[1]谭营,郑少秋.烟花算法研究进展[J].智能系统学报,2014,9(5):515-528.[doi:10.3969/j.issn.1673-4785.201409010]
TAN Ying,ZHENG Shaoqiu.Recent advances in fireworks algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(5):515-528.[doi:10.3969/j.issn.1673-4785.201409010]
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TAN Ying,ZHENG Shaoqiu.Recent advances in fireworks algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(5):515-528.[doi:10.3969/j.issn.1673-4785.201409010]
烟花算法研究进展
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第5期
页码:
515-528
栏目:
综述
出版日期:
2014-10-25
- Title:
- Recent advances in fireworks algorithm
- Keywords:
- swarm intelligence; fireworks algorithm; explosion amplitude; adaptive explosion amplitude; dynamic search strategy; multi-objective fireworks algorithm; parallel implementation
- 分类号:
- TP301
- 摘要:
- 烟花算法由于具有很强的优化问题求解的能力,近年来逐渐受到研究者的广泛关注。对现有烟花算法的研究工作进行了全面总结,主要包括烟花算法提出的背景、烟花算法的基本原理、单目标烟花算法的改进、混合算法、多目标烟花算法、基于GPU的并行烟花算法以及烟花算法在实际问题中的应用研究等。对于单目标烟花算法及改进算法、混合算法,文中给出了各种改进烟花算法的机制分析和对比研究,最后,给出了烟花算法的未来研究方向,包括爆炸算子搜索机制的深入分析、烟花交互机制研究、多目标烟花算法研究、并行烟花算法研究、扩展烟花算法求解的问题类型以及应用拓展。
- Abstract:
- Fireworks algorithm (FWA) has shown great successes in dealing with complex optimization problems and has attracted a great amount of attention recently. In this paper, FWA was completely analyzed, Including the FWA background evaluation, the study of fundamental principles of FWA, developments in single objective FWA optimization, hybrid algorithms, multi-objective fireworks algorithm, graphic processing unit(GPU) based parallel fireworks algorithm, and their applications in practice. For single objective FWA and improved and hybrid algorithms, the mechanism analysis and comparative research of various improved FWAs are given in this paper. Finally, the future research directions for FWA are pointed out, which include the analysis of explosion operator, study of interaction strategies among the fireworks, research on multi-objective fireworks algorithm and parallel fireworks algorithm, types of solutions to the extended FWA, and application expansion.
- 参考文献/References:
-
[1] BENI G, WANG J. Swarm intelligence in cellular robotic systems[C]//Proceedings of NATO Advanced Workshop on Robots and Biological Systems. 1989:75-80.
[2] BENI G, WANG J. Swarm intelligence in cellular robotic systems[C]//Berlin:Springer, 1993:703-712.
[3] BONABEAU E, DORIGO M, THERAULAZ G. Swarm intelligence:from natural to artificial systems[M]. New York:Oxford University Press, 1999:1-15.
[4] KENNEDY J, EBERHART R C. Swarm intelligence[M]. Morgan Kaufmann, 2001:504-512.
[5] LIU Y, PASSINO K M. Swarm intelligence:literature overview[J]. Journal of Ohio State University, 2000:1-9.
[6] KRAUSE J, RUXTON G D, KRAUSE S. Swarm intelligence in animals and humans[J]. Trends in Ecology & Evolution, 2010, 25(1):28-34.
[7] REYNOLDS C W. Flocks, herds and schools:a distributed behavioral model[J]. ACM SIGGRAPH Computer Graphics, 1987, 21(4):25-34.
[8] COLORNI A, DORIGO M, MANIEZZO V. Distributed optimization by ant colonies[C]//Proceedings of the First European Conference on Artificial Life. Paris, 1991:134-142.
[9] KENNEDY J, RUSSELL E.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks. Perth, WA, 1995:1942-1948.
[10] ENGELBRECHT A P. Fundamentals of computational swarm intelligence[M]. John Wiley & Sons, 2006:15-28.
