Descirption
In this repository, we particularly focused on ANN trained by Particle Swarm Optimization (ANN-PSO), in which the local-best and global-best particle positions represent possible solutions to the set of network weights. The global-best position of the swarm, which corresponds to the minimum cost function over time, is determined in our work by minimizing a new non-extensive cross-entropy error cost function. To know more about it please follow the link: https://link.springer.com/article/10.1007/s00500-020-05080-7. Cite our paper, if you are using it in your research work.
Below is the citaion of our paper, in case you are using our code:
Susan, Seba, Rohit Ranjan, Udyant Taluja, Shivang Rai, and Pranav Agarwal. "Global-best optimization of ANN trained by PSO using the non-extensive cross-entropy with Gaussian gain." Soft Computing (2020): 1-13