基于邻域正交交叉算子的混合蛙跳算法研究与应用
|
摘要
混合蛙跳算法(SFLA)是由Eusuff和Lansey在2003年首次提出的,得到了国内外学者的广泛关注,对算法的研究应用已经渗透到多个领域,成为交叉学科中一个前沿性研究问题。混合蛙跳算法是一种新的基于全局协同搜索的智能优化方法,它结合了Memetic算法和粒子群优化算法两者的优点,是继粒子群优化算法之后的又一种新的群体智能优化算法。作为一种全新的仿生优化算法,具有概念简单、参数少、计算速度快、全局寻优能力强、易于实现等特点。但是SFLA还有很多不完善的地方,如:理论基础薄弱,研究成果及其应用较少,并且存在易收敛到局部最优、在求解部分函数优化问题时效果不够理想等。
本文针对算法存在的缺陷提出一种基于邻域正交交叉算子的混合蛙跳算法(SFLA-OCO),并对该算法中主要参数对算法性能的影响、算法在电力系统负荷预测问题和TSP求解中的应用进行了分析,主要研究内容如下:
1.简要介绍了最优化问题、智能优化算法以及混合蛙跳算法研究进展的相关内容,详细论述了混合蛙跳算法的基本原理、数学模型、算法流程、核心步骤以及算法特点。
2.针对基本混合蛙跳算法在根据局部更新策略更新个体最差值Xw时带有一定的盲目性,寻优结果精度低收敛速度慢等缺点,对其进行了改进,提出了一种基于邻域正交交叉算子的混合蛙跳算法,改善新个体的部分分量值,从而提高种群的多样性,较好的平衡了算法的全局搜索能力和局部搜索能力,提高了算法的收敛速度和计算精度。
3.通过仿真实验,分析了基于邻域正交交叉算子的混合蛙跳算法的主要参数对算法性能的影响,为进一步研究混合蛙跳算法提供了很好的参考依据。
4.将基于邻域正交交叉算子的混合蛙跳算法用于支持向量机的参数优化,建立一种SFLA-OCO-SVM预测模型,并应用于电力系统短期负荷预测,取得了较好的效果。
5.将基于邻域正交交叉算子的混合蛙跳算法用于求解TSP中,并取得了较好的效果,说明本文的改进算法能有效地解决大多数组合优化问题,具有良好的应用前景和实用价值。
最后,对全文进行了总结并对混合蛙跳算法的研究进行了展望,由于该算法的理论分析还不够完善,许多问题有待于做进一步的研究。
Shuffled Frog Leaping Algorithm (SFLA) was first proposed by Eusuff and Lansey in 2003, which have received wide attention from foreign scholars. At present, this algorithm research has already improved many other application and has already become extremely active from research question in the interdisciplinary studies. Shuffled frog leaping algorithm is a new intelligent optimization method based on global cooperative search, which combines the advantages of Memetic algorithm and Particle Swarm Optimization algorithm and it is a new swarm intelligence algorithm after the presence of PSO .As a new optimization algorithm, SFLA has been proved to have many advantages, such as simple concept、less parameters、fast convergence speed、better global search capability and easy implementation, and so on. However, its theoretical foundation is weak, less research and application, and there is easy to converge to a local optimum, some functions in solving optimization problems result is not satisfactory and other defects.
Since SFLA has so much shortcomings, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO),and analyzed the influence of the main parameters to the algorithm. The improved algorithm is applied to power system load forecasting problem and solving TSP, the main contents are as follows:
1.A brief introduction of intelligent optimization problems and intelligent optimization algorithm was made. The basic principles, mathematical model and algorithm flow of SFLA were discussed in detail. The research progress of AFSA was summarized. All of the above indicated the importance of researching the SFLA.
2.Aiming at the low optimization precision、slow convergence speed and a certain degree of blindness when update the individual worst values, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO), the value of parts of the new individual was improved. Therefore, the new algorithm not only improved the convergence speed、the optimization accuracy and the diversity of population but also balanced the global search ability and local search ability successfully.
3.Through experiments, parameters selection was researched in details, which provided the useful reference for the further study of the SFLA.
4.The Shuffled Frog Leaping Algorithm based on local orthogonal crossover operato(rSFLA-OCO), which was given from reference, was used to be a solution for parameters optimization of support vector machines. The SFLA-OCO-SVM predict model established by this paper was applied into short-term load forecasting in power systems, which received preferable result.
5.The improved SFLA was applied into solving the TSP, which received preferable result. The results show that the improved algorithm can effectively solve the most combinatorial optimization problems and has not only good prospects but also a wide range of practical value.
