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国外蜂群算法（Artificial Bee Colony Algorithm ,ABC）源码,Artificial Bee Colony Algorithm
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Training Artificial Neural Network.
XOR Problem.
Summation Units, Log-Sigmoid Neurons with Biases.
Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons.
Returns mean square error between desired and actual outputs.
Reference Pape
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Artificial Bee Colony OPTIMIZATION ALGO
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应用matlab编写的人工蜂群算法智能优化-Intelligent Optimization of application matlab prepared artificial bee colony algorithm
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基于人工蜂群算法（artificial bee colony algorithm）的聚类优化算法-Clustering optimization algorithm based on artificial bee colony algorithm
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Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. It is as simple as Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm
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人工蜂群算法改进版，非常典型，非常适合用于寻优-Artificial bee colony algorithm is an improved version, very typical, very suitable for optimization
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Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga,
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Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga,
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是蛋白质二级结构优化（基于2维度非晶格模型）的优化代码，可直接运行，算法书写简单，高中生都能看懂。唯独一点，必须引用我们的两篇论文，否则算侵犯版权。- descr iption : A self-organized matlab m file for protein structure
optimization using 2D off-lattice model and ABC algorithm.
Users MUST cite t
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ABC即人工蜂群算法，本代码描述了人工蜂群算法解决函数优化问题。-ABC namely artificial bee colony algorithm, this code describes the function of artificial bee colony algorithm to solve optimization problems.
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人工蜂群算法是模仿蜜蜂行为提出的一种优化方法，是集群智能思想的一个具体应用，它的主要特点是不需要了解问题的特殊信息，只需要对问题进行优劣的比较，通过各人工蜂个体的局部寻优行为，最终在群体中使全局最优值突现出来，有着较快的收敛速度。-Artificial bee colony algorithm is an optimization technique to mimic the behavior of bees make is thinking of a particular cluster of
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针对工业控制中常用的PID控制器参数整定困难的问题,提出一种基于人工蜂群算法的参数整定方法。将PID控制器待整定参数看作蜜源,利用蜂群特有的角色转变机制搜索优质的参数组合 选取绝对误差矩积分性能指标作为参数寻优的目标函数。仿真实验结果表明,所采用的算法能够提高控制系统的动态性能,增强系统的快速性和稳定性,适用于PID控制器的自整定。
-For industrial control commonly used in PID controller tuning difficult problem
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人工蜂群算法是模仿蜜蜂行为提出的一种优化方法，具有较快的收敛速度和寻优精度。-Artificial bee colony algorithm is to mimic the behavior of an optimization method proposed by bees, has a faster convergence rate and optimization accuracy.
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在求解函数优化问题时，为了提升人工蜂群算法局部搜索能力，提出了一种新颖的混沌蜂群算法。-in order to improve the ability of artificial bee colony algotithm at exploitation, a novel chaos-artificial bee colony algorithm is proposed for continuous function optimization problems.
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In computer science and operations research, the artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm, proposed by Karaboga in 2005.[1]
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人工蜂群算法，模拟蜂群寻蜜的过程可以实现最优化-Artificial bee colony algorithm to simulate the process of honey bee colony optimization can be achieved
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基本人工蜂群算法，带中文注释
可以用来求解优化问题，也可在此基础上改进(Basic Artificial Bee Colony Algorithm with Chinese Notes
Can be used to solve the optimization problem can also be improved on this basis)
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Artificial bee colony optimization for matlab
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人工蜂群算法(Artificial Bee Colony, ABC)是由Karaboga于2005年提出的一种新颖的基于群智能的全局优化算法，其直观背景来源于蜂群的采蜜行为，蜜蜂根据各自的分工进行不同的活动，并实现蜂群信息的共享和交流，从而找到问题的最优解。人工蜂群算法属于群智能算法的一种。(Artificial bee colony algorithm is a novel optimization algorithm based on swarm intelligence is propos
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