基于改进人工搜索群算法的多目标电磁装置优化
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摘要
在介绍多目标优化问题和人工搜索群算法基本原理的基础上,将Pareto理论引入人工搜索群算法中,提出了多目标人工搜索群算法(MOASSA)。同时精选4个两目标函数算例对MOASSA进行测试,通过与其他多目标优化算法比较,证明了MOASSA的有效性。最后,将MOASSA成功应用于双E型交流接触器优化问题。求解结果验证了MOASSA在解决实际工程问题方面的有效性和实用性,为解决电磁领域优化问题提供了一种可靠的方法。
This paper briefly discussed the basic theories of multi-objective optimization problem and artificial searching swarm algorithm. Based on it,the Pareto theory was introduced into the artificial searching swarm algorithm to improve the algorithm and proposed a multi-objective artificial searching swarm algorithm( MOASSA).Then four multi-objective function examples were selected to test the proposed algorithm. By comparing with other multi-objective optimization algorithms,the validity of the MOASSA was proved. Subsequently,taking the double Etype AC contactor as research object,the multi-objective artificial searching swarm algorithm was successfully applied to solve this problem. The result verifies the validity and practicability of the proposed algorithm in solving the practical engineering problems. A reliable method was provided for solving the optimization problems of electromagnetic field.
This paper briefly discussed the basic theories of multi-objective optimization problem and artificial searching swarm algorithm. Based on it,the Pareto theory was introduced into the artificial searching swarm algorithm to improve the algorithm and proposed a multi-objective artificial searching swarm algorithm( MOASSA).Then four multi-objective function examples were selected to test the proposed algorithm. By comparing with other multi-objective optimization algorithms,the validity of the MOASSA was proved. Subsequently,taking the double Etype AC contactor as research object,the multi-objective artificial searching swarm algorithm was successfully applied to solve this problem. The result verifies the validity and practicability of the proposed algorithm in solving the practical engineering problems. A reliable method was provided for solving the optimization problems of electromagnetic field.
引文
[1]COELLO C A C,LAMONT G B,VAN VELDHUISEN D A.Evolutionary algorithms for solving multiobjective problems[M].Berlin:Springer,2007.
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[5]COELLO C A C,PULIDO G T,LECHUGA M S.Handling multiple objectives with particle swarm optimization[J].IEEE Transactionson Evolutionary Computation,2004,8(3):256-279.
[6]CHEN T G,GUO W.Artificial searching swarm algorithm and its performance analysis[J].Applied Mathematics,2012,3(10):1435-1441.
[7]CHEN T G,PANG L L,DU J,et al.Artificial searching swarm algorithm for solving constrainedoptimization problems[J].ICIS,2009(1):562-565.
[8]CHEN T G.A simulative bionic intelligent optimization algorithm:artificial searching swarm algorithm and its performance analsis[C]//CSO,2009,2:864-866.
[9]王宁,杨仕友.电磁场逆问题数值分析的多目标量子进化算法[J].电工技术学报,2014,29(5):49-53.
[10]陈堂功,郭旺,高智箭,等.基于人工搜索群算法的电磁装置优化设计[J].低压电器,2012,(23):7-10.
[11]张璐,周建丽.军事医学救援中信息情报工作的几点做法[J].西部医学,2009(21):45-49.
[12]CHEN T G.A simulative bionic intelligent optimization algorithm:Artificial searching swarm algorithm and its performance analysis[C]//CSO2009,2009,2:864-866.
[13]CHEN T G,PANG L L,DU J,et al.Artificial searching swarm algorithm for solving constrained optimization problems[C]//ICIS2009,2009,1:562-565.
[14]CHEN T G,WANG Y H,PANG L L,et al.An improved artificial searching swarm algorithm and its performance analysis[C]//ICCMS2010,2010,3:260-263.
[15]谢炳兴.静止无功补偿装置中PID参数的优化[D].天津:河北工业大学,2014.
[16]郭旺.行波感应加热装置优化设计[D].天津:河北工业大学,2012.
[17]刘亚贤.基于改进人工搜索群算法的电磁装置优化设计[D].天津:河北工业大学,2014.
[18]DEB K,THIELE L,LAUMANNS M.,et al.Scalable multi-objective optimization test problems[C]//In Proceedings of the 2002 Congress on Evolutionary Computation,CEC’02.,2002,1:825-830.