In conclusion, this paper makes a summarization of the prospect of the SFLA. However, since the theoretical analysis of the algorithm is not perfect, many questions are needed to be further researched.
本文针对算法存在的缺陷提出一种基于邻域正交交叉算子的混合蛙跳算法(SFLA-OCO),并对该算法中主要参数对算法性能的影响、算法在电力系统负荷预测问题和TSP求解中的应用进行了分析,主要研究内容如下:
1.简要介绍了最优化问题、智能优化算法以及混合蛙跳算法研究进展的相关内容,详细论述了混合蛙跳算法的基本原理、数学模型、算法流程、核心步骤以及算法特点。
2.针对基本混合蛙跳算法在根据局部更新策略更新个体最差值Xw时带有一定的盲目性,寻优结果精度低收敛速度慢等缺点,对其进行了改进,提出了一种基于邻域正交交叉算子的混合蛙跳算法,改善新个体的部分分量值,从而提高种群的多样性,较好的平衡了算法的全局搜索能力和局部搜索能力,提高了算法的收敛速度和计算精度。
3.通过仿真实验,分析了基于邻域正交交叉算子的混合蛙跳算法的主要参数对算法性能的影响,为进一步研究混合蛙跳算法提供了很好的参考依据。
4.将基于邻域正交交叉算子的混合蛙跳算法用于支持向量机的参数优化,建立一种SFLA-OCO-SVM预测模型,并应用于电力系统短期负荷预测,取得了较好的效果。
5.将基于邻域正交交叉算子的混合蛙跳算法用于求解TSP中,并取得了较好的效果,说明本文的改进算法能有效地解决大多数组合优化问题,具有良好的应用前景和实用价值。
最后,对全文进行了总结并对混合蛙跳算法的研究进行了展望,由于该算法的理论分析还不够完善,许多问题有待于做进一步的研究。
Shuffled Frog Leaping Algorithm (SFLA) was first proposed by Eusuff and Lansey in 2003, which have received wide attention from foreign scholars. At present, this algorithm research has already improved many other application and has already become extremely active from research question in the interdisciplinary studies. Shuffled frog leaping algorithm is a new intelligent optimization method based on global cooperative search, which combines the advantages of Memetic algorithm and Particle Swarm Optimization algorithm and it is a new swarm intelligence algorithm after the presence of PSO .As a new optimization algorithm, SFLA has been proved to have many advantages, such as simple concept、less parameters、fast convergence speed、better global search capability and easy implementation, and so on. However, its theoretical foundation is weak, less research and application, and there is easy to converge to a local optimum, some functions in solving optimization problems result is not satisfactory and other defects.
Since SFLA has so much shortcomings, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO),and analyzed the influence of the main parameters to the algorithm. The improved algorithm is applied to power system load forecasting problem and solving TSP, the main contents are as follows:
1.A brief introduction of intelligent optimization problems and intelligent optimization algorithm was made. The basic principles, mathematical model and algorithm flow of SFLA were discussed in detail. The research progress of AFSA was summarized. All of the above indicated the importance of researching the SFLA.
2.Aiming at the low optimization precision、slow convergence speed and a certain degree of blindness when update the individual worst values, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO), the value of parts of the new individual was improved. Therefore, the new algorithm not only improved the convergence speed、the optimization accuracy and the diversity of population but also balanced the global search ability and local search ability successfully.
3.Through experiments, parameters selection was researched in details, which provided the useful reference for the further study of the SFLA.
4.The Shuffled Frog Leaping Algorithm based on local orthogonal crossover operato(rSFLA-OCO), which was given from reference, was used to be a solution for parameters optimization of support vector machines. The SFLA-OCO-SVM predict model established by this paper was applied into short-term load forecasting in power systems, which received preferable result.
5.The improved SFLA was applied into solving the TSP, which received preferable result. The results show that the improved algorithm can effectively solve the most combinatorial optimization problems and has not only good prospects but also a wide range of practical value.
In conclusion, this paper makes a summarization of the prospect of the SFLA. However, since the theoretical analysis of the algorithm is not perfect, many questions are needed to be further researched.
引文
[1]汪定伟等.智能优化方法[M].北京:高等教育出版社,2006.10
[2] Cheng R,Gen M.Compromise approach-based genetic algorithms for bicriterion shorted path problems [R].Ashikage:Ashikage Institute of Technology,1998.
[3]李颖娟,汪定伟.准时化生产计划的半无限规划模型与模拟退火方法[J].控制与决策, 1998,13(5):603-607.
