一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化

耿志强; 韩永明; 朱群雄

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一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化

过程系统工程 | 更新时间：2025-12-10

- 一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化
- A new multi-swarms competitive particle swarm optimization algorithm and its application for operational optimization in high density polyethylene equipment
- 化工学报 2011年第8期页码：2176-2181
- 作者机构：
  
  北京化工大学信息科学与技术学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： P18
- 网络首发：2011-08-05，
  
  纸质出版：2011-08-05
- 稿件说明：
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耿志强, 韩永明, 朱群雄. 一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化[J]. 化工学报, 2011,(8):2176-2181.

GENG Zhiqiang, HAN Yongming, ZHU Qunxiong. A new multi-swarms competitive particle swarm optimization algorithm and its application for operational optimization in high density polyethylene equipment[J]. CIESC Journal, 2011, (8): 2176-2181.
耿志强, 韩永明, 朱群雄. 一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化[J]. 化工学报, 2011,(8):2176-2181. DOI：

GENG Zhiqiang, HAN Yongming, ZHU Qunxiong. A new multi-swarms competitive particle swarm optimization algorithm and its application for operational optimization in high density polyethylene equipment[J]. CIESC Journal, 2011, (8): 2176-2181. DOI：

摘要

利用模糊C均值聚类对种群自适应划分，提出一种基于模糊C均值聚类的多群竞争粒子群优化算法。根据种群规模选择不同的寻优策略，规模大者采用标准粒子群算法寻优，规模小者在最优解邻域随机搜索，增大跳出局部最优概率。在每个聚类内部，个体相互通信，通过竞争学习分别找到各聚类种群的适应值，按照不同聚类的适应值排序，再把适应值小者向其邻近的适应值大者融合，通过种群间的竞争保证种群向最优解搜索。该算法避免陷入局部最优，提高了算法的全局搜索能力，通过标准函数验证了算法的有效性。最后，把提出的优化算法应用到高密度聚乙烯装置（HDPE）乙烯单体总消耗的优化操作，实际应用效果良好。

Abstract

The fuzzy C means clustering is used to divide the swarms adaptively

and a fuzzy C means multi-swarms competitive PSO(FCMCPSO)algorithm is proposed.According to the scale of the swarms to select different optimal strategies

the swarm of large scale uses the standard particle swarm algorithm to optimize

and the swarm of small scale randomly searches in the optimal solution neighborhood

increasing the probability of jumping out of the local optimization.Within every clustering

the adaptive value of every clustering swarm by competitive learning is respectively found and arranged the order of the different adaptive value

and then the swarm of small adaptive value integrates with the neighboring swarm of large adaptive value

ensuring the particle swarms to search towards the optimal solution by the competition in the swarms.The validity was tested by the benchmark functions to improve the global search capability.At last

the proposed algorithm was used to optimize the operational conditions of high density polyethylene(HDPE)equipment in order to decrease the consumption of ethylene.

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