[19]ZITZLER E,THIELE L.An evolutionary algorithm for multiobjective optimization:The Strength Pareto Approach[R]Zwich:TIK-Report,1998.
[20]雷德明,严新平.多目标智能优化算法及其应用[M].北京:科学出版社,2009.
[21]付丹丹.基于改进粒子群算法的多目标优化研究[D].哈尔滨:哈尔滨工程大学,2012.
[22]管婷婷.多目标粒子群算法在物流配送中的应用研究[D].南昌:南昌大学,2012.
[23]郑延玲.改进的粒子群算法在多目标优化问题上的研究和应用[D].郑州:郑州大学,2014.
[24]黄冀卓,王湛,马人乐.一种新的求解约束多目标优化问题的遗传算法[J].计算机工程与应用,2006,42(23):47-51.
[2]AIMIN Z,Bo Y Q,HUI L,et al.Multi-objective evolutionary algorithms:A survey of the state of the art[J].Swarm and Evolutionary Computation,2011(1):32-49.
[3]FONSECA C M,FLEMING P J.Genetic algorithms for multiobjective optimization:Formulation discussion and Generalization[C]//Proceedings of the 5th International Conference on Genetic Algorithms,1993:416-423.
[4]DEB K,AGRAWAL S,PRATAP A,et al.A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization:NSGA-II[J].Lecture Notes in Computer Science,2000,1917:849-858.
[5]COELLO C A C,PULIDO G T,LECHUGA M S.Handling multiple objectives with particle swarm optimization[J].IEEE Transactionson Evolutionary Computation,2004,8(3):256-279.
[6]CHEN T G,GUO W.Artificial searching swarm algorithm and its performance analysis[J].Applied Mathematics,2012,3(10):1435-1441.
[7]CHEN T G,PANG L L,DU J,et al.Artificial searching swarm algorithm for solving constrainedoptimization problems[J].ICIS,2009(1):562-565.
[8]CHEN T G.A simulative bionic intelligent optimization algorithm:artificial searching swarm algorithm and its performance analsis[C]//CSO,2009,2:864-866.
[9]王宁,杨仕友.电磁场逆问题数值分析的多目标量子进化算法[J].电工技术学报,2014,29(5):49-53.
[10]陈堂功,郭旺,高智箭,等.基于人工搜索群算法的电磁装置优化设计[J].低压电器,2012,(23):7-10.
[11]张璐,周建丽.军事医学救援中信息情报工作的几点做法[J].西部医学,2009(21):45-49.
[12]CHEN T G.A simulative bionic intelligent optimization algorithm:Artificial searching swarm algorithm and its performance analysis[C]//CSO2009,2009,2:864-866.
[13]CHEN T G,PANG L L,DU J,et al.Artificial searching swarm algorithm for solving constrained optimization problems[C]//ICIS2009,2009,1:562-565.
[14]CHEN T G,WANG Y H,PANG L L,et al.An improved artificial searching swarm algorithm and its performance analysis[C]//ICCMS2010,2010,3:260-263.
[15]谢炳兴.静止无功补偿装置中PID参数的优化[D].天津:河北工业大学,2014.
[16]郭旺.行波感应加热装置优化设计[D].天津:河北工业大学,2012.
[17]刘亚贤.基于改进人工搜索群算法的电磁装置优化设计[D].天津:河北工业大学,2014.
[18]DEB K,THIELE L,LAUMANNS M.,et al.Scalable multi-objective optimization test problems[C]//In Proceedings of the 2002 Congress on Evolutionary Computation,CEC’02.,2002,1:825-830.
[19]ZITZLER E,THIELE L.An evolutionary algorithm for multiobjective optimization:The Strength Pareto Approach[R]Zwich:TIK-Report,1998.
[20]雷德明,严新平.多目标智能优化算法及其应用[M].北京:科学出版社,2009.
[21]付丹丹.基于改进粒子群算法的多目标优化研究[D].哈尔滨:哈尔滨工程大学,2012.
[22]管婷婷.多目标粒子群算法在物流配送中的应用研究[D].南昌:南昌大学,2012.
[23]郑延玲.改进的粒子群算法在多目标优化问题上的研究和应用[D].郑州:郑州大学,2014.
[24]黄冀卓,王湛,马人乐.一种新的求解约束多目标优化问题的遗传算法[J].计算机工程与应用,2006,42(23):47-51.