[4]曹立斌,周建兰.一种改进的禁忌搜索法在函数优化问题中的应用[J].微机发展,2003,13:39-42.
[5]李茂军,罗安,童调生.人工免疫算法及其应用研究[J].控制理论与应用,2004,21(2):153-156.
[6]刘耀林,焦利民.人工神经网络的基准地价评估方法研究[J].地球信息科学,2002,4:1-4.
[7]高尚,杨静宇.群体智能算法及其应用[M].北京:中国水利水电出版社,2006.
[8]张纪会,徐心和.一种新的进化算法—蚁群算法[J].系统工程理论与实践,1999,3:84-87.
[9] Tu Xiaoyuan,Terzopoulos D. Artificial Fishes: Physics, Locomotion, Perceptionand Behavior[C]//Proceedings of SIGGRAPH’94. 1994.
[10]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002, 22(11):32-38.
[11]邢文训,谢金星编著.现代优化计算方法[M].北京:清华大学出版社,2005.9
[12] Hayashi D et al.Distributed optimization by using artificial life[C].Tras.IEEJapan,116-C(5):584-590,1996.
[13] Eusuff M M, Lansey K E. Shuffled frog leaping algorithm: a memetic meta- heuristic for combinatorial optimization [M]. Heuristics J, in press, 2000.
[14] EusuffM M, Lansey K E. Optimization of water distribution network design using the shuffled frog leap ing algorithm [ J ]. Water Resources Planning and Management, 2003,129 (3) : 210-225.
[15] Rahimi-Vahed A, Mirzaei A H. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem[J]. Computers and Industrial Engineering, 2007, 53(4):642-666.
[16]苏晋荣.基于群体智能优化算法的TSP问题研究及应用.山西大学,2007.
[17]王玫,朱云龙,何小贤.群体智能研究综述[J].计算机工程,2005,31(22):194~196.
[18]张燕,康琦,汪镭,吴启迪.群体智能[J].冶金自动化.2005,12,1-4.
[19] Elbeltagi E, Hegazy T, Grierson D. A modified shuffled frog-leaping optimization algorithm: Applications to project management[J]. Structure andInfrastructure Engineering, 2007,3(1): 125-130.
[20]李英海,周建中,杨俊杰等.一种基于阈值选择策略的改进混合蛙跳算法[J].计算机工程与应用,2007,43 (35) :19-21.
[21]栾垚琛,盛建伦.基于粒子群算法的混洗蛙跳算法[J].计算机与现代化,2009(11):39-42.
[22]赵守法.蛙跳算法的研究与应用[D].上海,华东师范大学,2008.
[23]赵鹏军.一种新的仿生优化算法及其改进[J]商洛学院学报,2009,23(2):19-22.
[24] Zhang Xuncai, Hu Xuemei, Cui Guangzhao, Wang Yanfeng, Niu Ying. An improved shuffled frog leaping algorithm with cognitive behavior[A]. 7th World Congress on Intelligent Control and Automation, WCICA’08[C]. Chongqing, China:2008, 6197-6202.
[25] Li Yinghai, Zhou Jianzhong, Yang Junjie, Liu Li, Qin Hui, Yang Li. The Chaos-based shuffled frog leaping algorithm and its application[A]. 4th International Conference on Natural Computation, ICNC 2008[C]. Jinan, China:2008, 481-485.
[26]朱光宇.模因内三角概率选择混合蛙跳算法[J].计算机集成制造系统,2009,15(10):1979-1985.
[27] Chung G, Lansey K. Application of the shuffled frog leaping algorithm for the optimization of a general large-scale water supply system [J]. Water Resources Management, 2009,23(4): 797-823.
[28] Liong S Y, Atiquzzamanm. Optimal design of water distribution network using shuffled complex evolution [ J ]. The Institution of Engineers, 2004, 44 (1) : 93-107.
[29]汪丽娜,陈晓宏,李粤安,林凯荣.混合蛙跳算法和投影寻踪模型的洪水分类研究[J].水电能源科学,2009, 27(2):62-64.
[30]赵鹏军,刘三阳.求解复杂函数优化问题的混合蛙跳算法[J].计算机应用研究,2009,26(7): 2435-2437.
[31]罗雪晖,杨烨,李霞.改进混合蛙跳算法求解旅行商问题[J].通信学报,2009,30(7),130-135.
[32]王亚敏,潘全科,张振领.一种基于离散蛙跳算法的旅行商问题求解方法.[J]聊城大学学报,22(1):81-85.
