/code_IQSO_MLP

(Code IQSO-MLP) nQSV-Net: a novel queuing search variant for global space search and workload modeling

Primary LanguagePythonMIT LicenseMIT

Improved Queuing Search Optimization for Global Search

Full experiment with Benchmark Functions Test

  • In the file: full_experiment.pdf

How to read our repository

  • utils: includes helper functions (objective functions used in meta-heuristic algorithms)

    • We have also built a framework for benchmark functions (such as unimodal, multimodal, composition, ... CEC 2014, ...)
    • Check it out: https://pypi.org/project/opfunu/

  • script_test

    • Included each algorithm-run file (Test purpose)
    • To run it, move it to root folder (code_IQSO_MLP) like the file: run_GA.py

  • models: includes all algorithms (4 folders)

    • human_based

    • physics_based

    • swarm_based

    • evolutionary_based

    • The file: root_algo.py is the root for all meta-heuristic algorithms. Because there are lots of common functions among algorithms. So better to have an abstract layer for all algorithms.

    • We are building a library for all the state-of-the-art meta-heuristic algorithms using python.

    • It haven't done yet, but you can check its development version at: https://github.com/thieunguyen5991/metaheuristics

  • How to run?

    • 1st: run file run_multiple_algo.py (run each algorithm 15 times and save the best fitness of each run times and loss of the best among 15 run times into folder: convergence and stability)

    • 2nd: run file get_experiment_infor.py to read saved data from 1st step. Then transform that data into dict type for making latex table then save it in folder: overall/all_algo_infor.pkl

    • 3rd: run file gen_result_tex.py to make latex table from all_algo_infor.pkl file

    • 4th: run file plot_stab_conv.py to draw convergence and stability of 30 functions.

    • To change the parameters of models in: run_multiple_algo.py

Publications

  • If you see our code and data useful and use it, please cites us here

    • Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.

    • Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.

    • Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.

  • The pre-version of our paper and code can be found at: https://github.com/chasebk/

  • Don't hesitate if you have any question about our code and paper via nguyenthieu2102@gmail.com or hoangnghiabao96@gmail.com

License

License: MIT