| 3,125 | 77 | 426 |
| 下载次数 | 被引频次 | 阅读次数 |
集群智能领域的研究正呈爆炸趋势增长,每年都有无数新的集群智能算法以及改进算法被提出,这些算法在各自的领域内都扮演着相当重要的角色。从集群智能算法的特点与待解决问题出发,首先介绍集群智能算法的概念及部分经典算法,重点介绍粒子群算法与蚁群优化算法的主要思想;然后根据不同集群智能算法在不同应用问题的差异表现,对当下的几个热点问题如Ad Hoc网络、大数据与机器学习、智能电网与智慧交通等领域的集群智能算法作了简单介绍;其次是关于集群智能算法领域理论研究的讨论,主要针对集群智能算法智能行为的产生机制、不同集群智能算法在面对同一问题的性能表现不同的原因、场景选定后集群智能算法性能最优的设计方法等问题展开,并给出了这些研究具有代表性的工作及未来的研究方向;最后对集群智能算法研究尤其是基础理论研究的发展方向进行了展望。
Abstract:The research of swarm intelligence is growing explosively. Countless new swarm intelligence algorithms and their improved algorithms are proposed every year. These algorithms play a very important role in their respective fields. Starting from the characteristics of swarm intelligence algorithm and the problems to be solved, this article first introduces the concept of swarm intelligence and some classic algorithms, and focuses on the main ideas of particle swarm optimization and ant colony optimization algorithms. Then, according to the performance differences of different swarm intelligence algorithms in different application problems, a brief introduction of swarm intelligence algorithms in several current hot issues such as Ad Hoc networks, big data and machine learning, smart grids and smart transportation is given. Followed by the discussion on the theoretical research of swarm intelligence algorithms mainly focuses on the generation mechanism of intelligence behavior in swarm intelligence algorithms, the reason why different swarm intelligence algorithms perform differently in the same problem, and the method to optimize the performance of the swarm intelligent algorithm in certain problem. The representative work and future research directions of these studies are given. Finally, the development direction of swarm intelligence algorithm research, especially basic theory research, is prospected.
[1]Dario P,Sandini G,Aebischer P.Robots and biological systems:Towards a new bionics?[J]Swarm Intelligence in Cellular Robotic Systems,1993(38):703-712.
[2]Kordon A K.Swarm intelligence:The benefits of swarms[M].Applying Computational Intelligence.Berlin,Germany:Springer Berlin Heidelberg,2010:145-174.
[3]Bonabeau E.Swarm intelligence:From natural to artificial systems[J].Santa Fe Institute Studies on The Ences of Complexity,1999:1-24.
[4]Dorigo M,Maniezzo V,Colorni A.Ant system:Optimization by a colony of cooperating agents[J].IEEE Transactions on Systems,Man and Cybernetics-Part B,1996,26(1):29-41.
[5]Kennedy J.Particle swarm optimization[C].Proceeding of 1995IEEE International Conference.Neural Networks,Perth,Australia,Nov.27-Dec.2011,4(8):1942-1948.
[6]Arpan K K.Bio inspired computing-a review of algorithms and scope of applications[J].Expert Systems with Applications,2016,59:20-32.
[7]Del Ser J,Osaba E,Molina D,et al.Bio-inspired computation:Where we stand and what's next[J].Swarm and Evolutionary Computation,2019(48):220-250.
[8]武坤琳,葛悦涛.俄罗斯《2030年前国家人工智能发展战略》浅析[J].无人系统技术,2020,3(2):63-66.
[9]王雅琳,杨依然,王彤,等.2019年无人系统领域发展综述[J].无人系统技术,2019,2(6):53-57.
[10]张肇聿,王一琳,李志.基于人工智能技术的25个行业发展趋势[J].无人系统技术,2019,2(1):17-22.
[11]张思齐,沈钧戈,郭行,等.智能无人系统改变未来[J].无人系统技术,2018,1(3):1-7.
[12]Clerc M,Kennedy J.The particle swarm-explosion,stability,and convergence in a multidimensional complex space[J].IEEETransactions on Evolutionary Computation,2002,6(1):58-73.
[13]Bergh F V D,Engelbrecht A P.Training product unit networks using cooperative particle swarm optimisers[C].International Joint Conference on Neural Networks.Proceedings (Cat.No.01CH37222),Washington,DC,USA,2001.
