¹Research Institute of Automation, East China University of Science and Technology, Shanghai, 200237, China
²Shanghai DianJi University, Shanghai, 200240, China

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Abstract—In this paper, a self-government particle swarm optimizer (SGPSO) is proposed to improve the performance of original PSO, in which particle updating depends on local best information searched at anterior runs as well as individual history best and global best at present. To evaluate the novel algorithm, some benchmark functions are employed in comparison with PSO. Experimental results show that the proposed algorithm can search more optimal solution than PSO and indicate the effectiveness of the novel algorithm to solve optimization problems. Finally, the proposed algorithm is applied in soft-sensing the Texaco furnace temperature. It is convinced that SGPSO based soft sensor is very capable of real-time assessment of the furnace temperature in the Texaco gasification process.

Index Terms—Particle swarm optimization, self-government, texaco gasification.

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Cite: Weitian Lin, Xingsheng Gu, Zhigang Lian, Yufa Xu, Bin Jiao, "A Self-Government Particle Swarm Optimization Algorithm and Its Application in Texaco Gasification," Journal of Software vol. 8, no. 2, pp. 472-479, 2013.