Article ID Journal Published Year Pages File Type
1180638 Chemometrics and Intelligent Laboratory Systems 2014 11 Pages PDF
Abstract

•A new velocity updating strategy is proposed.•The control parameters can be automatically adjusted in SAPSO-MVS.•Different velocity strategies are used in the proposed algorithm.•SAPSO-MVS is used to solve the PX oxidation reaction process optimization.

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.

Related Topics
Physical Sciences and Engineering Chemistry Analytical Chemistry
Authors
, ,