一种铜电解生产过程的多目标量子粒子群优化  

Copper Electro-refining Multi-objective Optimization Using Improved Quantum-behaved Particle Swarm Optimization Algorithm

作　　者：逄珊[1] 杨欣毅[2] 

机构地区：[1]鲁东大学信息与电气工程学院,烟台264025 [2]海军航空工程学院飞行器工程系,烟台264001

出　　处：《系统仿真学报》2014年第11期2714-2721,共8页Journal of System Simulation

摘　　要：为解决铜电解生产过程能耗高的问题,采用机理和辨识混合建模方法建立铜电解过程多目标优化模型。针对量子粒子群算法求解多目标优化问题存在的多样性差、分布不均甚至局部收敛的问题,提出了一种基于信息熵和混沌变异的改进多目标量子粒子群算法,对测试函数的计算结果表明所提出算法在求解分布性方面要明显优于其他经典算法。利用改进算法在给定电价和分时电价情况下进行铜电解过程多目标优化仿真,获得了生产工艺参数的最优组合,有效的降低能耗,为电解铜的生产过程优化提供了详细的指导和理论依据。In order to reduce the electricity consumption, the multi objective model of copper electro- refining was established using the hybrid of mechanism modeling method and Identification modeling me- thod. To solve the problems of the loss of diversity, bad distributed solutions set and local convergence in quantum-behaved particle swarm optimization （QPSO） algorithm, a novel QPSO based on information entropy and chaotic mutation was proposed in this study. Results on benchmark functions prove the new algorithm has better performance in solutions＇ distribution compared with other classical algorithms. The multi objective model was solved using the proposed algorithm on the given electric price condition and time-varying electric price condition. The optimized combination of the product process parameters was obtained, which reduces the electricity consumption and provides a detailed guidance for the optimization of copper electro-refining.

关 键 词：电解铜 建模 多目标优化 量子粒子群 信息熵 混沌 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]