[11] DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization[J]. IEEE Computational Intelligence Magazine, 2006, 1(4):28-39.
[12] LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3):281-295.
[13] ZHAN Z H, ZHANG J, LI Y, et al. Adaptive particle swarm optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2009, 39(6):1362-1381.
[14] XING B, GAO W J. Innovative computational intelligence:a rough guide to 134 clever algorithms[M]. Springer, 2014:250-260.
[15] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[16] LAUMANNS M. SPEA2:Improving the strength pareto evolutionary algorithm[J]. TIK-Report, 2001(103):1-21.
[17] LI X. A non-dominated sorting particle swarm optimizer for multiobjective optimization[C]//Genetic and Evolutionary Computation-GECCO 2003. Berlin:Springer, 2003:37-48.
[18] ZHOU Y, TAN Y. GPU-based parallel particle swarm optimization[C]//IEEE Congress on Evolutionary Computation. 2009:1493-1500.
[19] DING K, ZHENG S, TAN Y. A GPU-based parallel fireworks algorithm for optimization[C]//Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference. ACM, 2013:9-16.
[20] LI J, WANG X, HE R, et al. An efficient fine-grained parallel genetic algorithm based on GPU-accelerated[C]//International Conference on IFIP, Network and Parallel Computing Workshops. Liaoning, China, 2007:855-862.
[21] TAN Y, YU C, ZHENG S, et al. Introduction to fireworks algorithm[J]. International Journal of Swarm Intelligence Research, 2013, 4(4):39-70.
[22] TAN Y, ZHU Y. Fireworks algorithm for optimization[C]//Berlin:Springer, 2010:355-364.
[23] HASTIE T, TIBSHIRANI R, FRIEDMAN J, et al. The elements of statistical learning:data mining, inference and prediction[J]. The Mathematical Intelligencer, 2005, 27(2):83-85.
[24] LIU J, ZHENG S, TAN Y. Analysis on global convergence and time complexity of fireworks algorithm[C]//2014 IEEE Congress on Evolutionary Computation (CEC).Beijing, China, 2014:3207-3213.
[25] EBEHART R C, SHI Y. Comparison between genetic algorithms and particle swarm optimization[C]//Berlin:Springer, 1998:611-616.
[26] PEI Y, ZHENG S, TAN Y, et al. An empirical study on influence of approximation approaches on enhancing fireworks algorithm[C]//IEEE International Conference on Systems, Man, and Cybernetics. Seoul, South Korea, 2012:1322-1327.
[27] LIU J, ZHENG S, TAN Y. The improvement on controlling exploration and exploitation of firework algorithm[M]//Advances in Swarm Intelligence. Berlin:Springer, 2013:11-23.
[28] ZHENG S, JANECEK A, TAN Y. Enhanced fireworks algorithm[C]//2013 IEEE Congress on Evolutionary Computation. Cancun, Mexico, 2013:2069-2077.
[29] ZHENG S, JANECEK A, LI J, et al. Dynamic search in fireworks algorithm[C]//2014 IEEE Congress on Evolutionary Computation. Beijing, China, 2014:3222-3229.
[30] LI J, ZHENG S, TAN Y. Adaptive Fireworks Algorithm[C]//2014 IEEE Congress on Evolutionary Computation. Beijing, China, 2014:3214-3221.
[31] ZHENG Y J, XU X L, LING H F, et al. A hybrid fireworks optimization method with differential evolution operators[J]. Neurocomputing, 2012(148):75-80.
[32] YU C, KELLEY L, ZHENG S, et al. Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems[C]//2014 IEEE Congress on Evolutionary Computation.[S.l.]:[s.n.], 2014:3238-3245.
[33] GAO H, DIAO M. Cultural firework algorithm and its application for digital filters design[J]. International Journal of Modelling, Identification and Control, 2011, 14(4):324-331.
