东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (10): 1377-1380.DOI: -

• 论著 • 上一篇    下一篇

一种新的多目标粒子群算法的研究与应用

王小刚;李明杰;王福利;胡伦;   

  1. 东北大学信息科学与工程学院;江西铜业集团公司贵溪冶炼厂;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-10-15 发布日期:2013-06-22
  • 通讯作者: Wang, X.-G.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60374003)

Study on a new MOPSO and its applications

Wang, Xiao-Gang (1); Li, Ming-Jie (1); Wang, Fu-Li (1); Hu, Lun (2)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China; (2) Guixi Smelter, Jiangxi Copper Group, Guixi 335424, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-10-15 Published:2013-06-22
  • Contact: Wang, X.-G.
  • About author:-
  • Supported by:
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摘要： 基于多目标粒子群优化算法的研究成果,以提升多目标优化方法性能为目标,结合铜电解稳态优化工程实际,提出了一种新的多目标优化粒子群算法.该方法采用可变的外部集策略和快速排序方法来减少优化计算量,采用拥挤度算子和强支配关系保证结果良好的分布性.测试函数的仿真对比分析及对铜电解实际过程数据的优化运行结果均表明该方法在收敛性、解的分布性及计算效率方面具有良好品质,特别是在求解三目标优化问题时的突出表现,表明这种算法在多目标优化领域具有更广泛的适用性.

关键词: 多目标优化, 粒子群算法, 外部集, 铜电解

Abstract: To update the performance of the multi-objective optimization method, a new MOPSO (multi-objective particle swarm optimization) algorithm is proposed on the basis of earlier works relevant to particle swarm optimization and in combination with the optimization of a specific copper electrolysis process. Introducing the size-variable external data set and fast sorting to reduce the time for computation, both the congestion operator and strong predominance are used in the algorithm to provide a good diversity in its solutions. The new algorithm thus shows better convergence, solution diversity and computation efficiency especially the superiority in solving the three-objective optimization problems. All the advantages mentioned above are demonstrated by the simulation/comparison results of testing functions and the optimization of real data recorded in copper electrolysis process. The broader applicability in multi-objective optimization field of the proposed algorithm is thus proved.

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