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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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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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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，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 colony algorithm is to imitate the bee behavior put forward an optimization method，Only need to compare advantages and disadvantages of problem， Through the l
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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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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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Artificial bee colony optimization for matlab
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人工蜂群算法是Karaboga于2005年提出的一种新颖的群集智能优化算法。算法主要模拟智能采蜜行为，蜜蜂根据各自的分工进行不同的采蜜活动(Artificial bee colony algorithm () is a novel swarm intelligence optimization algorithm proposed by Karaboga in 2005. The algorithm mainly simulates the intelligent honey collectin
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人工蜂群算法自2005年被Karaboga等人提出以来，以其操作简单、参数少、易于编程实现、收敛速度快等特点而受到越来越多的关注。2007年，Karaboga【2007】使用人工蜂群算法对多变量函数进行优化，并对由人工蜂群算法（ABC），遗传算法（GA），粒子温度算法（PSO）和粒子温度灵敏演化算法（PS-EA）产生的结果进行了比较。 结果表明，人工蜂群算法优于其他算法。2009年，Karaboga【2009】使用人工蜂群算法优化大量的数值函数，并对由人工蜂群算法（ABC），遗传算法（GA），粒
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