[14]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机应用研究,2004(1):47-50.
[15]刘元,阳春华,李勇刚,等.粒子群算法在锌电解优化调度中的应用[J].自动化与仪表,2006(4):11-14.
[16]Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony (ABC)algorithm[J].Kluwer Academic Publishers,2007,39(3):459-471.
[17]Bansal J C,Sharma H,Jadon S S,et al.Spider monkey optimization algorithm for numerical optimization[J].Memetic Computing,2014,6(1):31-47.
[18]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(11):32-38.
[19]Liu Y,Passino K M.Biomimicry of social foraging bacteria for distributed optimization:Models,principles,and emergent behaviors.[J].Journal of Optimization Theory&Applications,2002,115(3):603-628.
[20]Eusuff M M,Lansey K E.Optimization of water distribution network design using the shuffled frog leaping algorithm[J].Journal of Water Resources Planning and Management,2003,129(3):210-225.
[21]Krishnanand K N,Ghose D.Detection of multiple source locations using a glowworm metaphor with applications to collective robotics[M].Proceedings-2005 IEEE Swarm Intelligence Symposium.Pasadena,CA,USA:IEEE Computer Society,2005(2005):84-91.
[22]Karaboga D.An idea based on honey bee swarm for numerical optimization[J].Technical Report TR06,Erciyes University.2005(6):10.
[23]Pham D T,Ghanbarzadeh A,Ko E,et al.The bees algorithm-a novel tool for complex optimisation problems[M].The Bees Algorithm-A Novel Tool for Complex Optimisation Problems.Amsterdam,Netherlands:Elsevier Science Ltd,2006:454-459.
[24]Pan J,Tsai P,Chu S.Cat swarm optimization[C].Pacific Rim International Conference on Artificial Intelligence,Springer,Berlin,Heidelberg,2006.
[25]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[26]Martin R,Stephen W,Ajith A,et al.Termite:A swarm intelligent routing algorithm for mobile wireless ad-hoc networks[M].New York,USA:Cornell University.2005:155-184.
[27]Yang X S,Lees J M,Morley C T.Application of virtual ant algorithms in the optimization of CFRP shear strengthened precracked structures[C].Computational Science-ICCS 2006,6th International Conference,Reading,UK,May 28-31,2006,Proceedings,Part I.
[28]Simon D.Biogeography-based optimization[J].IEEE Transactions on Evolutionary Computation,2008,12(6):702-713.
[29]Filho C J A B,Neto F B D L,Lins A J C C,et al.A novel search algorithm based on fish school behavior[C].2008 IEEE International Conference on Systems,Man and Cybernetics,Singapore,2008.
[30]Yang X S,Deb S.Cuckoo Search via Lévy flights[C].2009World Congress on Nature&Biologically Inspired Computing(NaBIC),Coimbatore,India,2009.
[31]He S,Wu Q H,Saunders J R.Group search optimizer:An optimization algorithm inspired by animal searching behavior[J].IEEE Transactions on Evolutionary Computation,2009,13(5):973-990.
[32]黎成.新型元启发式蝙蝠算法[J].电脑知识与技术,2010,6(23):6569-6572.
[33]Yang,She X.Firefly algorithm,stochastic test functions and design optimisation[J].International Journal of Bio Inspired Computation,2010,2(2):78-84.
[34]Hedayatzadeh R,Salmasi F A,Keshtgari M,et al.Termite colony optimization:A novel approach for optimizing continuous problems[C].2010 18th Iranian Conference on Electrical Engineering,Isfahan,Iran,2010.
[35]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.
[36]潘文超.应用果蝇优化算法优化广义回归神经网络进行企业经营绩效评估[J].太原理工大学学报(社会科学版),2011,29(4):1-5.
[37]Yang X S.Flower pollination algorithm for global optimization[J].Unconventional Computation and Natural Computation,2012(7445):240-249.
[38]Gandomi A H,Alavi A H.Krill herd:A new bio-inspired optimization algorithm[J].Communications in Nonlinear Science&Numerical Simulation,2012,17(12):4831-4845.
[39]Mozaffari A,Fathi A,Behzadipour S.The great salmon run:Anovel bio-inspired algorithm for artificial system design and optimisation[J].International Journal of Bio-Inspired Computation,2012,4(5):286-301.