[33]骆剑平,李霞.求解TSP的改进混合蛙跳算法[J].深圳大学学报理工版,27(2):173-178.
[34]轩宗怡,张翠军.基于混合蛙跳算法的背包问题求解[J].科学技术与工程,2009,9(15):4363-4365.
[35]王亚敏,潘全科,冀俊忠.求解考试时间安排问题的离散蛙跳算法[J].计算机工程与应用,2009, 45(36): 40-43.
[36] Bhaduri A, Bhaduri A. Color image segmentation using clonal selection-based shuffled frog leaping algorithm [A]. ARTCom 2009-International Conference on Advances in Recent Technologies in Communication and Computing[C]. Kottayam, Kerala, India:2009, 517-520.
[37] Wang Ziqiang, Xia Sun. Image watermarking scheme based on shuffled frog leaping algorithm [A]. 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, KAM 2008[C]. Wuhan, China:2008, 239-242.
[38]李梁,陈建勋.基于二值图像的线检测法[J].计算机工程与设计,2009,30(10):2477-2479.
[39]彭振,赵知劲,郑仕链.基于混合蛙跳算法的认知无线电频谱分配[J].计算机工程,2010,36(6):210-217.
[40]郑仕链,楼才义,杨小牛.基于改进混合蛙跳算法的认知无线电协作频谱感知[J].物理学报,2010,59(5):3611-3617.
[41] Hatem E, Emad E, Tarek H, et al. Comparison of two evolutionary algorithms for optimization of bridge deck repairs [J].Computer-Aided Civil and Infrastructure Engineering, 2006,21:561-572.
[42] Bhaduri, Antariksha. A clonal selection based shuffled frog leaping algorithm[A]. 2009 IEEE International Advance Computing Conference, IACC 2009[C]. Patiala, India:2009, 125-130.
[43]Sun Xia, Wang Ziqiang, Zhang Dexian. A Web document classification method based on shuffled frog leaping algorithm[A]. 2nd International Conference on Genetic and Evolutionary Computing, WGEC 2008[C]. Hubei, China:2008, 205-208.
[44]Amiri B, Fathian M, Maroosi A. Application of shuffled frog-leaping algorithm on clustering[J]. International Journal of Advanced Manufacturing Technology, 2009,45(1-2): 199-209.
[45] Alireza R V, Ali H M. Solving a Bi-criteria Permutation Flow-shop Problem Using Shuffled Frog-Leaping Algorithm[M]. Soft Comput, Springer-Verlag, 2007.
[46] Rahimi-Vahed A, Dangchi M, Rafiei H, Salimi E. A novel hybrid multi-objedtive shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology, 2009,41(11-12): 1227-1239.
[47]朱光宇,林蔚清.基于改进混合蛙跳算法的贴片机贴装顺序优化[J].中国工程机械学报,2008, 6(4):428-432.
[48] Zhen Ziyang, Wang Daobo, Liu Yuanyuan. Improved shuffled frog leaping algorithm for continuous optimization problem[A]. 2009 IEEE Congress on Evolutionary Computation, CEC 2009[C]. Trondheim, Norway:2009, 2992-2995.
[49]任立群.基于蛙跳算法的无线Mesh网QoS路由算法[J].聊城大学学报,2010,23(1):82-84.
[50] Zhao Zhijin, Yue Keqiang, Zhao Zhidong. Discrete shuffled frog leaping algorithm for multi-user detection in DS-CDMA communication system[A]. 2008 11th IEEE International Conference on Communication Technology, ICCT 2008[C]. Hangzhou, China:2008, 421-424.
[51]谢圣献,潘全科,潘玉霞,贾保先.蛙跳算法与批量无等待流水线调度问题的优化[J].计算机应用研究,2010,27(8):2909-2912.
[52]岳克强,赵知劲,赵治栋.基于神经网络离散混合蛙跳算法的多用户检测[J].计算机工程,2009,35(19):184-186.
[53]陈功贵,李智欢,陈金富,段献忠.含风电场电力系统动态优化潮流的混合蛙跳算法[J].电力系统自动化,2009,33(4),25-30.
[54] Elbe Tegie,HegaZY T,Grier Sond.Comparison among five evolutionary-based optimization algorithms[J].Advanced Engineering Informatics,2005,19(1):43-53.
[55]王辉,钱锋.群体智能优化算法[J].化工自动化及仪表,2007,34(5):7-13.
[56]李鑫.基于混合进化的SFL算法及其应用[D],河北大学,2010.
