文献类型：期刊文献

英文题名：Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization

作者：Yu, Kunjie[1];Wang, Xin[2];Wang, Zhenlei[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2016

卷号：96

起止页码：156

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20160301826538);WOS:【SCI-EXPANDED(收录号:WOS:000370907200013)】；

基金：This research was supported by National Key Scientific and Technical Project of China under Grant No. 2015BAF22B02, National Natural Science Foundation of China under Grant Nos. 21276078, 61422303, Fundamental Research Funds for the Central Universities, Shanghai Natural Science Foundation under Grant No. 14ZR1421800, and the State Key Laboratory of Synthetical Automation for Process Industries under Grant No. PAL-N201404.

语种：英文

外文关键词：Particle swarm optimization; Learning strategy; Space transformation; Ethylene cracking furnace

摘要：This paper proposes a new variant of particle swarm optimization (PSO), namely, multiple learning PSO with space transformation perturbation (MLPSO-STP), to improve the performance of PSO. The proposed MLPSO-STP uses a novel learning strategy and STP. The novel learning strategy allows each particle to learn from the average information on the personal historical best position (pbest) of all particles and from the information on multiple best positions that are randomly chosen from the top 100p% of pbest. This learning strategy enables the preservation of swarm diversity to prevent premature convergence. Meanwhile, STP increases the chance to find optimal solutions. The performance of MLPSO-STP is comprehensively evaluated in 21 unimodal and multimodal benchmark functions with or without rotation. Compared with eight popular PSO variants and seven state-of-the-art metaheuristic search algorithms, MLPSO-STP performs more competitively on the majority of the benchmark functions. Finally, MLPSO-STP shows satisfactory performance in optimizing the operating conditions of an ethylene cracking furnace to improve the yields of ethylene and propylene. (C) 2015 Elsevier B.V. All rights reserved.