[40]Rui T,Simon F,Xin-She Y,et al.Wolf search algorithm with ephemeral memory[C].Seventh International Conference on Digital Information Management (ICDIM 2012),Macau,Macao,2012.
[41]Kaveh A,Farhoudi N.A new optimization method:Dolphin echolocation[J].Advances in Engineering Software,2013(59):53-70.
[42]Neshat M,Sepidnam G,Sargolzaei M.Swallow swarm optimization algorithm:A new method to optimization[J].Neural Computing&Applications,2013,23(2):429-454.
[43]Li X,Zhang J,Yin M.Animal migration optimization:An optimization algorithm inspired by animal migration behavior[J].Neural Computing&Applications,2014(24):7-8.
[44]Meng X,Liu Y,Gao X,et al.A new bio-inspired algorithm:Chicken swarm optimization[J].Lecture Notes in Computer Science,2014(8794):86-94.
[45]Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014:46-61.
[46]Seyedali M.The ant lion optimizer[J].Advances in Engineering Software,2015(83):80-98.
[47]Uymaz S A,Tezel G,Yel E.Artificial algae algorithm (AAA) for nonlinear global optimization[J].Applied Soft Computing,2015(31):153-171.
[48]Mirjalili S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.
[49]Li M D,Zhao H,Weng X W,et al.A novel nature-inspired algorithm for optimization:Virus colony search[J].Advances in Engineering Software,2016(92):65-88.
[50]Alireza A.A novel metaheuristic method for solving constrained engineering optimization problems:Crow search algorithm[J].Computers and Structures,2016(169):1-12.
[51]Yong W,Tao W,Cheng Z Z,et al.A new stochastic optimization approach:Dolphin swarm optimization algorithm[J].International Journal of Computational Intelligence and Applications,2016(15):1650011.
[52]Abedinia O,Amjady N,Ghasemi A.A new metaheuristic algorithm based on shark smell optimization[J].Complexity,2016(21):97-116.
[53]Mirjalili S,Lewis A.The whale optimization algorithm[J].Advances in Engineering Software,2016(95):51-67.
[54]Saremi,Shahrzad,Mirjalili,et al.Grasshopper optimisation algorithm:Theory and application[J].Advances in Engineering Software,2017(105):30-47.
[55]Mirjalili S,Gandomi A H,Mirjalili S Z,et al.Salp swarm algorithm:A bio-inspired optimizer for engineering design problems[J].Advances in Engineering Software,2017(114):163-191.
[56]Dhiman G,Kumar V.Spotted hyena optimizer:A novel bio-inspired based metaheuristic technique for engineering applications[J].Advances in Engineering Software,2017(114):48-70.
[57]Jain M,Singh V,Rani A.A novel nature-inspired algorithm for optimization:Squirrel search algorithm[J].Swarm and Evolutionary Computation,2018(44):148-175.
[58]Di Caro G.Antnet:Distributed stigmergetic control for communications networks[J].Journal of Artificial Intelligence Research,1998(9):317-365.
[59]Mesut G,Sorges U,Bouazizi I.ARA-the ant-colony based routing algorithm for manets[C].Proceedings of International Conference on Parallel Processing Workshop,Vancouver,BC,Canada,2002.
[60]Baras,J,Harsh M.A probabilistic emergent routing algorithm for mobile ad hoc networks[J].Proceedings of Workshop on Modeling and Optimization in Mobile,Ad Hoc and Wireless Networks,2003(8):1-10.
[61]Gianni D C,Frederick D,Luca M G.Anthocnet:An adaptive nature‐inspired algorithm for routing in mobile ad hoc networks[J].European Transactions on Telecommunications,2005(5):443-455.
[62]Woungang I,Dhurandher S K,Obaidat M S,et al.An ant-swarm inspired energy-efficient ad hoc on-demand routing protocol for mobile ad hoc networks[C].2013 IEEE International Conference on Communications (ICC),Budapest,Hungary,2013.
[63]Vijayalakshmi P,Francis S A J,Dinakaran J A.A robust energy efficient ant colony optimization routing algorithm for multi-hop ad hoc networks in manets[J].Wireless Networks,2016 (6):2081-2100.