[57]刘智明,周激流,敖蔷.遗传算法交叉算子的分析[J].四川大学学报,2002,39(5):857-860.
[58]刘清,廖忠,沈祖诒等.多点正交交叉的遗传算法[J].计算机工程,2005,31(24):151~158.
[59]王联国,洪毅,赵付青,余冬梅.基于邻域正交交叉算子的人工鱼群算法[J]农业机械学报,2008,39(8):140-144.
[60]庄媛媛.基于粒子群的电力系统短期负荷预测[J].控制管理,2007,9(3):9-11.
[61]白鹏等.支持向量机理论及工程应用实例[M].西安:电子科技大学出版社,2007,12.
[62]王武,张元敏,蔡子亮.基于遗传优化神经网络的电力系统短期负荷预测[J].继电器,2008,36(9):39-43.
[63]冷北雪.基于支持向量机的电力系统短期负荷预测[D].成都,西南交通大学,2010.
[64]关颖.支持向量机在电力系统短期负荷预测中的应用[D].天津,天津大学,2006.
[65]于承敏,金磊.基于支持向量机的相关反馈算法研究[J].聊城大学学报,2007,20(2):105-108
[66]张国云.支持向量机算法及其应用研究[J].湖南大学,2006年博士论文,11-16
[67]徐纬芳,刘成忠.基于PCA和支持向量机的径流预测应用研究[J].水资源与水工程学报,2010,21(6):72-75.
[68] CristianiniN, Shawe-Taylor J.支持向量机导论[M].李国正等译.北京:电子工业出版社, 2004: 82-106.
[69]孔锐,张冰.一种快速最小二乘支持向量机分类算法[J].计算机工程与应用,2007,43(32):168-171.
[70]李艳英.基于支持向量机参数优化的群智能优化算法研究[D].天津,天津大学2007.
[71]杨旭,纪玉波,田雪.基于遗传算法的SVM参数选取[J].辽宁石油化工大学学报,2004,24(1):54-58.
[72]刘东平,单甘霖,张岐龙,段修生.基于改进遗传算法的支持向量机参数优化[J].微计算机应用,2010,31(5):11-15.
[73] Chapelle O, Vapnik V. Choosing Multiple Parameters for Support Vector Machines[R]. New York: AT& T Research Labs, 200143.
[74]曹成涛,徐建闽.基于PSO-SVM的短期交通流预测方法.计算机工程与应用,2007,43(15),12～14.
[75]李海雁,张宗平.变共轭梯度算法及其在电力系统负荷预测中的应用[J].甘肃科技,2001,23(12):102-106.
[76]朱杰.蚁群算法解决TSP问题的浅析[J].电脑知识与技术,2008,13(4):724-726.
[77]炎士涛.改进遗传算法求解TSP问题[J].河南科技学院学报,2010,38(1):86-89.
[78]喻菡.遗传算法求解的研究[D].西南交通大学,2006年硕士论文.
[79]黄波,蔡之华.0/1背包问题及其解法研究[J].人工智能及识别技术,2007.
[80]徐刚,于泳波.基于改进的粒子群算法求解0/1背包问题[J].齐齐哈尔大学学报,2007,23(1):71-74.
[81]孙红丽.背包问题的算法设计与分析研究[J].电脑知识与技术,2008,13(7):1534-1535.
[82]兰绍江,韩丽霞,王宇平.图着色问题的混合遗传算法[J].计算机工程与应用,2008,44(28):57-59.
[83]王凌,郑大钟.求解同顺序加工调度问题的一种改进遗传算法[J].系统工程理论与实践,2002,74(6):74-79.
[84]林楠,孟飙,范玉青.基于混合遗传算法车间多工艺路线批量调度[J].北京航空航天大学学报,2007,33(12):1471-1476.
[85]管显笋.基于微粒群优化算法的车间调度问题研究[D].燕山大学,2010.
[86]程浩,刘心报,刘林,经怀明.一种用遗传算法求解装箱问题的新编码方法[J].合肥工业大学学报,2006,29(2):144-147.
[87]马绍汉,陈火旺.组合优化问题反问题的研究进展王洪国[J].计算机科学,2004,31(2):17-21.
[88]张春霞,王蕊.基于遗传算法求解TSP问题的算法设计[J].安阳工学院学报,2007,54(4):57-60.
[89]刘煌.基于GA的改进粒子群算法研究及其在TSP问题上的应用[D].武汉理工大学,2010.
[2] Cheng R,Gen M.Compromise approach-based genetic algorithms for bicriterion shorted path problems [R].Ashikage:Ashikage Institute of Technology,1998.
