Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Swarm Intelligence: A Review of Algorithms

  • Chapter
  • First Online:
Nature-Inspired Computing and Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 10))

Abstract

Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kar AK (2016) Bio-inspired computing—a review of algorithms and scope of applications. Expert Syst Appl 59:20–32

    Article  Google Scholar 

  2. Parpinelli RS, Lopes HS, Freitas AA (2001) An ant colony based system for data mining:Applications to medical data. In: Lee S, Goodman E, Wu A, Langdon WB, Voigt H, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001), San Francisco, California, USA, 7–11. Morgan Kaufmann, pp 791–797

    Google Scholar 

  3. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, vol 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

    Google Scholar 

  4. Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23):2325–2336

    Article  Google Scholar 

  5. Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glow-worm metaphor with applications to collective robotics. In: IEEE swarm intelligence symposium, Pasadena, CA, pp 84–91

    Google Scholar 

  6. Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Google Scholar 

  7. Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: AIP conference proceedings, vol 953, pp 162–173

    Google Scholar 

  8. Yazdani M, Jolai F (2015) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng (in press)

    Google Scholar 

  9. Raton FL, USA, pp 351–392. Liu C, Yan X, Liu C, Wu H (2011) The wolf colony algorithm and its application. Chin J Electron 20:212–216

    Google Scholar 

  10. Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3:267–274

    Article  Google Scholar 

  11. Prabha MS, Vijayarani S (2011) Association rule hiding using artificial bee colony algorithm. Int J Comput Appl 33(2):41–47

    Google Scholar 

  12. Crawford B, Soto R, Johnson F, Monfroy E, Paredes F (2014) A max-min ant system algorithm to solve the software project scheduling problem. Expert Syst Appl 41(15):6634–6645

    Article  Google Scholar 

  13. Hu XM, Zhang J, Yun Li Y (2008) Orthogonal methods based ant colony search for solving continuous optimization problems. J Comput Sci Technol 23(1):2–18

    Article  Google Scholar 

  14. Gupta DK, Arora Y, Singh UK, Gupta JP (2012) Recursive ant colony optimization for estimation of parameters of a function. In: 1st international conference on recent advances in information technology (RAIT), pp 448–454

    Google Scholar 

  15. Abraham A, Ramos V (2003) Web usage mining using artificial ant colony clustering. In: Proceedings of congress on evolutionary computation (CEC2003), Australia, IEEE Press, pp 1384–1391. ISBN 0780378040

    Google Scholar 

  16. Handl J, Knowles J, Dorigo M (2003) Ant-based clustering: a comparative study of itsrelative performance with respect to k-means, average link and 1d-som. Technical ReportTR/IRIDIA/2003-24, Universite Libre de Bruxelles

    Google Scholar 

  17. Schockaert S, De Cock M, Cornelis C, Kerre EE (2004) Efficient clustering with fuzzy ants. Appl Comput Intell

    Google Scholar 

  18. Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimizationalgorithm. IEEE Trans Evol Comput 6(4):321–332

    Article  MATH  Google Scholar 

  19. Ramos V, Abraham A (2003) Swarms on continuous data. In: Proceedings of the congress on evolutionary computation. IEEE Press, pp 1370–1375

    Google Scholar 

  20. Liu B, Abbass HA, McKay B (2004) Classification rule discovery with ant colonyoptimization. IEEE Comput Intell Bull 3(1):31–35

    Google Scholar 

  21. Gambardella LM, Dorigo M (1995) Ant-q: A reinforcement learning approach to the traveling salesman problem. In: Proceedings of the eleventh international conference on machine learning, pp 252–260

    Google Scholar 

  22. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B: Cybern 26(1):29–41

    Article  Google Scholar 

  23. Gambardella LM, Dorigo M (1996) Solving symmetric and asymmetric tsps by ant colonies. In: Proceedings of the IEEE international conference on evolutionary computation (ICEC’96), pp 622–627

    Google Scholar 

  24. Stutzle T, Hoos HH (1997) The MAX-MIN ant system and local search for the traveling salesman problem. In: Proceedings of the IEEE international conference on evolutionary computation (ICEC’97), pp 309–314

    Google Scholar 

  25. Stutzle T, Hoos HH (1998) Improvements on the ant system: introducing the MAX-MIN ant system. In: Steele NC, Albrecht RF, Smith GD (eds) Neural Artificial networks and genetic, algorithms, pp 245–249

