Design a multi-objective optimization with dynamic and global filter pruning strategy for convolutional neural network

In recent years, the Deep Neural Networks (DNN) have managed tremendous growth and widespread applications. However, their applicability in embedded and mobile devices has been constrained by its complex arrangement, maximal computation, and the storage requirements. This paper designed an Evolutionary Multi-Objective Optimization (EMO) through a Dynamic and Global Filter Pruning (DG-FP) approach (EMO-DGFP) for an effective Convolutional Neural Network (CNN). Initially, Train the CNN in the Python system, then use the DG-FP approach to remove unnecessary filters. Furthermore, the CNN filters were adjusted using an EMO technique. The filter pruning issue has been resolved using an EMO based on non-dominated sorting. Then fine-tuning process was performed to regain its accuracy. The developed model efficiency has been tested against other widely used models, and the results showed 99.96% accuracy, 2% mistake rate, 2.6% compression ratio, and 80.67% pruning rate.