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

(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'.