登录
首页 » matlab » MFOA

MFOA

于 2020-06-16 发布
0 957
下载积分: 1 下载次数: 10

代码说明:

说明:  基于CEC——2017benchmark测试集,计算最优 修正的果蝇算法,弥补原始果蝇算法在负数集上的缺失(modify fruit fly optimization)

文件列表:

cec17_func.cpp, 41819 , 2019-01-17
cec17_func.mexw64, 51712 , 2017-06-29
input_data, 0 , 2019-01-17
input_data\M_10_D10.txt, 2520 , 2016-09-04
input_data\M_10_D100.txt, 250200 , 2016-09-04
input_data\M_10_D2.txt, 104 , 2016-09-04
input_data\M_10_D20.txt, 10040 , 2016-09-04
input_data\M_10_D30.txt, 22560 , 2016-09-04
input_data\M_10_D50.txt, 62600 , 2016-09-04
input_data\M_11_D10.txt, 2520 , 2016-09-04
input_data\M_11_D100.txt, 250200 , 2016-09-04
input_data\M_11_D30.txt, 22560 , 2016-09-04
input_data\M_11_D50.txt, 62600 , 2016-09-04
input_data\M_12_D10.txt, 2520 , 2016-09-04
input_data\M_12_D100.txt, 250200 , 2016-09-04
input_data\M_12_D30.txt, 22560 , 2016-09-04
input_data\M_12_D50.txt, 62600 , 2016-09-04
input_data\M_13_D10.txt, 2520 , 2016-09-04
input_data\M_13_D100.txt, 250200 , 2016-09-04
input_data\M_13_D30.txt, 22560 , 2016-09-04
input_data\M_13_D50.txt, 62600 , 2016-09-04
input_data\M_14_D10.txt, 2520 , 2016-09-04
input_data\M_14_D100.txt, 250200 , 2016-09-04
input_data\M_14_D30.txt, 22560 , 2016-09-04
input_data\M_14_D50.txt, 62600 , 2016-09-04
input_data\M_15_D10.txt, 2520 , 2016-09-04
input_data\M_15_D100.txt, 250200 , 2016-09-04
input_data\M_15_D30.txt, 22560 , 2016-09-04
input_data\M_15_D50.txt, 62600 , 2016-09-04
input_data\M_16_D10.txt, 2520 , 2016-09-04
input_data\M_16_D100.txt, 250200 , 2016-09-04
input_data\M_16_D30.txt, 22560 , 2016-09-04
input_data\M_16_D50.txt, 62600 , 2016-09-04
input_data\M_17_D10.txt, 2520 , 2016-09-04
input_data\M_17_D100.txt, 250200 , 2016-09-04
input_data\M_17_D30.txt, 22560 , 2016-09-04
input_data\M_17_D50.txt, 62600 , 2016-09-04
input_data\M_18_D10.txt, 2520 , 2016-09-04
input_data\M_18_D100.txt, 250200 , 2016-09-04
input_data\M_18_D30.txt, 22560 , 2016-09-04
input_data\M_18_D50.txt, 62600 , 2016-09-04
input_data\M_19_D10.txt, 2520 , 2016-09-04
input_data\M_19_D100.txt, 250200 , 2016-09-04
input_data\M_19_D30.txt, 22560 , 2016-09-04
input_data\M_19_D50.txt, 62600 , 2016-09-04
input_data\M_1_D10.txt, 2520 , 2016-09-04
input_data\M_1_D100.txt, 250200 , 2016-09-04
input_data\M_1_D2.txt, 104 , 2016-09-04
input_data\M_1_D20.txt, 10040 , 2016-09-04
input_data\M_1_D30.txt, 22560 , 2016-09-04
input_data\M_1_D50.txt, 62600 , 2016-09-04
input_data\M_20_D10.txt, 2520 , 2016-09-04
input_data\M_20_D100.txt, 250200 , 2016-09-09
input_data\M_20_D20.txt, 10040 , 2016-09-04
input_data\M_20_D30.txt, 22560 , 2016-09-04
input_data\M_20_D50.txt, 62600 , 2016-09-04
input_data\M_21_D10.txt, 25200 , 2016-09-04
input_data\M_21_D100.txt, 2502000 , 2016-09-04
input_data\M_21_D2.txt, 832 , 2016-09-04
input_data\M_21_D20.txt, 100400 , 2016-09-04
input_data\M_21_D30.txt, 225600 , 2016-09-04
input_data\M_21_D50.txt, 626000 , 2016-09-04
input_data\M_22_D10.txt, 25200 , 2016-09-04
input_data\M_22_D100.txt, 2502000 , 2016-09-04
input_data\M_22_D2.txt, 832 , 2016-09-04
input_data\M_22_D20.txt, 100400 , 2016-09-04
input_data\M_22_D30.txt, 225600 , 2016-09-04
input_data\M_22_D50.txt, 626000 , 2016-09-04
input_data\M_23_D10.txt, 25200 , 2016-09-04
input_data\M_23_D100.txt, 2502000 , 2016-09-04
input_data\M_23_D2.txt, 832 , 2016-09-04
input_data\M_23_D20.txt, 100400 , 2016-09-04
input_data\M_23_D30.txt, 225600 , 2016-09-04
input_data\M_23_D50.txt, 626000 , 2016-09-04
input_data\M_24_D10.txt, 25200 , 2016-09-04
input_data\M_24_D100.txt, 2502000 , 2016-09-04
input_data\M_24_D2.txt, 832 , 2016-09-04
input_data\M_24_D20.txt, 100400 , 2016-09-04
input_data\M_24_D30.txt, 225600 , 2016-09-04
input_data\M_24_D50.txt, 626000 , 2016-09-04
input_data\M_25_D10.txt, 25200 , 2016-09-04
input_data\M_25_D100.txt, 2502000 , 2016-09-04
input_data\M_25_D2.txt, 832 , 2016-09-04
input_data\M_25_D20.txt, 100400 , 2016-09-04
input_data\M_25_D30.txt, 225600 , 2016-09-04
input_data\M_25_D50.txt, 626000 , 2016-09-04
input_data\M_26_D10.txt, 25200 , 2016-09-04
input_data\M_26_D100.txt, 2502000 , 2016-09-04
input_data\M_26_D2.txt, 832 , 2016-09-04
input_data\M_26_D20.txt, 100400 , 2016-09-04
input_data\M_26_D30.txt, 225600 , 2016-09-04
input_data\M_26_D50.txt, 626000 , 2016-09-04
input_data\M_27_D10.txt, 25200 , 2016-09-04
input_data\M_27_D100.txt, 2502000 , 2016-09-04
input_data\M_27_D2.txt, 832 , 2016-09-04
input_data\M_27_D20.txt, 100400 , 2016-09-04
input_data\M_27_D30.txt, 225600 , 2016-09-04
input_data\M_27_D50.txt, 626000 , 2016-09-04
input_data\M_28_D10.txt, 25200 , 2016-09-04
input_data\M_28_D100.txt, 2502000 , 2016-09-04