    Google Scholar 

  26. Stutzle T, Hoos HH (1999) MAX-MIN ant system and local search for combinatorial optimization problems. In: Osman IH, Voss S, Martello S, Roucairol C (eds) Meta-heuristics: advances and trends in local search paradigms for optimization, pp 313–329

    Google Scholar 

  27. Eyckelhof CJ, Snoek M (2002) Ant systems for a dynamic tsp. In: ANTS ’02: Proceedings of the third international workshop on ant algorithms, London, UK. Springer, pp 88–99

    Google Scholar 

  28. Bullnheimer B, Hartl RF, Strauss C (1999) Applying the ant system to the vehicle routing problem. In: Roucairol C, Voss S, Martello S, Osman IH (eds) Meta-heuristics, advances and trends in local search paradigms for optimization

    Google Scholar 

  29. Cicirello VA, Smith SF (2001) Ant colony control for autonomous decentralized shop floor routing. In: The fifth international symposium on autonomous decentralized systems, pp 383–390

    Google Scholar 

  30. Wade A, Salhi S (2004) An ant system algorithm for the mixed vehicle routing problem with backhauls. In: Metaheuristics: computer decision-making, Norwell, MA, USA, 2004. Kluwer Academic Publishers, pp 699–719

    Google Scholar 

  31. Maniezzo V (1998) Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. Research CSR 98-1, Scienze dell’Informazione, Università di Bologna, Sede di Cesena, Italy

    Google Scholar 

  32. Maniezzo V, Colorni A (1999) The ant system applied to the quadratic assignment problem. IEEE Trans Knowl Data Eng

    Google Scholar 

  33. Gambardella LM, Taillard E, Dorigo M (1999) Ant colonies for the quadratic assignment problem. J Oper Res Soc 50:167–176

    Article  MATH  Google Scholar 

  34. Stutzle T, Dorigo M (1999) ACO algorithms for the quadratic assignment problem. In: Dorigo M, Corne D, Glover F (eds) New ideas in optimization

    Google Scholar 

  35. Colorni A, Dorigo M, Maniezzo V, Trubian M (1994) Ant system for job shop scheduling. J Oper Res Stat Comput Sci 34(1):39–53

    Google Scholar 

  36. Forsyth P, Wren A (1997) An ant system for bus driver scheduling. Research Report 97.25, University of Leeds School of Computer Studies

    Google Scholar 

  37. Socha K, Knowles J, Sampels M (2002) A MAX-MIN ant system for the university timetabling problem. In: Dorigo M, Di Caro G, Sampels M (eds) Proceedings of ANTS2002—third international workshop on ant algorithms. Lecture notes in computer science, vol 2463. Springer, Berlin, Germany, pp 1–13

    Google Scholar 

  38. Schoonderwoerd R, Holland OE, Bruten JL, Rothkrantz LJM (1996) Ant-based loadbalancing in telecommunications networks. Adapt Behav 2:169–207

    Google Scholar 

  39. Di Caro G, Dorigo M (1998) Antnet: distributed stigmergetic control forcommunications networks. J Artif Intell Res 9:317–365

    MATH  Google Scholar 

  40. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theoret Comput Sci 344(2):243–278

    Article  MathSciNet  MATH  Google Scholar 

  41. Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  42. Dorigo M, Birattari M (2010) Ant colony optimization. In: Encyclopedia of machine learning. Springer US, pp 36–39

    Google Scholar 

  43. Hong TP, Tung YF, Wang SL, Wu YL, Wu MT (2012) A multi-level ant-colony mining algorithm for membership functions. Inf Sci 182(1):3–14

    Article  MathSciNet  Google Scholar 

  44. Bououden S, Chadli M, Karimi HR (2015) An ant colony optimization-based fuzzy predictive control approach for nonlinear processes. Inf Sci 299:143–158

    Article  MathSciNet  Google Scholar 

  45. Mandloi M, Bhatia V (2015) Congestion control based ant colony optimization algorithm for large MIMO detection. Expert Syst Appl 42(7):3662–3669

    Article  Google Scholar 

  46. Ghasab MAJ, Khamis S, Mohammad F, Fariman HJ (2015) Feature decision-making ant colony optimization system for an automated recognition of plant species. Expert Syst Appl 42(5):2361–2370

    Article  Google Scholar 

  47. Kuo RJ, Chiu CY, Lin YJ (2004) Integration of fuzzy theory and ant algorithm for vehicle routing problem with time window. In: IEEE annual meeting of the fuzzy information, 2004. Processing NAFIPS’04, vol 2, pp 925–930. IEEE

