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Description: SFLA算法是解决组合性优化问题的算法。它是基于集合类方法的启发式研究，这种SFLA初始化于一群虚拟集合青蛙，在池塘中跳跃，搜寻最优的食物地点。青蛙们可以被看作是具有思维的的主体。一种思维可以被看作是一个思想的集合或是文化的进化。每一种思维都是由一系列策略构成。在这种策略进化期间，青蛙的思维也在发生改变，导致了他们在面向目标时方位的改变，这种思维的变化或改变的发生，正是因为青蛙受到其他更好思想的影响。-SFLA algorithm to solve portfolio optimization problems and algorithms. It is based on the collections of the heuristic research method, which is initialized to a group of SFLA virtual collection of frogs, jumping in the pond, searching for the optimal location of food. Frogs can be seen as the main body with thinking. Can be seen as a way of thinking is a set of ideological or cultural evolution. Each type of thinking is constituted by a series of strategies. Period in the evolution of this strategy, the frog is also a change of thinking, leading to goal-oriented when they change direction, this way of thinking of the change or changes occur, because frogs are better ideas.
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Author: yangfei |
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Description: 运用蛙跳算法解决TSP问题的IEEE英文文献,帮助学习算法改进-Solving TSP with Shuffled Frog-Leaping Algorithm
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Author: xsx |
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Description:
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Size: 3072 |
Author: prad |
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Description: 一种基于Visual C++的混合蛙跳算法程序，希望对各位学习者提供一点帮助-Visual C++ based on the shuffled frog leaping algorithm program you want to provide learners with a little help
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Size: 1024 |
Author: 孟庆莹 |
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Description: manuscript that describes a shuffled frog leaping algorithm and svm optimization for images recognition
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Author: harouni |
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Description: 这是一个改进的SFLA（混合蛙跳算法），使用MATLAB 实现，代码简单并且实用，欢迎下载。-This is an improved the SFLA (shuffled frog leaping algorithm), using MATLAB to achieve, the code is simple and practical, welcome to download.
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Size: 2048 |
Author: tdy |
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Description: 2012年2013年的国内外多目标蛙跳算法最新SCI文献，利于了解和学习多目标蛙跳算法最新动态-2012 2013 domestic and international multi-objective shuffled frog leaping algorithm SCI literature, which will help to understand and learn the latest developments of the multi-objective shuffled frog leaping algorithm
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Author: 陈玉挺 |
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Description: 讨论四种群体智能优化算法———蚁群算法、微粒群算法、人工鱼群算法和混合蛙跳算法 ,对其算法的
原理、发展及应用进行了综述。提出了群体智能优化算法统一框架模式 ,并对群体智能优化算法进一步发展进行
了讨论。
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Discuss four swarm intelligence optimization algorithm--- ant colony algorithm, particle swarm optimization, artificial fish swarm algorithm and shuffled frog leaping algorithm, their algorithm theory, development and application are reviewed. Swarm intelligence optimization algorithm proposed unified framework model, and the further development of swarm intelligence optimization algorithms are discussed.
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Size: 396288 |
Author: chenchen |
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Description: 混合蛙跳算法程序，采用1、若干次运行取最优
2、增加进化代数maxgen
3、加大种群规模popsize。三种方法来可有效克服随机初始化带来的局部极小问题-Shuffled frog leaping algorithm, using one, several runs to take the best two, increasing evolution algebra maxgen 3, to increase the population size popsize. Three ways to effectively overcome local minima caused by random initialization problem
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Author: wuqiong |
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Description: In order to minimize power losses caused by high current and improve the voltage profile in the network distribution, the introduction of dispersed generations also called productions decentralized in distribution network (DG) plays an important role. The installation of DGs directly affects the power requested or purchased. The sizing and placement of DGs in the system must be optimal because a wrong choice has a negative impact on the system behavior. To solve this combinatorial problem, an algorithm known as Firefly algorithm is proposed in this paper. This is a meta-heuristic algorithm inspired by the behavior of fireflies flashing. The main objective of firefly flash is to act as a signaling system to attract other fireflies. Networks tested IEEE69-bus and IEEE33-bus are used to evaluate the effectiveness of this method. The results are compared with those obtained by genetic algorithm (GA) to IEEE69-bus, and Shuffled frog leaping algorithm (SFLA) for IEEE 33-bus.
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Author: pasupu |
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Description: research paper related to shuffled frog leaping algorithm for optimization problems in power system network, transmission , distribution area
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Size: 690176 |
Author: vaibhav |
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Description: research paper related to shuffled frog leaping algorithm for optimization problems in power system network, transmission , distribution area
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Author: vaibhav |
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Description: algorithm og shuffled frog leaping algorithm
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Author: TEHAMI |
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Description: 自己编写的
混洗蛙跳算法优化最小二乘支持向量机参数
把文件里的数据、LSSVM模型更换，就可以移植-written by Son.Y.M
Shuffled frog leaping algorithm to optimize the parameters of LS-SVM
The data in the file, LSSVM model replacement, you can transplant
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Size: 2048 |
Author: Sun Yuanmeng |
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Description: 智能算法，混合蛙跳算法，一种模拟生物属性的寻优算法-The shuffled frog leaping algorithm
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Author: 姚超然 |
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Description: this paper deals with insights of unequal clustering using Shuffled frog leaping algorithm
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Author: chitti |
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Description: SFGA 混合蛙跳算法 用c++语言实现-SFGA SFLA with c++ language
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Author: 杨芸 |
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Description: This paper propose a Firefly algorithm (FA) for
optimal placement and sizing of distributed generation (DG) in
radial distribution system to minimize the total real power losses
and to improve the voltage profile. FA is a metaheuristic
algorithm which is inspired by the flashing behavior of fireflies.
The primary purpose of firefly’s flash is to act as a signal system
to attract other fireflies. Metaheuristic algorithms are widely
recognized as one of the most practical approaches for hard
optimization problems. The most attractive feature of a
metaheuristic is that its application requires no special
knowledge on the optimization problem. In this paper, IEEE 33-
bus distribution test system is used to show the effectiveness of
the FA. Comparison with Shuffled Frog Leaping Algorithm
(SFLA) is also given.
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Author: AMIR555 |
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Description: The component pick-and-place sequence is one of the key factors to affect the working efficiency of theShuffled Frog-leaping Algorithms-The component pick-and-place sequence is one of the key factors to affect the working efficiency of the
surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled
Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA)
with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior
to the frog. The objective function of component pick-and-place sequence of the gantry multi-head
component surface mounting machine was established. Parameters selection is critical for SFLA. In this
study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters
like memeplex numbers m, the frogs’ number P and local evolution numbers iPart were found having notable
effects on the mounting time (time spent for components picking and placing), but the interactions
among these parameters were not obvious. Multiple comparison procedures wer
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Author: yangs |
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Description: matlab code for sfla
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Author: masad |
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