Community Collaboration Improves Blood Pressure Control by 13.5% in a Medium-Sized Metropolitan Area

Particle swarm optimization (PSO) has been successful in solving many benchmark test functions and real-world industrial problems over the past decades. However, the performance of PSO is significantly affected by the choice of control parameters and the design of velocity updating strategies. Therefore, a self-adaptive PSO with multiple velocity strategies (SAPSO-MVS) is proposed to improve PSO performance. SAPSO-MVS can generate self-adaptive control parameters during the entire evolution process and use a new velocity updating strategy. To test the effectiveness of the proposed algorithm, SAPSO-MVS is compared with 8 well-known state-of-the-art PSO variants and 3 famous non-PSO algorithms on a set of benchmark test functions. Simulation results show that the average performance of the proposed algorithm is better than the performances of other compared algorithms. SAPSO-MVS is also used to optimize the 10 operation conditions of the p-Xylene oxidation reaction process. Satisfactory results are obtained.