Hybrid particle swarm cooperative optimization algorithm and its application to MBC in alumina production  

作　　者：Shengli Song Li Kong Yong Gan Rijian Su 

机构地区：[1]Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [2]College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

出　　处：《Progress in Natural Science:Materials International》2008年第11期1423-1428,共6页自然科学进展·国际材料（英文版）

基　　金：supported in part by the National High Technology Research and Development Program of China(Grant No.2004AA1Z2420)

摘　　要：An effective hybrid particle swarm cooperative optimization （HPSCO） algorithm combining simulated annealing method and simplex method is proposed. The main idea is to divide particle swarm into several sub-groups and achieve optimization through cooperativeness of different sub-groups among the groups. The proposed algorithm is tested by benchmark functions and applied to material balance computation （MBC） in alumina production. Results show that HPSCO, with both a better stability and a steady convergence, has faster convergence speed and higher global convergence ability than the single method and the improved particle swarm optimization method. Most importantly, results demonstrate that HPSCO is more feasible and efficient than other algorithms in MBC.联合模仿的退火的方法和单一的方法的一个有效混合粒子群合作社优化(HPSCO ) 算法被建议。主要想法是把粒子群划分成几亚群并且在这些组之中通过不同亚群的合作海角完成优化。建议算法被基准功能测试并且在氧化铝生产适用于物料均衡计算(MBC ) 。结果证明与更好的稳定性和稳定的集中， HPSCO 比单个方法和改进粒子群有更快的集中速度和更高全球的集中能力优化方法。最重要地，结果证明 HPSCO 比在 MBC 的另外的算法更可行、有效。

关 键 词：PSO Simulated annealing SIMPLEX Cooperative optimization 

分 类 号：O242.23[理学—计算数学]