Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization
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- 作者：Kunjie Yua ; yukunjie1990@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Xin Wangb ; wangxin26@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Zhenlei Wanga ; wangzhen_l@163.com" class="auth_mail" title="E-mail the corresponding author ; wangzhen_l@ecust.edu.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Particle swarm optimization ; Learning strategy ; Space transformation ; Ethylene cracking furnace
- 刊名：Knowledge-Based Systems
- 出版年：2016
- 出版时间：15 March 2016
- 年：2016
- 卷：96
- 期：Complete
- 页码：156-170
- 全文大小：889 K
文摘
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A new variant of PSO, abbreviated as MLPSO-STP, is proposed.
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A novel learning strategy is used to enhance the global search ability.
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Space transformation perturbation is used to obtain better solutions.
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MLPSO-STP outperforms its peers in terms of searching accuracy and reliability.
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MLPSO-STP is used to optimize the operating conditions of ethylene cracking furnace.