Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search

The network architecture search technique is nowadays becoming the next generation paradigm of architectural engineering, which could free experts from trials and errors while achieving state-of-the-art performances in lots of applications such as image classification and language modeling. It is immensely crucial for deploying deep networks on a wide range of mobile devices with limited computing resources to provide more flexible service. In this paper, a novel multi-objective oriented algorithm called MOCS-Net for mobile devices network architecture search is proposed. In particular, the search space is compact and flexible which leverages good virtues from efficient mobile CNNs and is block-wise constructed by different stacked blocks. Moreover, an enhanced multi-objective cuckoo algorithm is incorporated, in which mutation is achieved by Lévy flights which are performed at the block level. Experimental results suggest that MOCS-Net could find competitive neural architectures on ImageNet with a better trade-off among various competing objectives compared with other state-of-the-art methods. Meanwhile, these results show the effectiveness of proposed MOCS-Net and the promise to further the use of MOCS-Net in various deep-learning paradigms.