[64]Kamali S,Opatrny J.POSANT:A position based ant colony routing algorithm for mobile ad-hoc networks[C].2007 Third International Conference on Wireless and Mobile Communications (ICWMC'07),Guadeloupe,French Caribbean,2007.
[65]Abdi M J,Giveki D.Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J].Engineering Applications of Artificial Intelligence,2013(1):603-608.
[66]Chen Y,Miao D,Wang R.A rough set approach to feature selection based on ant colony optimization-sciencedirect[J].Pattern Recognition Letters,2010,31(3):226-233.
[67]Huang C L.ACO-based hybrid classification system with feature subset selection and model parameters optimization[J].Neurocomputing,2009,73(1-3):438-448.
[68]Kadri O,Mouss L H,Mouss M D.Fault diagnosis of rotary kiln using SVM and binary ACO[J].Journal of Mechanical Science&Technology,2012,26(2):601-608.
[69]Verma P,Sanyal K,Srinivasan D,et al.Computational intelligence techniques in smart grid planning and operation:A survey[J].IEEE Innovative Smart Grid Technologies-Asia (ISGTAsia),2018:891-896.
[70]Gamarra,C,Guerrero J M.Computational optimization techniques applied to microgrids planning:A review[J].Renewable&Sustainable Energy Reviews,2015(48):413-424.
[71]Ramirez-Rosado I J,Dominguez-Navarro J A.Possibilistic model based on fuzzy sets for the multiobjective optimal planning of electric power distribution networks[J].IEEE Transactions on Power Systems,2004,19(4):1801-1810.
[72]Basu A K,Chowdhury S.Strategic deployment of CHP-based distributed energy resources in microgrids[C].2009 IEEE Power&Energy Society General Meeting,Calgary,AB,Canada,2009.
[73]Logenthiran T,Srinivasan D,Phyu E.Particle swarm optimization for demand side management in smart grid[C].2015 IEEEInnovative Smart Grid Technologies-Asia (ISGT ASIA),Bangkok,Thailand,2015.
[74]Zhou Y,Xu G.Demand side energy management with PSO and regulated electric vehicles behaviours[C].2014 IEEE PESAsia-Pacific Power and Energy Engineering Conference(APPEEC),Hong Kong,China,2014.
[75]Nayak S K,Sahoo N,Panda G.Demand side management of residential loads in a smart grid using 2d particle swarm optimization technique[C].2015 IEEE Power,Communication and Information Technology Conference (PCITC),Bhubaneswar,India,2015.
[76]Jabbarpour M R,Zarrabi H,Jung J J,et al.A green ant-based method for path planning of unmanned ground vehicles[J].IEEEAccess,2017:1-1.
[77]Mesbahi T,Rizoug N,Patrick B,et al.Optimal energy management for a Li-ion battery/supercapacitor hybrid energy storage system based on a particle swarm optimization incorporating nelder-mead simplex approach[J].IEEE Transactions on Intelligent Vehicles,2017(2):1-1.
[78]Guo L,Lin X,Ge P,et al.Torque distribution for electric vehicle with four in-wheel motors by considering energy optimization and dynamics performance[C].2017 IEEE Intelligent Vehicles Symposium (IV),Los Angeles,CA,2017.
[79]Wu X C,Qin G H,Sun M H,et al.Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems[J].Frontiers of Information Technology&Electronic Engineering,2017,18(9):1385-1396.
[80]Chan K Y,Dillon T,Chang E,et al.Prediction of short-term traffic variables using intelligent swarm-based neural networks[J].IEEE Transactions on Control Systems Technology,2013,21(1):263-274.
[81]Song L.Improved intelligent method for traffic flow prediction based on artificial neural networks and ant colony optimization[J].Journal of Convergence Information Technology,2012,7(8):272-280.
[82]Ser J D,Osaba E,Sanchez-Medina J J,et al.Bioinspired computational intelligence and transportation systems:A long road ahead[J].IEEE Transactions on Intelligent Transportation Systems,2020,21(2):466-495.
[83]吴琦,于海靖,谢勇,等.人工智能在自动驾驶领域的应用及启示[J].无人系统技术,2019,2(1):23-28.