    Google Scholar 

  48. Chiu CY, Kuo IT, Lin CH (2009) Applying artificial immune system and ant algorithm in air-conditioner market segmentation. Expert Syst Appl 36(3):4437–4442

    Article  Google Scholar 

  49. Hua XY, Zheng J, Hu WX (2010) Ant colony optimization algorithm for computing resource allocation based on cloud computing environment [J]. J East China Normal Univ (Nat Sci) 1(1):127–134

    Google Scholar 

  50. Chiu CY, Lin CH (2007) Cluster analysis based on artificial immune system and ant algorithm. In: Third international conference on natural computation (ICNC 2007), vol 3, pp 647–650. IEEE

    Google Scholar 

  51. Abraham A, Ramos V (2003) Web usage mining using artificial ant colony clustering and linear genetic programming. In: The 2003 congress on evolutionary computation, 2003. CEC’03, vol 2, pp 1384–1391. IEEE

    Google Scholar 

  52. Wu L (2011) UCAV path planning based on FSCABC. Inf–Int Interdiscip J 14(3):687–692

    MathSciNet  Google Scholar 

  53. Ding L, Hongtao W, Yu Y (2015) Chaotic artificial bee colony algorithm for system identification of a small-scale unmanned helicopter. Int J Aerosp Eng 2015, Article ID 801874:1–12

    Google Scholar 

  54. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  55. Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31(1–4):61–85

    Article  Google Scholar 

  56. Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697

    Article  MATH  Google Scholar 

  57. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  58. Deng X (2013) An enhanced artificial bee colony approach for customer segmentation in mobile e-commerce environment. Int J Adv Comput Technol 5(1)

    Google Scholar 

  59. Babu MSP, Rao NT (2010) Implementation of artificial bee colony (ABC) algorithm on garlic expert advisory system. Int J Comput Sci Res 1(1):69–74

    Google Scholar 

  60. Lukasik S, Zak S (2009) Firefly algorithm for continuous constrained optimization tasks. In: Computational collective intelligence. Semantic web, social networks and multiagent systems. Springer, Berlin, Heidelberg, pp 97–106

    Google Scholar 

  61. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications. Springer, Berlin, Heidelberg, pp 169–178

    Google Scholar 

  62. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84

    Article  Google Scholar 

  63. Yang X-S, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: Gonzalez JR (ed) Nature inspired cooperative strategies for optimization (NISCO 2010), SCI 284. Springer, Berlin, pp 101–111

    Google Scholar 

  64. Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186

    Article  Google Scholar 

  65. Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  66. Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056

    Article  Google Scholar 

  67. Mishra A, Agarwal C, Sharma A, Bedi P (2014) Optimized gray-scale image watermarking using DWT-SVD and firefly algorithm. expert syst appl 41(17):7858–7867

    Article  Google Scholar 

  68. Rahmani A, MirHassani SA (2014) A hybrid firefly-genetic algorithm for the capacitated facility location problem. Inf Sci 283:70–78

    Article  MathSciNet  MATH  Google Scholar 

  69. Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl 42(21):8221–8231

    Article  Google Scholar 

  70. Verma OP, Aggarwal D, Patodi T (2015) Opposition and dimensional based modified firefly algorithm. Expert Syst Appl

    Google Scholar 

  71. Apostolopoulos T, Vlachos A (2010) Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int J Comb 2011

    Google Scholar 

  72. Kazem A, Sharifi E, Hussain FK, Saberi M, Hussain OK (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl Soft Comput 13(2):947–958

    Article  Google Scholar 

  73. Horng MH (2012) Vector quantization using the firefly algorithm for image compression. Expert Syst Appl 39(1):1078–1091

    Article  Google Scholar 

  74. Sayadi MK, Hafezalkotob A, Naini SGJ (2013) Firefly-inspired algorithm for discrete optimization problems: an application to manufacturing cell formation. J Manuf Syst 32(1):78–84

    Article  Google Scholar 

  75. Karthikeyan S, Asokan P, Nickolas S, Page T (2015) A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems. Int J Bio-Inspired Comput 7(6):386–401

    Article  Google Scholar 

  76. dos Santos Coelho L, Mariani VC (2013) Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278

    Article  Google Scholar 

  77. Krishnanand KN, Ghose D (2009a) Glowworm swarm optimization: a new method foroptimizing multi-modal functions. Int J Comput Intell Stud 1(1):84–91