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • GA优PID参数
    利用GA遗传算法优化PID控制器中的参数,实现对对控制器的改善,以求稳准快(GA genetic algorithm is used to optimize the parameters in the PID controller, so that the controller can be improved so as to stabilize the system quickly.)
    2018-07-03 14:22:01下载
    积分:1
  • 混沌tent映射tent分叉程序
    说明:  构建基于改进灰狼优化算法的神经网络数据预测模型(IGWO-BPNN),目的在于用改进的灰狼优化算法优化神经网络模型,利用神经网络的反向传播优势,改善神经网络算法易于陷入局部最小值的缺陷,提高神经网络模型的预测精度。(The grey wolf algorithm (GWO), which is inspired by the predatory behavior of the gray wolf group, is a new group intelligent optimization algorithm that imitates the leadership of gray wolf population and hunting mechanism in nature)
    2020-11-06 21:39:49下载
    积分:1
  • GA
    说明:  经典遗传算法案例,利用遗传算法求解铁罐问题(genetic algorithm optimization)
    2020-06-16 04:00:01下载
    积分:1
  • 灰狼重油建模程序
    说明:  改进的灰狼算法用于重油热解模型的建模程序(greywolf algorithm for observation of heavy oil thermal cracking)
    2021-01-21 19:18:40下载
    积分:1
  • myAntBp
    说明:  采用蚁群算法对BP神经网络进行优化,并结合实例进行应用验证。(The ant colony algorithm is used to optimize the BP neural network, and an example is used to validate it.)
    2020-10-28 13:19:58下载
    积分:1
  • 原BBO程序包
    能实现原生BBO算法,以及与其他进化算法进行对比(The native BBO algorithm can be implemented and compared with other evolutionary algorithms)
    2021-04-23 14:18:47下载
    积分:1
  • FJSP-Dynamic-master
    说明:  解决车间资源分配的动态调度问题,采用遗传算法(Solving the dynamic scheduling problem of workshop resource allocation, using genetic algorithm)
    2020-06-16 11:20:02下载
    积分:1
  • 基于子区域的粒子群优研究_曾嘉俊
    基于子区域的粒子群优化算法研究,粒子群算法是进化算法的一种,多用于路径规划等问题(Subregion-based Particle Swarm Optimization)
    2020-06-18 22:20:02下载
    积分:1
  • 基于人群搜索的函数优
    说明:  包含人群搜索算法源程序,和rastrigin、Schaffer和Spher三个函数的优化,并与PSO比较(Including the source program of crowd search algorithm, and the optimization of rastrigin, Schaffer and Sphere functions, and comparing with PSO)
    2019-06-27 01:37:39下载
    积分:1
  • Toolbox_all_algorithms
    说明:  这是MATLAB中最新的优化工具箱,它利用最近提出的7种算法来优化您的问题。 此工具箱中提供的算法包括: 灰狼优化器(GWO),蚂蚁狮子优化器(ALO),多功能优化器(MVO),蜻蜓算法(DA),蛾火焰算法(MFO),正弦余弦算法(SCA)和鲸鱼优化算法(WOA) 。(This is the latest optimization toolbox in MATLAB, which optimizes your problem using seven algorithms recently proposed. The algorithms provided in this toolbox include: GWO, ALO, MVO, DA, MFO, SCA and WOA.)
    2019-03-27 14:54:43下载
    积分:1
  • 696518资源总数
  • 106005会员总数
  • 36今日下载