[34] FANG W, SUN J, XU W, et al. FIR digital filters design based on quantum-behaved particle swarm optimization[C]//International Conference on Innovative Computing, Information and Control. 2006:615-619.
[35] FANG W, SUN J, XU W. FIR filter design based on adaptive quantum-behaved particle swarm optimization algorithm[J]. Systems Engineering and Electronics, 2008, 30(7):1378-81.
[36] IUGU T, TAKAGI H. Accelerating a GA convergence by fitting a single-peak function[C]//1999 IEEE International Conference on Fuzzy System. Seoul, South Korea, 1999:1415-1420.
[37] ZHANG B, ZHANG M X, ZHENG Y J. A hybrid biogeography-based optimization and fireworks algorithm[C]//2014 IEEE Congress on Evolutionary Computation.[S.l.]:[s.n.], 2014:3200-3206.
[38] SUGANTHAN P N, HANSEN N, LIANG J J, et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R]. 2005, 2005005.
[39] LIANG J J, QU B Y, SUGANTHAN P N, et al. Problem definitions and evaluation criteria for the cec 2013 special session on real-parameter optimization[R]. Computational Intelligence Laboratory, 2013, 201-212.
[40] LIANG J J, QU B Y, SUGANTHAN P N. Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization[J]. Nanyang Technological University, 2013:1-50.
[41] WILCOXON F. Individual comparisons by ranking methods[J]. Biometrics Bulletin, 1945:80-83.
[42] TAN Y, XIAO Z M. Clonal particle swarm optimization and its applications[C]//IEEE Congress on Evolutionary Computation.[S.l.]:[s.n.], 2007:2303-2309.
[43] BRATTON D, KENNEDY J. Defining a standard for particle swarm optimization[C]//IEEE Swarm Intelligence Symposium. Honolulu, HI, 2007:120-127.
[44] ZHENG Y J, SONG Q, CHEN S Y. Multiobjective fireworks optimization for variable-rate fertilization in oil crop production[J]. Applied Soft Computing, 2013, 13(11):4253-4263.
[45] HAJECK J, SZOLLOS A, ?ISTEK J. A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization[J]. Advances in Engineering Software, 2010, 41(7):1031-1057.
[46] GOLDBERG D E.Genetic algorithms in search, optimization, and machine learning[M]. Reading Menlo Park:Addison-Wesley, 1989:412.
[47] STORN R, PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4):341-359.
[48] 杜振鑫. 烟花算法求解非线性方程组[J]. 现代计算机, 2013(3):18-21.DU Zhenxin. Fireworks algorithm for solving nonlinear equations[J]. Modern computer. 2013(3):18-21.
[49] 张家琴. 求解 0/1 背包问题的烟花算法研究[J]. 武汉工程职业技术学院学报, 2011, 23(3):64-66.ZHANG Jiaqin. Fireworks algorithm for solving 0/1 knapsack problems[J]. Journal of Wuhan Engineering Institute, 2011, 23(3):64-66.
[50] PHOLDEE N, BUREERAT S. Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints[J]. Advances in Engineering Software, 2014, 75:1-13.
[51] JANECEK A, TAN Y. Swarm intelligence for non-negative matrix factorization[J]. International Journal of Swarm Intelligence Research , 2011, 2(4):12-34.
[52] ZHENG S, TAN Y. A unified distance measure scheme for orientation coding in identification[C]//2013 International Conference on Information Science and Technology. Yangzhou, China, 2013:979-985.
[53] HE W, MI G, TAN Y. Parameter optimization of local-concentration model for spam detection by using fireworks algorithm[C]//Advances in Swarm Intelligence. Berlin:Springer, 2013:439-450.
[54] MOHAMED I A, KOWSALYA M. A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm[J]. International Journal of Electrical Power & Energy Systems, 2014, 62:312-322.
[55] RAJARAM R, PALANISAMY K, RAMASAMY S, et al. Selective harmonic elimination in PWM inverter using fire fly and fire works algorithm[J]. International Journal of Innovative Research in Advanced Engineering, 2014, (1):8:55-62.