    Article  Google Scholar 

  78. Krishnanand KN, Ghose D (2009b) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3(2):87–124

    Article  Google Scholar 

  79. Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005, pp 84–91

    Google Scholar 

  80. Krishnanand KN, Ghose D (2009) A glowworm swarm optimization based multi-robot system for signal source localization. In: Design and control of intelligent robotic systems. Springer, Berlin, Heidelberg, pp 49–68

    Google Scholar 

  81. Senthilnath J, Omkar SN, Mani V, Tejovanth N, Diwakar PG, Shenoy AB (2012) Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE J Sel Top Appl Earth Obs Remote Sens 5(3):762–768

    Article  Google Scholar 

  82. Gong Q, Zhou Y, Luo Q (2011) Hybrid artificial glowworm swarm optimization algorithm for solving multi-dimensional knapsack problem. Procedia Eng 15:2880–2884

    Article  Google Scholar 

  83. Zhou YQ, Huang ZX, Liu HX (2012) Discrete glowworm swarm optimization algorithm for TSP problem. Dianzi Xuebao (Acta Electronica Sinica) 40(6):1164–1170

    Google Scholar 

  84. Di Silvestre ML, Graditi G, Sanseverino ER (2014) A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach. IEEE Trans Ind Inform 10(1):152–162

    Article  Google Scholar 

  85. Al-Madi N, Aljarah I, Ludwig SA (2014) Parallel glowworm swarm optimization clustering algorithm based on MapReduce. In: 2014 IEEE symposium on swarm intelligence (SIS). IEEE, pp 1–8

    Google Scholar 

  86. Yang XS (2010). A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74

    Google Scholar 

  87. Jaddi NS, Abdullah S, Hamdan AR (2015) Multi-population cooperative bat algorithm-based optimization of artificial neural network model. Inf Sci 294:628–644

    Article  MathSciNet  Google Scholar 

  88. Rekaby A (2013) Directed artificial bat algorithm (DABA): a new bio-inspired algorithm. In: International conference on advances in computing, communications and informatics (ICACCI), Mysore

    Google Scholar 

  89. Mirjalili S, Mirjalili SM, Yang X (2014) Binary bat algorithm, neural computing and applications (in press) (2014). Springer. doi:10.1007/s00521-013-1525-5

  90. Yang XS (2011) Bat algorithm for multi-objective optimization. Int J Bio-Inspired Comput 3(5):267–274

    Article  Google Scholar 

  91. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2012) Bat algorithm for constrained optimization tasks. Neural Comput Appl doi:10.1007/s00521-012-1028-9

  92. Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149

    Article  Google Scholar 

  93. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  94. Rodrigues D, Pereira LA, Nakamura RY, Costa KA, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst Appl 41(5):2250–2258

    Article  Google Scholar 

  95. Meng XB, Gao XZ, Liu Y, Zhang H (2015) A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Syst Appl 42(17):6350–6364

    Article  Google Scholar 

  96. Svečko R, Kusić D (2015) Feed-forward neural network position control of a piezoelectric actuator based on a BAT search algorithm. Expert Syst Appl 42(13):5416–5423

    Article  Google Scholar 

  97. Ali ES (2014) Optimization of power system stabilizers using BAT search algorithm. Int J Electr Power Energy Syst 61:683–690

    Article  Google Scholar 

  98. Li L, Halpern JY, Bahl P, Wang YM, Wattenhofer R (2005) A cone-based distributed topology-control algorithm for wireless multi-hop networks. IEEE/ACM Trans Netw 13(1):147–159

    Article  MATH  Google Scholar 

  99. Musikapun P, Pongcharoen P (2012) Solving multi-stage multi-machine multi-product scheduling problem using bat algorithm. In: 2nd international conference on management and artificial intelligence, vol 35. IACSIT Press Singapore, pp 98–102

    Google Scholar 

  100. Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math (2013)

    Google Scholar 

  101. Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In 2012 25th SIBGRAPI conference on graphics, patterns and images. IEEE, pp 291–297

    Google Scholar 

  102. Hasançebi O, Teke T, Pekcan O (2013) A bat-inspired algorithm for structural optimization. Comput Struct 128:77–90

    Article  Google Scholar 

  103. Khan K, Nikov A, Sahai A (2011) A fuzzy bat clustering method for ergonomic screening of office workplaces. In: Third international conference on software, services and semantic technologies S3T 2011. Springer, Berlin, Heidelberg, pp 59–66

