Cost Optimization in Neural Network using Whale Swarm Algorithm with Batched Gradient Descent Optimizer
Chinnasamy Karthikeyan, E Sreedevi, Naveen Kumar, E Vamsidhar, T. Rajesh Kumar, D. Vijendra Babu · IOP Conference Series Materials Science and Engineering · 2020
Abstract Optimization algorithms are liable for sinking the losses and to give the most precise outcomes conceivable. Optimizers are utilized to modify the properties of neural network, for example, training rate and weights are used to reduce the losses. Optimization means a procedure of obtaining a global optimal solution for a given problem under given conditions. The real-world problems in the scientific fields, such as engineering design and economic planning, mostly are multimodal, high-dimensional, disconnected, and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using traditional method based on gradient. Nature-inspired algorithms are becoming delightful in resolving mathematical optimization problems, like multiprocessor scheduling problem, vehicle routing and classification problems etc. In this manuscript, Whale Swarm Optimization algorithm on optimizing the neural networks, one of the meta-heuristic algorithms is applied to analysis of the cardiovascular disease dataset and compares the performance with Gradient Descent and RMSprop optimization techniques.