[3]李颖娟,汪定伟.准时化生产计划的半无限规划模型与模拟退火方法[J].控制与决策, 1998,13(5):603-607.
[4]曹立斌,周建兰.一种改进的禁忌搜索法在函数优化问题中的应用[J].微机发展,2003,13:39-42.
[5]李茂军,罗安,童调生.人工免疫算法及其应用研究[J].控制理论与应用,2004,21(2):153-156.
[6]刘耀林,焦利民.人工神经网络的基准地价评估方法研究[J].地球信息科学,2002,4:1-4.
[7]高尚,杨静宇.群体智能算法及其应用[M].北京:中国水利水电出版社,2006.
[8]张纪会,徐心和.一种新的进化算法—蚁群算法[J].系统工程理论与实践,1999,3:84-87.
[9] Tu Xiaoyuan,Terzopoulos D. Artificial Fishes: Physics, Locomotion, Perceptionand Behavior[C]//Proceedings of SIGGRAPH’94. 1994.
[10]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002, 22(11):32-38.
[11]邢文训,谢金星编著.现代优化计算方法[M].北京:清华大学出版社,2005.9
[12] Hayashi D et al.Distributed optimization by using artificial life[C].Tras.IEEJapan,116-C(5):584-590,1996.
[13] Eusuff M M, Lansey K E. Shuffled frog leaping algorithm: a memetic meta- heuristic for combinatorial optimization [M]. Heuristics J, in press, 2000.
[14] EusuffM M, Lansey K E. Optimization of water distribution network design using the shuffled frog leap ing algorithm [ J ]. Water Resources Planning and Management, 2003,129 (3) : 210-225.
[15] Rahimi-Vahed A, Mirzaei A H. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem[J]. Computers and Industrial Engineering, 2007, 53(4):642-666.
[16]苏晋荣.基于群体智能优化算法的TSP问题研究及应用.山西大学,2007.
[17]王玫,朱云龙,何小贤.群体智能研究综述[J].计算机工程,2005,31(22):194~196.
[18]张燕,康琦,汪镭,吴启迪.群体智能[J].冶金自动化.2005,12,1-4.
[19] Elbeltagi E, Hegazy T, Grierson D. A modified shuffled frog-leaping optimization algorithm: Applications to project management[J]. Structure andInfrastructure Engineering, 2007,3(1): 125-130.
[20]李英海,周建中,杨俊杰等.一种基于阈值选择策略的改进混合蛙跳算法[J].计算机工程与应用,2007,43 (35) :19-21.
[21]栾垚琛,盛建伦.基于粒子群算法的混洗蛙跳算法[J].计算机与现代化,2009(11):39-42.
[22]赵守法.蛙跳算法的研究与应用[D].上海,华东师范大学,2008.
[23]赵鹏军.一种新的仿生优化算法及其改进[J]商洛学院学报,2009,23(2):19-22.
[24] Zhang Xuncai, Hu Xuemei, Cui Guangzhao, Wang Yanfeng, Niu Ying. An improved shuffled frog leaping algorithm with cognitive behavior[A]. 7th World Congress on Intelligent Control and Automation, WCICA’08[C]. Chongqing, China:2008, 6197-6202.
[25] Li Yinghai, Zhou Jianzhong, Yang Junjie, Liu Li, Qin Hui, Yang Li. The Chaos-based shuffled frog leaping algorithm and its application[A]. 4th International Conference on Natural Computation, ICNC 2008[C]. Jinan, China:2008, 481-485.
[26]朱光宇.模因内三角概率选择混合蛙跳算法[J].计算机集成制造系统,2009,15(10):1979-1985.
[27] Chung G, Lansey K. Application of the shuffled frog leaping algorithm for the optimization of a general large-scale water supply system [J]. Water Resources Management, 2009,23(4): 797-823.
[28] Liong S Y, Atiquzzamanm. Optimal design of water distribution network using shuffled complex evolution [ J ]. The Institution of Engineers, 2004, 44 (1) : 93-107.
[29]汪丽娜,陈晓宏,李粤安,林凯荣.混合蛙跳算法和投影寻踪模型的洪水分类研究[J].水电能源科学,2009, 27(2):62-64.
[30]赵鹏军,刘三阳.求解复杂函数优化问题的混合蛙跳算法[J].计算机应用研究,2009,26(7): 2435-2437.
[31]罗雪晖,杨烨,李霞.改进混合蛙跳算法求解旅行商问题[J].通信学报,2009,30(7),130-135.
