Python code for "Multi-fidelity Meta-optimization for Nature Inspired Optimization Algorithms"
Description
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'.
Files
Institutions
- Beijing University of Chemical Technology