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SFLA

于 2020-10-15 发布

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下载积分: 1 下载次数: 1

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说明：蛙跳基本程序。对于N维函数优化问题,第i只青蛙可表示为P(i)=(P1i,P2i,…,PNi),根据每只青蛙适应值(位置)的大小按下降顺序排列。(Frog jump basic procedure. For n-dimensional function optimization, the ith frog can be expressed as P(I)=(P1i,P2i...,PNi), in descending order according to the size of each frog's fitness (position).)

文件列表：

SFLA\func_plot.m, 2245 , 2020-08-29
SFLA\Get_Functions_details.m, 6278 , 2020-08-29
SFLA\IsInRange.m, 487 , 2015-08-28
SFLA\main.m, 1981 , 2020-08-29
SFLA\RandSample.m, 681 , 2015-08-28
SFLA\RunFLA.m, 3258 , 2015-08-28
SFLA\sfla.asv, 2780 , 2020-08-29
SFLA\sfla.m, 2816 , 2020-08-29
SFLA\SortPopulation.m, 635 , 2015-08-28

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pie chart
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Solving NP-hard discrete optimization problems to optimality is often an immense job requiring very ecient algorithms, and the B&B paradigm is one of the main tools in construction of these. A B&B algorithm searches the complete space of solutions for a given problem for the best solution. However, explicit enumeration is normally impossible due to the exponentially increasing number of potential solutions. The use of bounds for the function to be optimized combined with the value of the current best solution enables the algorithm to search parts of the solution space only implicitly.

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SFLA

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