[32]王亚敏,潘全科,张振领.一种基于离散蛙跳算法的旅行商问题求解方法.[J]聊城大学学报,22(1):81-85.
[33]骆剑平,李霞.求解TSP的改进混合蛙跳算法[J].深圳大学学报理工版,27(2):173-178.
[34]轩宗怡,张翠军.基于混合蛙跳算法的背包问题求解[J].科学技术与工程,2009,9(15):4363-4365.
[35]王亚敏,潘全科,冀俊忠.求解考试时间安排问题的离散蛙跳算法[J].计算机工程与应用,2009, 45(36): 40-43.
[36] Bhaduri A, Bhaduri A. Color image segmentation using clonal selection-based shuffled frog leaping algorithm [A]. ARTCom 2009-International Conference on Advances in Recent Technologies in Communication and Computing[C]. Kottayam, Kerala, India:2009, 517-520.
[37] Wang Ziqiang, Xia Sun. Image watermarking scheme based on shuffled frog leaping algorithm [A]. 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, KAM 2008[C]. Wuhan, China:2008, 239-242.
[38]李梁,陈建勋.基于二值图像的线检测法[J].计算机工程与设计,2009,30(10):2477-2479.
[39]彭振,赵知劲,郑仕链.基于混合蛙跳算法的认知无线电频谱分配[J].计算机工程,2010,36(6):210-217.
[40]郑仕链,楼才义,杨小牛.基于改进混合蛙跳算法的认知无线电协作频谱感知[J].物理学报,2010,59(5):3611-3617.
[41] Hatem E, Emad E, Tarek H, et al. Comparison of two evolutionary algorithms for optimization of bridge deck repairs [J].Computer-Aided Civil and Infrastructure Engineering, 2006,21:561-572.
[42] Bhaduri, Antariksha. A clonal selection based shuffled frog leaping algorithm[A]. 2009 IEEE International Advance Computing Conference, IACC 2009[C]. Patiala, India:2009, 125-130.
[43]Sun Xia, Wang Ziqiang, Zhang Dexian. A Web document classification method based on shuffled frog leaping algorithm[A]. 2nd International Conference on Genetic and Evolutionary Computing, WGEC 2008[C]. Hubei, China:2008, 205-208.
[44]Amiri B, Fathian M, Maroosi A. Application of shuffled frog-leaping algorithm on clustering[J]. International Journal of Advanced Manufacturing Technology, 2009,45(1-2): 199-209.
[45] Alireza R V, Ali H M. Solving a Bi-criteria Permutation Flow-shop Problem Using Shuffled Frog-Leaping Algorithm[M]. Soft Comput, Springer-Verlag, 2007.
[46] Rahimi-Vahed A, Dangchi M, Rafiei H, Salimi E. A novel hybrid multi-objedtive shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology, 2009,41(11-12): 1227-1239.
[47]朱光宇,林蔚清.基于改进混合蛙跳算法的贴片机贴装顺序优化[J].中国工程机械学报,2008, 6(4):428-432.
[48] Zhen Ziyang, Wang Daobo, Liu Yuanyuan. Improved shuffled frog leaping algorithm for continuous optimization problem[A]. 2009 IEEE Congress on Evolutionary Computation, CEC 2009[C]. Trondheim, Norway:2009, 2992-2995.
[49]任立群.基于蛙跳算法的无线Mesh网QoS路由算法[J].聊城大学学报,2010,23(1):82-84.
[50] Zhao Zhijin, Yue Keqiang, Zhao Zhidong. Discrete shuffled frog leaping algorithm for multi-user detection in DS-CDMA communication system[A]. 2008 11th IEEE International Conference on Communication Technology, ICCT 2008[C]. Hangzhou, China:2008, 421-424.
[51]谢圣献,潘全科,潘玉霞,贾保先.蛙跳算法与批量无等待流水线调度问题的优化[J].计算机应用研究,2010,27(8):2909-2912.
[52]岳克强,赵知劲,赵治栋.基于神经网络离散混合蛙跳算法的多用户检测[J].计算机工程,2009,35(19):184-186.
[53]陈功贵,李智欢,陈金富,段献忠.含风电场电力系统动态优化潮流的混合蛙跳算法[J].电力系统自动化,2009,33(4),25-30.
[54] Elbe Tegie,HegaZY T,Grier Sond.Comparison among five evolutionary-based optimization algorithms[J].Advanced Engineering Informatics,2005,19(1):43-53.
[55]王辉,钱锋.群体智能优化算法[J].化工自动化及仪表,2007,34(5):7-13.
