201. Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization.
- Author
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Yu, Kunjie, Wang, Xin, and Wang, Zhenlei
- Subjects
- *
PARTICLE swarm optimization , *PERTURBATION theory , *ETHYLENE , *FURNACES , *MACHINE learning , *SEARCH algorithms - Abstract
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 100 p % 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. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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