高效动态微型多目标遗传算法及其应用
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摘要
针对具有多个优化目标且目标和约束会随时间(环境)变化的动态优化问题,提出了一种高效的动态多目标遗传算法。该算法在微型遗传算法的基础上,针对动态优化问题的特点,加入一种环境检测机制,以实现对不同环境下的Pareto最优解集的快速求取。通过对四种不同类型的动态多目标优化测试问题的求解,并与经典算法DNSGAII进行对比,验证了该算法具有较高的求解效率和求解精度。最后,将该算法应用于一个动态的垃圾焚烧系统的PID控制参数的优化问题中,将阶跃响应下的最大超调量和上升时间作为优化目标,对PID比例系数和微分系数两个参数进行优化,结果表明,算法能够快速求出不同环境下的Pareto最优解集。
An efficient dynamic multiobjective genetic algorithm based on the micro genetic algorithm was suggested to solve dynamic multiobjective optimization problems, which optimization objectives and constraints changed over time(Environment). An environmental detection mechanism was employed to efficiently obtain the Pareto optimal sets of different environments of the dynamic optimization problem. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to non-dominated sorting genetic algorithmⅡ. Eventually, this approach is applied to the PID control of a dynamic refuse incineration system for minimum the maximum overshoot and rise time of step response.
An efficient dynamic multiobjective genetic algorithm based on the micro genetic algorithm was suggested to solve dynamic multiobjective optimization problems, which optimization objectives and constraints changed over time(Environment). An environmental detection mechanism was employed to efficiently obtain the Pareto optimal sets of different environments of the dynamic optimization problem. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to non-dominated sorting genetic algorithmⅡ. Eventually, this approach is applied to the PID control of a dynamic refuse incineration system for minimum the maximum overshoot and rise time of step response.
引文
[1]Deb K,Bhaskara U R N,Karthik S.Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II:A Case Study on Hydro-thermal Power Scheduling[C]//Proc.of the4th International Conference on Evolutionary MultiCriterion Optimization,LNCS4403,Matsushima,Japan,October25-28,2007.Matsushima,Japan:Springer-Verlag,2007.
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[7]Zhuhong Zhang.Multiobjective Optimization Immune Algorithm in Dynamic Environments and Its Application to Greenhouse Control[J].Applied Soft Computing(S1568-4946),2008,8(2):959-971.
[8]尚荣华,焦李成,公茂果,等.免疫克隆算法求解动态多目标优[J].软件学报,2007,18(11):2700-2711.
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[11]刘淳安,王宇平.基于新模型的动态多目标优化进化算法[J].计算机研究与发展,2008,45(4):603-611.
[12]G P Liu,X Han,C Jiang.An Efficient Multiobjective Optimization Approach Based on the Micro Genetic Algorithm and Its Application[J].International Journal of Mechanics and Materials in Design(S1569-1713),2006,8(1):37-49.
[13]Yaochu Jin,Bernhard Sendhoff.Constructing Dynamic Optimization Test Problems Using the Multi-objective Optimization Concept[C]//G R Raidl,et a1.EvoWorkshops 2004,LNCS3005.Berlin,Germany:Springer-Verlag,2004:525-536.
[14]Farina M,Deb K,Amato P.Dynamic Multiobjective Optimization Problems:Test Cases,Approximations,and applications[J].IEEE Transactions on Evolutionary Computation(S1089-778X),2004,8(5):425-442.
[15]Deb K,Pratap A,Agarwal S,et al.A Fast and Elitist Multiobjective Genetic Algorithm:NSGAⅡ[J].IEEE Transactions on Evolutionary Computation(S1089-778X),2002,6(2):182-197.
[16]李小凡.基于RBF神经网络整定的PID控制器设计[J].兵工自动化,2009,28(1):45-46.
[17]陶永华,尹怡欣,葛芦生.新型PID控制及其应用[M].北京:机械工业出版社,1998.
[18]湛锋,魏星,郭建全,等.基于改进粒子群优化算法的PID参数整定[J].继电器,2005,33(19):23-27.
[2]Mitra K,Raha S,Majumdar S.Multiobjective Dynamic Optimization of A Semi-batch epoxy Polymerization Process[J].Computers&Chemical Engineering(S0098-1354),2004,28(12):2583-2594.
[3]Palaniappan S,Zein-Sabatto S,Sekmen A.Dynamic Multiobjective Optimization of War Resource Allocation Using Adaptive Genetic Algorithms[C]//Proceedings of the Southeastcon.USA:IEEE,IEEE,2001:160-165.
[4]Jin Y,Olhofer M,Sendhoff B.Dynamic Weighted Aggregation for Evolutionary Multi-objective Optimization:Why Does It Work and How?[C]//Proceedings of the Genetic and Evolutionary Computation Conference.Germany:Morgan Kaufmann,2001:1042-1049.
[5]Bingul Z.Adaptive Genetic Algorithms Applied to Dynamic Multiobjective Problems,Appl[J].Soft Comput(S1432-7643),2007,7(3):791-799.
[6]Iason H,David W.Dynamic Multiobjective Optimization with Evolutionary Algorithms:A Forward Looking Approach[C]//Proc.of the GECCO,06,Washington,USA,January 2-4,2006.Washington,USA:IEEE Press,2006:1201-1208.
[7]Zhuhong Zhang.Multiobjective Optimization Immune Algorithm in Dynamic Environments and Its Application to Greenhouse Control[J].Applied Soft Computing(S1568-4946),2008,8(2):959-971.
[8]尚荣华,焦李成,公茂果,等.免疫克隆算法求解动态多目标优[J].软件学报,2007,18(11):2700-2711.
[9]尚荣华,马文萍,焦李成等.免疫遗忘动态多目标优化[J].哈尔滨工程大学学报,2006,27(S):205-209.
[10]刘淳安,王宇平.解动态多目标优化问题的进化算法及其收敛性分析[J].电子学报,2007,35(6):1118-1121.
[11]刘淳安,王宇平.基于新模型的动态多目标优化进化算法[J].计算机研究与发展,2008,45(4):603-611.
[12]G P Liu,X Han,C Jiang.An Efficient Multiobjective Optimization Approach Based on the Micro Genetic Algorithm and Its Application[J].International Journal of Mechanics and Materials in Design(S1569-1713),2006,8(1):37-49.
[13]Yaochu Jin,Bernhard Sendhoff.Constructing Dynamic Optimization Test Problems Using the Multi-objective Optimization Concept[C]//G R Raidl,et a1.EvoWorkshops 2004,LNCS3005.Berlin,Germany:Springer-Verlag,2004:525-536.
[14]Farina M,Deb K,Amato P.Dynamic Multiobjective Optimization Problems:Test Cases,Approximations,and applications[J].IEEE Transactions on Evolutionary Computation(S1089-778X),2004,8(5):425-442.
[15]Deb K,Pratap A,Agarwal S,et al.A Fast and Elitist Multiobjective Genetic Algorithm:NSGAⅡ[J].IEEE Transactions on Evolutionary Computation(S1089-778X),2002,6(2):182-197.
[16]李小凡.基于RBF神经网络整定的PID控制器设计[J].兵工自动化,2009,28(1):45-46.
[17]陶永华,尹怡欣,葛芦生.新型PID控制及其应用[M].北京:机械工业出版社,1998.
[18]湛锋,魏星,郭建全,等.基于改进粒子群优化算法的PID参数整定[J].继电器,2005,33(19):23-27.