Description: 利用C++编写蚁群优化算法解决51城市旅行商问题-prepared Ant Colony Optimization Algorithm for 51 city Traveling Salesman Problem Platform: |
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Description: 是一个基于Matlab软件的模糊C-means聚类算法原代码-Matlab is a software based on the fuzzy C-means clustering algorithm source code Platform: |
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Author:杨明 |
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Description: 经典的粒子群算法（PSO），通过迭代搜索更新产生更优良的下一代解。供大家参考学习-classical PSO (PSO), through iterative search update produce the next generation better solution. For your reference learning Platform: |
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Author:笑容 |
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Description: This a demo program of image edge detection using ant colony, based on
the paper, "An Ant Colony Optimization Algorithm For Image Edge Detection," 基于蚁群算法的图像边缘检测。IEEE于2008收录的文章.有matlab源程序和“An Ant Colony Optimization Algorithm For Image Edge Detection”这篇PDF文章。希望对大家有一定帮助。-This is a demo program of image edge detection using ant colony, based on the paper, " An Ant Colony Optimization Algorithm For Image Edge Detection," Based on Ant Colony Optimization Image Edge Detection. IEEE in 2008 included in the article. Matlab source code and there is " An Ant Colony Optimization Algorithm For Image Edge Detection" This PDF article. Hope to assist everyone must have. Platform: |
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Author:miaosiyi |
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Description: 无约束连续函数优化的人工蚁群算法通用MATLAB源码
此源码是对人工蚁群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解
-Unconstrained optimization of continuous function of artificial ant colony algorithm for general-purpose MATLAB source code。
This source code is an implementation of artificial ant colony algorithm for unconstrained optimization of continuous function to solve for the case containing the constraints, you can first use the penalty function and other methods to deal with the problem into the unconstrained model, then use the origin codes solving Platform: |
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Author:孙准 |
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Description: 蚁群算法源代码，无须修改便可以直接编译执行-Ant colony algorithm source code, no need to change the implementation can be directly translated Platform: |
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Description: 此源码是对人工蚁群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解，-This source code is an implementation of artificial ant colony algorithm for unconstrained optimization of continuous function to solve for the case containing the constraints, you can first use the penalty function and other methods to deal with the problem into a non-binding model, and then use the source code is solved, Platform: |
Size: 2048 |
Author:张望 |
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Description: 基于蚁群算法的PID控制参数优化Matlab源码,步骤详细，附有说明。-PID Control Based on Ant Colony Optimization Algorithm for Matlab source, the steps in detail, with explanation. Platform: |
Size: 2048 |
Author:程玉昆 |
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Description: 此源码是对人工蚁群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解。-This source code is an implementation of artificial ant colony algorithm for unconstrained continuous function optimization, with constraints for the case, you can start using the penalty function and other methods to deal with the problem into the unconstrained model, then use the source code solved. Platform: |
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Author:ly |
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Description: 其主要内容包括蚁群算法的思想起源、研究现状及机制原理；蚁群算法的复杂度分析；蚁群算法的收敛性证明；蚁群算法参数对其性能的影响；蚁群算法的参数选择原则；离散域和连续域蚁群算法的若干改进策略；蚁群算法在多个优化领域的典型应用；蚁群算法的硬件实现技术；蚁群算法与其它仿生优化算法的比较与融合；蚁群算法的研究展望；最后还在附录部分给出了基本蚁群算法的程序源代码和相关网站。.-Its main contents include the origin of ant colony algorithm idea, principle and mechanism of Research the complexity analysis of ant colony algorithm prove the convergence of ant colony algorithm ant colony algorithm parameters on its performance the principle of ant colony algorithm parameters discrete domain and continuous domain of a number of ant colony algorithm improvement strategies ant colony optimization algorithm is typical in many applications ACO hardware implementation technology ant colony optimization algorithm and other bionic comparison and integration ant colony algorithm research prospects final appendix also shows the basic ant colony algorithm source code and related sites. . Platform: |
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Author:ma |
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Description: 连续优化问题如(Schaffer函数)的蚁群算法源码和图像-Continuous optimization problems such as (Schaffer the function) ant colony algorithm source code and images Platform: |
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Author:liuyu |
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Description: In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search [3] and share some similarities with Estimation of Distribution Platform: |
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Author:ibra |
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Search - source code ant colony optimization