Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization
作者：
Highlights：
• A new variant of PSO, abbreviated as MLPSO-STP, is proposed.
• A novel learning strategy is used to enhance the global search ability.
• Space transformation perturbation is used to obtain better solutions.
• MLPSO-STP outperforms its peers in terms of searching accuracy and reliability.
• MLPSO-STP is used to optimize the operating conditions of ethylene cracking furnace.
摘要
•A new variant of PSO, abbreviated as MLPSO-STP, is proposed.•A novel learning strategy is used to enhance the global search ability.•Space transformation perturbation is used to obtain better solutions.•MLPSO-STP outperforms its peers in terms of searching accuracy and reliability.•MLPSO-STP is used to optimize the operating conditions of ethylene cracking furnace.
论文关键词：Particle swarm optimization,Learning strategy,Space transformation,Ethylene cracking furnace
论文评审过程：Received 8 April 2015, Revised 24 August 2015, Accepted 26 December 2015, Available online 4 January 2016, Version of Record 4 February 2016.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.12.020
