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智能化算法的应用，介绍了各种智能算法的概况，详细介绍了蚁群算法、免疫算法、遗传算法、粒子群算法在配电网重构中的应用，可以管中窥豹地学习智能算法在具体问题中的应用-The application of intelligent algorithms, introduced an overview of the various intelligent algorithms, detailed information on ant colony algorithm, immune algorithm,
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支持向量机参数选择的相关中文文献，主要包括遗传算法、微粒子群算法、混沌优化算法等。-Chinese literature about Support vector machine parameters, including genetic algorithms, Particle swarm optimization, Chaos optimization algorithm.
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某些实际问题的优化目标是求所有的局部最优解，即求解多峰寻优问题，为了求解多峰优化问题，提出了改造的微粒
群优化算法．尽量减少微粒群算法中的全局因素，从而增大其局部因素，同时采用变步长方法增加微粒的多样性．并给出了该算法
的原理和步骤．仿真实验表明该算法概念清楚，计算简单，具有很好的局部寻优特性，可应用求解于多峰寻优问题．另外还给出了几
个运算实例和与其它优化算法的比较-Some of the practical problems of optimization goal is
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Optimization problems are very important in many fields. To the present,
many optimization algorithms based on computational intelligence have been proposed,
such as the Genetic Algorithm, Ant Colony Optimization (ACO), and Particle Swarm
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摘要：新型元启发式算法例如粒子群算法，萤火虫算法，和声搜索算法已经成为现今复杂的优化问题的有效解决方法。该文基于蝙
蝠的回声定位行为提出了一种新型的元启发式算法———蝙蝠算法，同时也将现有的一些算法的优点引入到该算法中。 改文对该算
法进行了详细的公式化表述并对其执行流程的作出了说明，并且将该算法与遗传算法、粒子群优化算法等算法进行了比较。仿真结
果表明,蝙蝠算法明显优于其他算法，并对进一步的研究作出了展望。-Summary: The new meta-heuristic algor
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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模糊方法与粒子群算法结合，能有效的提高算法的性能。-Fuzzy methods and particle swarm algorithms, can effectively improve the performance of the algorithm.
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粒子群算法源程序，是近年来发展起来的一种新的进化算法。有实现容易、精度高、收敛快等优点。是一种并行算法。-Particle swarm algorithm source code, is a new evolutionary algorithm developed in recent years. There are easy to implement, high precision, fast convergence and so on. Is a kind of parallel algor
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智能算法，包括粒子群，改进粒子群，改进遗传算法等，可用于预测等领域！-Intelligent algorithms, including PSO, improved particle swarm, improved genetic algorithm can be used to predict other fields!
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Particle Swarm Optimization and Differential
Evolution Algorithms: Technical Analysis,
Applications and Hybridization Perspectives
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除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to the ant colony algorithm, can be used to optimize the PID parameters, there are many intelligent algorithms, such as genetic algorithms, simulated annealing algorithm, particle
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联合稀疏恢复的Swarm算法，在基本particle swarm optimization (PSO)算法基础上进行改进-Inspired by particle swarm
optimization (PSO) algorithm and some sparse recovery algorithms,
a novel swarm intelligence algorithm called M-SISR is
proposed to solve the problem. In M-
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粒子群算法求极值蚁群算法求极值和遗传算法求极值-Particle Swarm extremum extremum ant colony algorithm and genetic algorithms Extreme
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粒子群优化（PSO）是一种进化计算技术（进化计算）。
捕食鸟行为的研究。粒子群算法（PSO）的基本思想是通过群体中个体之间的协作和信息共享找到最优解。
粒子群优化算法的优点是它简单且易于实现，没有多个参数。目前，它已广泛应用于函数优化、神经网络训练、模糊系统控制等遗传算法中。(The particle swarm optimization (PSO:Particle swarm optimization) is an evolutionary computing technology (Ev
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近红外光谱(NIRS)是一种间接分析技术,其应用需建立相应的校正模型。为了提高模型的解释能力、预测准确度和建模效率,需要对NIRS 进行波长选择,优选最小化冗余信息。选用蚁群优化(ACO)、遗传优化(GA)、粒子群优化(PSO)、随机青蛙(RF)和模拟退火(SA)5 种智能优化算法对烟叶总氮和烟碱近红外光谱数据进行特征波长选择,结合偏最小二乘(PLS)算法,构建了多个烟叶总氮和烟碱的校正模型,(Near infrared spectroscopy (NIRS) is an indirect an
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该文档对智能算法，包括遗传算法、粒子群算法、禁忌搜索算法和人工神经网络算法等的概念和性能进行了基本总结(This paper summarizes the concept and performance of intelligent algorithms, including genetic algorithm, particle swarm optimization algorithm, tabu search algorithm and artificial neural network a
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