Dual-drive opposition-based non-inertial particle swarm optimization for deep learning in IoTs

Since the particle swarm optimization (PSO) was proposed to overcome the inherent defects of PSO such as premature convergence and dependent on parameters settings, different PSO variants are devised to optimize different complex optimization problems; NOPSO is an excellent representative among them. This paper ensembles the three types of velocity update formulas proposed in NOPSO and presents a dual-drive opposition-based non-inertial PSO to improve the robustness of algorithm while ensuring the searching efficiency and solving accuracy of optimization procedure. Two main strategies are introduced in the new algorithm: (1) a dual-drive velocity update formula (DDVM) is proposed to control move of particles and (2) an elite differential evolutionary mutation strategy (EDEM) is devised to help particles escape from local optimum. The modified algorithm with two above strategies is proved to be competitive compared with some state-of-the-art OBL-based PSO including NOPSO and can be effectively applied to deep learning in IoTs in the foreseeable future.