多目标P系统仿生优化算法  

Multi-objective Bio-inspired Optimization Algorithm Based on a P System

作　　者：杨世品[1,2] 陆小华[2] 薄翠梅[1] 李丽娟[1] 徐启[1] YANG Shipin;LU Xiaohua;BO Cuimei;LI Lijuan;XU Qi(College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816,China;College of Chemical Engineering, Nanjing Tech University, Nanjing 211816,China)

机构地区：[1]南京工业大学电气工程与控制科学学院,南京211816 [2]南京工业大学化工学院,南京211816

出　　处：《北京工业大学学报》2016年第10期1475-1481,共7页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61403190;61203020);国家"973"计划资助项目(2013CB733500)

摘　　要：为了更加高效地求解多目标优化问题,提出了一种基于P系统的仿生优化算法.算法结合P系统的动态膜结构以增强算法的适应性,同时结合经典的NSGA-Ⅱ拥挤距离选择策略和膜内仿生自噬机制提高算法所得最优Pareto解的多样性.此外,算法内循环中的动态变异、交流及交叉等规则使得所提算法获得的Pareto最优边界与真实Pareto最优前沿的逼近度更高.仿真实验结果表明:该算法处理多目标优化问题时所得解集具有更好的收敛度和多样性.将该算法应用于非最小相位对象的PID控制器的多目标优化设计,获得了较好的系列非劣控制器组,基于搜索结果的PID切换控制策略具有满意的控制效果.To solve the multi-objective optimization problem more efficiently, a P system bio-inspired optimization algorithm was proposed. The algorithm used the dynamic membrane structure to enhance its adaptability. The crowding distance selecting strategy of classical NSGA-II in combination with bionic autophagy mechanism was adopted to strengthen the solutions’ diversity. Moreover, dynamic mutation rule, communication rule and crossover rule in inner loop made the last solutions gained by this method as close as possible to the true Pareto optimal front. Simulation results show that the solution set obtained by this algorithm possesses better convergence and diversity in solving multi-objective optimization problems. Additionally, to control the non-minimum phase processes better, a series of optimal PID controller set are obtained by using this algorithm. Based on these optimal controllers, the PID switch strategy gains a satisfactory performance.

关 键 词：多目标优化 P系统 非支配排序 自噬 PARETO最优 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]