[56]李鑫.基于混合进化的SFL算法及其应用[D],河北大学,2010.
[57]刘智明,周激流,敖蔷.遗传算法交叉算子的分析[J].四川大学学报,2002,39(5):857-860.
[58]刘清,廖忠,沈祖诒等.多点正交交叉的遗传算法[J].计算机工程,2005,31(24):151~158.
[59]王联国,洪毅,赵付青,余冬梅.基于邻域正交交叉算子的人工鱼群算法[J]农业机械学报,2008,39(8):140-144.
[60]庄媛媛.基于粒子群的电力系统短期负荷预测[J].控制管理,2007,9(3):9-11.
[61]白鹏等.支持向量机理论及工程应用实例[M].西安:电子科技大学出版社,2007,12.
[62]王武,张元敏,蔡子亮.基于遗传优化神经网络的电力系统短期负荷预测[J].继电器,2008,36(9):39-43.
[63]冷北雪.基于支持向量机的电力系统短期负荷预测[D].成都,西南交通大学,2010.
[64]关颖.支持向量机在电力系统短期负荷预测中的应用[D].天津,天津大学,2006.
[65]于承敏,金磊.基于支持向量机的相关反馈算法研究[J].聊城大学学报,2007,20(2):105-108
[66]张国云.支持向量机算法及其应用研究[J].湖南大学,2006年博士论文,11-16
[67]徐纬芳,刘成忠.基于PCA和支持向量机的径流预测应用研究[J].水资源与水工程学报,2010,21(6):72-75.
[68] CristianiniN, Shawe-Taylor J.支持向量机导论[M].李国正等译.北京:电子工业出版社, 2004: 82-106.
[69]孔锐,张冰.一种快速最小二乘支持向量机分类算法[J].计算机工程与应用,2007,43(32):168-171.
[70]李艳英.基于支持向量机参数优化的群智能优化算法研究[D].天津,天津大学2007.
[71]杨旭,纪玉波,田雪.基于遗传算法的SVM参数选取[J].辽宁石油化工大学学报,2004,24(1):54-58.
[72]刘东平,单甘霖,张岐龙,段修生.基于改进遗传算法的支持向量机参数优化[J].微计算机应用,2010,31(5):11-15.
[73] Chapelle O, Vapnik V. Choosing Multiple Parameters for Support Vector Machines[R]. New York: AT& T Research Labs, 200143.
[74]曹成涛,徐建闽.基于PSO-SVM的短期交通流预测方法.计算机工程与应用,2007,43(15),12～14.
[75]李海雁,张宗平.变共轭梯度算法及其在电力系统负荷预测中的应用[J].甘肃科技,2001,23(12):102-106.
[76]朱杰.蚁群算法解决TSP问题的浅析[J].电脑知识与技术,2008,13(4):724-726.
[77]炎士涛.改进遗传算法求解TSP问题[J].河南科技学院学报,2010,38(1):86-89.
[78]喻菡.遗传算法求解的研究[D].西南交通大学,2006年硕士论文.
[79]黄波,蔡之华.0/1背包问题及其解法研究[J].人工智能及识别技术,2007.
[80]徐刚,于泳波.基于改进的粒子群算法求解0/1背包问题[J].齐齐哈尔大学学报,2007,23(1):71-74.
[81]孙红丽.背包问题的算法设计与分析研究[J].电脑知识与技术,2008,13(7):1534-1535.
[82]兰绍江,韩丽霞,王宇平.图着色问题的混合遗传算法[J].计算机工程与应用,2008,44(28):57-59.
[83]王凌,郑大钟.求解同顺序加工调度问题的一种改进遗传算法[J].系统工程理论与实践,2002,74(6):74-79.
[84]林楠,孟飙,范玉青.基于混合遗传算法车间多工艺路线批量调度[J].北京航空航天大学学报,2007,33(12):1471-1476.
[85]管显笋.基于微粒群优化算法的车间调度问题研究[D].燕山大学,2010.
[86]程浩,刘心报,刘林,经怀明.一种用遗传算法求解装箱问题的新编码方法[J].合肥工业大学学报,2006,29(2):144-147.
[87]马绍汉,陈火旺.组合优化问题反问题的研究进展王洪国[J].计算机科学,2004,31(2):17-21.
[88]张春霞,王蕊.基于遗传算法求解TSP问题的算法设计[J].安阳工学院学报,2007,54(4):57-60.
[89]刘煌.基于GA的改进粒子群算法研究及其在TSP问题上的应用[D].武汉理工大学,2010.