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MFOA

于 2020-06-16 发布 文件大小:3694KB
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下载积分: 1 下载次数: 1

代码说明:

  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

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  • Hybrid-GWOPSO-optimization
    灰狼算法和粒子群算法的结合,是两种种群算法的结合,值得一看(The combination of grey Wolf algorithm and particle swarm optimization is a combination of two population algorithms, which is worth a look)
    2021-04-23 02:18:48下载
    积分:1
  • top2 of CEC2017
    说明:  CEC2017前2名的MATLAB算法实现 有EBOwithCMAR和jSO 各种参数都可以调整,包括种群数量、F因子、变异率、交叉率等(The realization of MATLAB algorithm for the top2 of cec217. There are ebowithcmar; JSO Various parameters can be adjusted, including population number, F factor, mutation rate, crossover rate, etc.)
    2020-05-07 16:29:30下载
    积分:1
  • CEC 2017 bound constrained benchmarks
    说明:  CEC2017前几名的MATLAB算法实现 有EBOwithCMAR; jSO; LSHADE_SPACMA; LSHADE-cnEpSin 各种参数都可以调整,包括种群数量、F因子、变异率、交叉率等(The realization of MATLAB algorithm for the top few of cec217. There are ebowithcmar; JSO; lshade_spacma; lshade cnepsin. Various parameters can be adjusted, including population number, F factor, mutation rate, crossover rate, etc.)
    2021-04-21 15:08:49下载
    积分:1
  • yiqunsuanfa
    说明:  蚁群算法的一个函数寻优案例 带约束条件 可运行出来(A function optimization case of ant colony algorithm with constraints can be run out)
    2019-01-13 11:38:18下载
    积分:1
  • 粒子群的寻优机制 pso
    说明:  粒子群算法的寻优机制,另附十余个测试函数。主程序为test_basic(Particle Optimization Algorithm)
    2020-06-24 00:40:02下载
    积分:1
  • 《智能优》汪定伟
    东北大学汪定伟编写的智能优化方法教材,对目前主要的智能优化方法遗传算法、禁忌搜索、蚁群算法和粒子群算法进行了介绍,内容详尽经典,是一本学习智能优化方法的好教材。(a good book about intelligent Optimization)
    2018-07-20 14:18:25下载
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    说明:  这是群智能优化算法,布谷鸟搜索算法,用matlab实现,用函数优化进行测试,简单易懂(This is swarm intelligence optimization algorithm, cuckoo search algorithm, implemented in matlab, test with function optimization, easy to understand)
    2018-10-10 12:24:20下载
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    说明:  新型的智能优化算法,水波优化算法,在基准函数CEC2014上比PSO,BBO,HS等表现要好。(A New Intelligent Optimum Algorithms, Water Wave Optimum Algorithms)
    2019-03-15 20:05:06下载
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    2020-01-07 21:17:43下载
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  • 爬山-遗传-极限学习机
    爬山改进遗传算法,提供更快的收敛速度,并用于优化极限学习机权值(Mountain climbing improved genetic algorithm to provide faster convergence speed and to optimize the weight of extreme learning machine)
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