基于记忆分子动理论多目标优化算法的经济环境调度
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摘要
针对电力系统经济调度问题的多目标特性,提出一种记忆分子动理论多目标优化算法(Multi-objective MemoryKinetic-MoleculartheoryOptimizationAlgorithm,MOMKMTOA)。该算法在分子动理论算法(Kinetic-Molecular theory Optimization Algorithm, KMTOA)的基础上引入记忆原理记忆,设计记忆更新与遗忘模型以提高算法的多样性,并提出记忆精英选择策略从当代解集中随机选择领导精英以避免陷入局部最优。通过CEC09标准测试函数和IEEE-30节点的两个案例验证说明,MOMKMTOA算法在求解高维复杂的多目标经济调度问题上具有一定的可行性和有效性。
Multi-Objective memory Kinetic-Molecular Theory Optimization Algorithm(MOMKMTOA) is proposed to solve the multi-objective characteristics of power system economic dispatching problem.To improve the diversity of the algorithm,the updated operator of memory and the forgotten operator of memory based on Kinetic-Molecular theory Optimization Algorithm(KMTOA) is designed.To avoid falling into local optimum,the selection strategy of leading elite which randomly select the leader from the first-level memory frontier is proposed.Test function of CEC09 and two cases of IEEE-30 nodes show that MOMKMTOA algorithm is feasible and effective in solving multi-objective economic dispatching problems with high-dimensional complexity.
Multi-Objective memory Kinetic-Molecular Theory Optimization Algorithm(MOMKMTOA) is proposed to solve the multi-objective characteristics of power system economic dispatching problem.To improve the diversity of the algorithm,the updated operator of memory and the forgotten operator of memory based on Kinetic-Molecular theory Optimization Algorithm(KMTOA) is designed.To avoid falling into local optimum,the selection strategy of leading elite which randomly select the leader from the first-level memory frontier is proposed.Test function of CEC09 and two cases of IEEE-30 nodes show that MOMKMTOA algorithm is feasible and effective in solving multi-objective economic dispatching problems with high-dimensional complexity.
引文
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[5]庄怀东,吴红斌,刘海涛,等.含电动汽车的微网系统多目标经济调度[J].电工技术学报,2014(s1):365~373.
[6]张秀霞,王爽心,吴冠玮.基于混沌遗传和模糊决策算法的多目标负荷经济调度[J].电力自动化设备,2009,29(1):94~99.
[7]刘刚,彭春华,相龙阳.采用改进型多目标粒子群算法的电力系统环境经济调度[J].电网技术,2011(7):139~144.
[8]Fan C D,Ouyang H L,Zhang Y,et al.Optimization algorithm based on kinetic-molecular theory[J].Journal of Central South University,2013,20(12):3504~3512.
[9]Sindhya K,Sinha A,Deb K,et al.“Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems,”[C].Eleventh Conference on Congress on Evolutionary Computation(CEC),Trondheim,Norway,vol.,pp.2919~2926,18~21 May 2009.
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[11]Kishor A,Singh P K,Prakash J.NSABC:Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering[J].Neurocomputing,2016,216:514~533.
[12]Mu C,Jiao L,Liu Y,et al.Multiobjective nondominated neighbor coevolutionary algorithm with elite population[J].Soft Computing,2015,19(5):1329~1349.
[13]Chen F,Huang G H,Fan Y R,et al.A nonlinear fractional programming approach for environmental-economic power dispatch[J].International Journal of Electrical Power&Energy Systems,2016,78:463~469.
[14]Abido M A.Multiobjective evolutionary algorithms for electric power dispatch problem[J].IEEE Transactions on Evolutionary Computation,2006,10(3):315~329.
[15]King R T F A,Rughooputh H C S,Deb K.“Evolutionary Multi-objective Environmental/Economic Dispatch:Stochastic Versus Deterministic Approaches,”[C].Third International Conference on Evolutionary Multi-Criterion Optimization(EMO),Guanajuato,Mexico,vol.,pp.677~691.9~11 Mar.,2005.
[16]Agrawal S,Panigrahi B K,Tiwari M K.Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch[J].IEEE Transactions on Evolutionary Computation,2008,12(5):529~541.
[17]Gong D W,Zhang Y,Qi C L.Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm[J].International Journal of Electrical Power&Energy Systems,2010,32(6):607~614.
[18]贾艳芳,易灵芝,李胜兵.基于多目标分子动理论的楼宇负荷用电调度优化[J].电网技术,2018,42(5).