thangle-duc Goto Github PK
Name: Le Duc Thang
Type: User
Company: Vietnamese-German University
Location: Vietnam
Name: Le Duc Thang
Type: User
Company: Vietnamese-German University
Location: Vietnam
We contribute here a Matlab-based optimization software package based on our novel optimization algorithm named Balancing Composite Motion Optimization (BCMO).
We contribute an updated Python version of BCMO Package provided in https://github.com/ThangLe-duc/BCMO_Package. In this package, we introduce an upgraded version named Balancing Composite Motion Optimization with Gaussian Random Walk (GRW-BCMO). The corresponding Matlab version of GRW-BCMO is also provided as the release v1.1 of BCMO package in https://github.com/ThangLe-duc/BCMO_Package/releases/tag/v1.1. In GRW-BCMO, we modify the trial mechanism for determining the instant global point and the best individual via a Gaussian random step biased from the current best individual to exploit its local space. This version will solve the boundary dependency of the original trial mechanism proposed in the previous one. The proposed algorithm is constructed without algorithm-specific parameters, the users can directly apply it to solve any unconstrained optimization problems without hyperparameter tuning. The users are recommended to use Python 3.7 or more. The required libraries for using this package is numpy and matplotlib. The users can easily follow the examples with detailed instructions to implement and modify the codes for personal uses. # Contributors - Thang Le-Duc - Quoc-Hung Nguyen - Hung Nguyen-Xuan Please cite the following reference if the package is useful for your project. # References: Thang Le-Duc, Quoc-Hung Nguyen, H. Nguyen-Xuan, Balancing Composite Motion Optimization, Information Sciences, in press, 2020 https://www.sciencedirect.com/science/article/pii/S0020025520300773
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We contribute here a Matlab-based package for training deep neural networks (DNN)s based on our novel optimization framework named Sequential Motion Optimization (SMO). We select the popular MNIST classification problem to demo in this package. Some detailed definitions and instructions are also presented into the codes to help the users easily modify it for personal research and uses.
This work proposes a Sequential Motion Optimization with Short-term Adaptive Moment Estimation (SMO-SAdam) to train neural networks.
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