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

Published: 24 July 2020| Version 2 | DOI: 10.17632/pj6d526kzm.2
Hui Li,
Zhiguo Huang


(version 2) We add the MATLAB version ( , 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 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'.



Beijing University of Chemical Technology


Continuous Optimization, Biologically Inspired Engineering