[56] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791.
[57] BASTOS C J, DE LIMA N F, LINS A J, et al. Fish school search[M]//Nature-inspired Algorithms for Optimization. Berlin:Springer, 2009:261-277.
[58] JANECEK A, TAN Y. Using population based algorithms for initializing nonnegative matrix factorization[M]. Berlin:Springer, 2011:307-316.
[59] JANECEK A, TAN Y. Iterative improvement of the multiplicative update nmf algorithm using nature-inspired optimization[C]//2011 Seventh International Conference on Natural Computation.Shanghai, China, 2011:1668-1672.
[60] KONG A W K, ZHANG D. Competitive coding scheme for palmprint verification[C]//Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004:520-523.
[61] JIA W, HUANG D S, ZHANG D. Palmprint verification based on robust line orientation code[J]. Pattern Recognition, 2008, 41(5):1504-1513.
[62] WU X, WANG K, ZHANG D. Palmprint authentication based on orientation code matching[C]//Audio-and Video-Based Biometric Person Authentication. Berlin:Springer, 2005:555-562.
[63] GUO Z, ZUO W, ZHANG L, et al. A unified distance measurement for orientation coding in palmprint verification[J]. Neurocomputing, 2010, 73(4):944-950.
[64] CHEN S, LUK B L. Digital IIR filter design using particle swarm optimisation[J]. International Journal of Modelling, Identification and Control, 2010, 9(4):327-335.
[65] ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms:empirical results[J]. Evolutionary Computation, 2000, 8(2):173-195.
[66] ABBASS H A, SARKER R, NEWTON C. PDE:a Pareto-frontier differential evolution approach for multi-objective optimization problems[C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, South Korea, 2011:971-978.
[67] GONG W, CAI Z. A multiobjective differential evolution algorithm for constrained optimization[C]//IEEE Congress on Evolutionary Computation. Hong Kong, China, 2008:181-188.
[68] QIAN W, YANG J, YANG Y, et al. A new random group search algorithm for solving the multi-objective programming problems[J]. Journal Liaoning Normal University, 2007, 30(2):141.
[69] NARA K, SHIOSE A, KITAGAWA M, et al. Implementation of genetic algorithm for distribution systems loss minimum re-configuration[J]. IEEE Transactions on Power Systems, 1992, 7(3):1044-1051.
[70] ZHU J Z. Optimal reconfiguration of electrical distribution network using the refined genetic algorithm[J]. Electric Power Systems Research, 2002, 62(1):37-42.
[71] ZHANG D, FU Z, ZHANG L. An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems[J]. Electric Power Systems Research, 2007, 77(5):685-694.
[72] SRINIVASA R R, NARASIMHAM S V L, RAMALINGA R M, et al. Optimal network reconfiguration of large-scale distribution system using harmony search algorithm[J]. IEEE Transactions on Power Systems, 2011, 26(3):1080-1088.
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备注/Memo
收稿日期:2014-09-05。
基金项目:国家自然科学基金资助项目(61375119、61170057、60875080).
作者简介:郑少秋, 男, 1990年生, 博士研究生, 主要研究方向为群体智能、计算智能、模式识别。
通讯作者:谭营, 男, 1964年生, 教授, 博士生导师, 博士, 主要研究方向为计算智能、群体智能、机器学习方法、人工免疫系统、群体智能、智能信息处理及信息安全应用。担任IJCIPR主编, IEEETransonCybernetics副主编, IJSIR副主编等, IEEESeniorMember, IEEECIS-ETTC委员, ICSI系列会议大会主席等。主持国家"863"计划、国家自然科学基金、国际合作交流等科研项目30余项。获得2009年度国家自然科学二等奖。获国家发明专利授权3项, 发表学术论文260余篇, 出版专著5部。E-mail:ytan@pku.edu.cn.
更新日期/Last Update:
1900-01-01