[84]Zhao D,Dai Y,Zhang Z.Computational intelligence in urban traffic signal control:A survey[J].IEEE Transactions on Systems Man&Cybernetics Part C,2012,42(4):485-494.
[85]Jabbarpour M R,Zarrabi H,Khokhar R H,et al.Applications of computational intelligence in vehicle traffic congestion problem:A survey[J].Soft Computing,2017(22):2299-2320.
[86]张丹,吴陈炜,谢安桓.城市交通问题的空中解决方案--自主载人飞行器研究综述[J].无人系统技术,2018,1(2):1-13.
[87]Mazzara M,Biselli L,Greco P P,et al.Social networks and collective intelligence:A return to the Agora[J].Computer Science,2013,39(1):51-64.
[88]Gao C,Lan X,Zhang X,et al.A bio-inspired methodology of identifying influential nodes in complex networks[J].Plos One,2013(8):e66732.
[89]Karnan M,Logheshwari T.Improved implementation of brain MRI image segmentation using Ant Colony System[C].2010IEEE International Conference on Computational Intelligence and Computing Research,Coimbatore,India,2010.
[90]Logeswari T,Karnan M.An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map[J].International Journal of Computer Theory and Engineering,2010:591-595.
[91]Soleimani V,Vincheh F H.Improving ant colony optimization for brain MRI image segmentation and brain tumor diagnosis[C].2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA),Birjand,Iran,2013.
[92]Firdaus A,Badrul A N,Shahaboddin S,et al.Dyhap:Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware[J].Plos One,2016,11(9):e0162627.
[93]Cui Z,Xue F,Cai X,et al.Detection of malicious code variants based on deep learning[J].IEEE Transactions on Industrial Informatics,2018:1-1.
[94]Nogueira-Collazo M,Porras C C,Fernandez-Leiva A.Competitive algorithms for coevolving both game content and AI.A case study:Planet wars[J].IEEE Transactions on Computational Intelligence&Ai in Games,2016(4):325-337.
[95]Recio G,Martin E,Estebanez C,et al.Antbot:Ant colonies for video games[J].IEEE Transactions on Computational Intelligence&Ai in Games,2012(4):295-308.
[96]Kennedy J,Eberhart R C,Shi Y H.Swarm intelligence[M].San francisco:Morgan Kaufmann Publishers,2001.
[97]Wolpert D H,Macready W G.No free lunch theorems for optimization[J].IEEE Transactions on Evolutionary Computation,1997(1):67-82.
[98]Yang X S,Deb S,Zhao Y,et al.Swarm intelligence:Past,present and future[J].Soft Computing.2018(22):5923-5933.
[99]Clerc M,Kennedy J.The particle swarm-explosion,stability,and convergence in a multidimensional complex space[J].IEEETransactions on Evolutionary Computation,2002,6(1):58-73.
[100]Yang X S.Metaheuristic optimization:Algorithm analysis and open problems[J].International Symposium on Experimental Algorithms,2012(6630):21-32.
[101]Yang X S.Nature-inspired algorithms and applied optimization[J].Studies in Computational Intelligence,2018:1-25.
[102]Pitzer E,Affenzeller M.A comprehensive survey on fitness landscape analysis[J].Recent Advances in Intelligent Engineering Systems,2012:161-191.
[103]Jana N D,Sil J,Das S.Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model:A perspective from fitness landscape analysis[J].Information Sciences,2017,s391-392:28-64.
[104]Yang X S.Efficiency analysis of swarm intelligence and randomization techniques[J].Journal of Computational and Theoretical Nanocience,2013,9(2):189-198.
[105]Crepinsek M,Liu S H,Mernik M.Exploration and exploitation in evolutionary algorithms:A survey[J].ACM Computing Surveys,2013,45(3):Article 35.
[106]Phan H D,Ellis K,Barca J C,et al.A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms[J].Neural Computing and Applications,2019(8):567-588.
基本信息:
DOI:10.19942/j.issn.2096-5915.2021.3.021
中图分类号:TP18
引用信息:
[1]秦小林,罗刚,李文博,等.集群智能算法综述[J].无人系统技术,2021,4(03):1-10.DOI:10.19942/j.issn.2096-5915.2021.3.021.
基金信息:
国家自然科学基金(61402537); 中国科学院“西部青年学者”项目; 四川省委组织部人才资助项目