that currently covers both areas together. Handbook of Graph Theory, Combinatorial Optimization, and Algorithms is the first to present a unified, comprehensive treatment of both graph theory and combinatorial optimization. Divided into 11 cohesive sections, the handbook’s 44 chapters focus on graph theory, combinatorial optimization, and algorithmic issues. The book provides readers with the algorithmic and theoretical foundations to: Understand phenomena as shaped by their graph structures Develop needed algorithmic and optimization tools for the study of graph structures Design and plan graph structures that lead to certain desirable behavior With contributions from more than 40 worldwide experts, this handbook equips readers with the necessary techniques and tools to solve problems in a variety of applications. Readers gain exposure to the theoretical and algorithmic foundations of a wide range of topics in graph theory and combinatorial optimization, enabling them to identify (and hence solve) problems encountered in diverse disciplines, such as electrical, communication, computer, social, transportation, biological, and other networks. Table of Contents SECTION I - Basic Concepts and Algorithms CHAPTER 1 - Basic Concepts in Graph Theory and Algorithms CHAPTER 2 - Basic Graph Algorithms CHAPTER 3 - Depth-First Search and Applications SECTION II - Flows in Networks CHAPTER 4 - Maximum Flow Problem CHAPTER 5 - Minimum Cost Flow Problem CHAPTER 6 - Multicommodity Flows SECTION III - Algebraic Graph Theory CHAPTER 7 - Graphs and Vector Spaces CHAPTER 8 - Incidence, Cut, and Circuit Matrices of a Graph CHAPTER 9 - Adjacency Matrix and Signal Flow Graphs CHAPTER 10 - Adjacency Spectrum and the Laplacian Spectrum of a Graph CHAPTER 11 - Resistance Networks, Random Walks, and Network Theorems SECTION IV - Structural Graph Theory CHAPTER 12 - Connectivity CHAPTER 13 - Connectivity Algorithms CHAPTER 14 - Graph Connectivity Augmentation CHAPTER 15 - Matchings CHAPTER 16 - Matching Algorithms CHAPTER 17 - Stable Marriage Problem CHAPTER 18 - Domination in Graphs CHAPTER 19 - Graph Colorings SECTION V - Planar Graphs CHAPTER 20 - Planarity and Duality CHAPTER 21 - Edge Addition Planarity Testing Algorithm CHAPTER 22 - Planarity Testing Based on PC-Trees CHAPTER 23 - Graph Drawing SECTION VI - Interconnection Networks CHAPTER 24 - Introduction to Interconnection Networks CHAPTER 25 - Cayley Graphs CHAPTER 26 - Graph Embedding and Interconnection Networks SECTION VII - Special Graphs CHAPTER 27 - Program Graphs CHAPTER 28 - Perfect Graphs CHAPTER 29 - Tree-Structured Graphs SECTION VIII - Partitioning CHAPTER 30 - Graph and Hypergraph Partitioning SECTION IX - Matroids CHAPTER 31 - Matroids CHAPTER 32 - Hybrid Analysis and Combinatorial Optimization SECTION X - Probabilistic Methods, Random Graph Models, and Randomized Algorithms CHAPTER 33 - Probabilistic Arguments in Combinatorics CHAPTER 34 - Random Models and Analyses for Chemical Graphs CHAPTER 35 - Randomized Graph Algorithms: Techniques and Analysis SECTION XI - Coping with NP-Completeness CHAPTER 36 - General Techniques for Combinatorial Approximation CHAPTER 37 - ε-Approximation Schemes for the Constrained Shortest Path Problem CHAPTER 38 - Constrained Shortest Path Problem: Lagrangian Relaxation-Based Algorithmic Approaches CHAPTER 39 - Algorithms for Finding Disjoint Paths with QoS Constraints CHAPTER 40 - Set-Cover Approximation CHAPTER 41 - Approximation Schemes for Fractional Multicommodity Flow Problems CHAPTER 42 - Approximation Algorithms for Connectivity Problems CHAPTER 43 - Rectilinear Steiner Minimum Trees CHAPTER 44 - Parameter Algorithms and Complexity

智能算法 智能算法是路线规划，深度学习等等各个领域所使用的优化算法，是算法进阶之路的必备之路。 简介 主要针对总体主流的算法进行，例如遗传算法，粒子群算法，模拟重复算法，免疫算法，蚁群算法等等一系列的算法。 | |登录微信公众号：TeaUrn 开始使用 实现版本Java，Python，MatLab多版本实现。具体详细说明单击以下连接针对每个算法都有详细的说明。 联系方式： 微信公众号： TeaUrn或者扫描下方二维码进行关注。里面有惊喜等你哦~~ 捐赠 如果您觉得文章对您有所帮助，可以请作者喝 :hot_beverage: 。 支付宝/微信/ QQ

Teaching-Learning-based Optimization

This is an algorithm which is used to solve optimization problem but the only thing is it can optimize only one problem

Impostors - Runtime Optimization v1.0.3

加州大学Richard M. Murray教授等人由课程讲义编写的书稿，介绍了最优控制、滚动时域、随机系统、Kalman滤波等。

随附的 ZIP 文件包含使用 SonnetLab Toolbox for MATLAB:registered: 的教程。 SonnetLab 是一个免费的 MATLAB:registered: 工具箱，使用户能够控制和自动化 Sonnet 的 3D 平面电磁模拟器。 附加的脚本将以单个短截线的形式打开现有的十四行诗项目，并优化短截线的长度以在 5 GHz 下提供低回波损耗。 原始电路包含控制存根长度的尺寸参数，优化例程修改参数值以生成项目的十次迭代。 随附的档案包括详细描述每一行代码的文档。 本教程需要最新版本的 SonnetLab。 SonnetLab 可从 Sonnet Software 网站下载： http ://sonnetsoftware.com/support/sonnet-suites/sonnetlab.html 请注意：SonnetLab 是与 Syracuse University 共同开发的，不是 So