    Google Scholar 

  104. Yi T-H, Li H-N, Zhang X-D (2012) Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm. Smart Mater Struct 21. doi:10.1088/0964-1726/21/12/125023

  105. Ramos-Frenańdez G, Mateos JL, Miramontes O, Cocho G, Larralde H, Ayala-Orozco B (2004) Levy walk patterns in the foraging movements of spider monkeys (Atelesgeoffroyi). Behav Ecol Sociobiol 55(223):230

    Google Scholar 

  106. Zhao R, Tang W (2008) Monkey algorithm for global numerical optimization. J Uncertain Syst 2(3):165–176

    Google Scholar 

  107. Wang J, Yu Y, Zeng Y, Luan W (2010). Discrete monkey algorithm and its application in transmission network expansion planning. In: IEEE conference on power and energy society general meeting, July 2010, pp 1–5

    Google Scholar 

  108. Vu PV, Chandler DM (2012) A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Process Lett 19(7):423–426

    Article  Google Scholar 

  109. Zhang S, Yang J, Cheedella V (2007) Monkey: approximate graph mining based on spanning trees. In: 2007 IEEE 23rd international conference on data engineering. IEEE, pp 1247–1249

    Google Scholar 

  110. Yi TH, Li HN, Zhang XD (2012) Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm. Smart Mater Struct 21(12):125023

    Article  Google Scholar 

  111. Rajkumar BR (2014) Lion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion’s social behaviour. In: IEEE congress on evolutionary computation, July 2014, pp 2116–2123

    Google Scholar 

  112. Shah-Hosseini H, Safabakhsh R (2003) A TASOM-based algorithm for active contour modeling. Pattern Recogn Lett 24(9):1361–1373

    Article  MATH  Google Scholar 

  113. Tang R, Fong S, Yang X.-S, Deb S (2012) Wolf search algorithm with ephemeral memory. In: IEEE seventh international conference on digital information management (ICDIM 2012), Aug 2012, pp 165–172

    Google Scholar 

  114. Wang J, Jia Y, Xiao Q (2015). Application of wolf pack search algorithm to optimal operation of hydropower station. Adv Sci Technol Water Resour 35(3):1–4 & 65

    Google Scholar 

  115. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  116. Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 1–12

    Google Scholar 

  117. Nipotepat M, Sunat K, Chiewchanwattana S (2014) An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets. In: IEEE international conference in computer science and engineering

    Google Scholar 

  118. Wong LI et al (2014) Grey wolf optimizer for solving economic dispatch problems. In: IEEE international conference on power and energy

    Google Scholar 

  119. Mee SH, Sulaiman MH, Mohamed MR (2014) An application of grey wolf optimizer for solving combined economic emission dispatch problems. Int Rev Modell Simul (IREMOS) 7(5):838–844

    Article  Google Scholar 

  120. El-Gaafary Ahmed AM et al (2015) Grey wolf optimization for multi input multi output system. Generations 10:11

    Google Scholar 

  121. Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 1–7

    Google Scholar 

  122. Madadi A, Motlagh MM (2014) Optimal control of DC motor using grey wolf optimizer algorithm. TJEAS J-2014-4-04/373-379, 4(4):373–379

    Google Scholar 

  123. Emary E, Zawbaa HM, Grosan C, Hassenian AE (2015) Feature subset selection approach by gray-wolf optimization. In: Afro-European conference for industrial advancement. Springer International Publishing, pp 1–13

    Google Scholar 

  124. El-Gaafary AA, Mohamed YS, Hemeida AM, Mohamed AAA (2015) Grey wolf optimization for multi input multi output system. Univ J Commun Netw 3(1):1–6

    Article  Google Scholar 

  125. Huang SJ, Liu XZ, Su WF, Tsai SC, Liao CM (2014) Application of wolf group hierarchy optimization algorithm to fault section estimation in power systems. In: IEEE international symposium on circuits and systems (ISCAS), June 2014, pp 1163–1166

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India

    Amrita Chakraborty

  2. Information Systems, DMS, Indian Institute of Technology, Delhi, 110016, New Delhi, India

    Arpan Kumar Kar

Authors
  1. Amrita Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arpan Kumar Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amrita Chakraborty .

Editor information

Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Chakraborty, A., Kar, A.K. (2017). Swarm Intelligence: A Review of Algorithms. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50920-4_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50919-8

  • Online ISBN: 978-3-319-50920-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation