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  1. m

    PYTHON code and MATLAB code for "Multi-fidelity Meta-optimization for Nature...

    • data.mendeley.com
    Updated Jul 24, 2020
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    Hui Li (2020). PYTHON code and MATLAB code for "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms" [Dataset]. http://doi.org/10.17632/pj6d526kzm.2
    Explore at:
    Unique identifier
    https://doi.org/10.17632/pj6d526kzm.2
    Dataset updated
    Jul 24, 2020
    Authors
    Hui Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    (version 2)

    We add the MATLAB version (multi-fidelity-parameter-tuning-matlab.zip) , hoping researchers who program with MATLAB will find it helpful.

    The structure of the MATLAB code is:

    1. Algorithm (Algorithm.m): 1.1 Basic Algorithm: 1.1.1 PSO.m 1.1.2 GWO.m 2.2 Multi-fidelity Parameter Tuning: 2.2.1 FidelityControlFunction.m 2.2.2 MFOptimizedNIO.m 2.2.2.1 MFOptimizedPSO.m 2.2.3 MFMetaGWO.m

    2. Cost Function: 2.1 SphereFunc.m 2.2 CEC14Func.m 2.2.1 input_data 2.2.2 cec14_func.cpp 2.2.3 cec14_func.mexw64

    3. Demo: 3.1 DemoMF.m

    One can run demo as follows:

    1. Go into project root: <YOUR_WORKSPACE>/multi-fidelity-parameter-tuning-matlab
    2. Run the following command in MATLAB window: DemoMF

    One can compile CEC 2014 as follows:

    Run the following command to create CEC 2014 library in MATLAB: mex cec14_func.cpp -DWINDOWS

    (version 1)

    The python code is used in the manuscript "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms" submitted to "Applied soft computing". The programming environment is: Python 3.6 or higher.

    The folders in the package include: 1. algorithms: Basic algorithms, including base class 'Algorithm' and [CS, DE, FOA, GWO, KH, PSO, SSA, WWO, WOA]. 2. applications: An engineering application: source term estimation. 3. benchmarks: Test functions, including base class 'Benchmark', basic test functions and 'CEC2014 Benchmark Suite'. 4. demo: Examples. 5. parameter_tuning: Multi-fidelity meta-NIOs and optimized-NIOs.

    If you prefer using the command line to run the program, please do not forget to manually add the working directory to 'sys.path'.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Hui Li (2020). PYTHON code and MATLAB code for "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms" [Dataset]. http://doi.org/10.17632/pj6d526kzm.2

PYTHON code and MATLAB code for "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms"

Explore at:
Unique identifier
https://doi.org/10.17632/pj6d526kzm.2
Dataset updated
Jul 24, 2020
Authors
Hui Li
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

(version 2)

We add the MATLAB version (multi-fidelity-parameter-tuning-matlab.zip) , hoping researchers who program with MATLAB will find it helpful.

The structure of the MATLAB code is:

  1. Algorithm (Algorithm.m): 1.1 Basic Algorithm: 1.1.1 PSO.m 1.1.2 GWO.m 2.2 Multi-fidelity Parameter Tuning: 2.2.1 FidelityControlFunction.m 2.2.2 MFOptimizedNIO.m 2.2.2.1 MFOptimizedPSO.m 2.2.3 MFMetaGWO.m

  2. Cost Function: 2.1 SphereFunc.m 2.2 CEC14Func.m 2.2.1 input_data 2.2.2 cec14_func.cpp 2.2.3 cec14_func.mexw64

  3. Demo: 3.1 DemoMF.m

One can run demo as follows:

  1. Go into project root: <YOUR_WORKSPACE>/multi-fidelity-parameter-tuning-matlab
  2. Run the following command in MATLAB window: DemoMF

One can compile CEC 2014 as follows:

Run the following command to create CEC 2014 library in MATLAB: mex cec14_func.cpp -DWINDOWS

(version 1)

The python code is used in the manuscript "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms" submitted to "Applied soft computing". The programming environment is: Python 3.6 or higher.

The folders in the package include: 1. algorithms: Basic algorithms, including base class 'Algorithm' and [CS, DE, FOA, GWO, KH, PSO, SSA, WWO, WOA]. 2. applications: An engineering application: source term estimation. 3. benchmarks: Test functions, including base class 'Benchmark', basic test functions and 'CEC2014 Benchmark Suite'. 4. demo: Examples. 5. parameter_tuning: Multi-fidelity meta-NIOs and optimized-NIOs.

If you prefer using the command line to run the program, please do not forget to manually add the working directory to 'sys.path'.

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