Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2016 | OriginalPaper | Chapter

9. Particle Swarm Optimization

Authors : Ke-Lin Du, M. N. S. Swamy

Published in: Search and Optimization by Metaheuristics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

PSO can locate the region of the optimum faster than EAs, but once in this region it progresses slowly due to the fixed velocity stepsize. Almost all variants of PSO try to solve the stagnation problem. This chapter is dedicated to PSO as well as its variants.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Topics in Evolutinary Algorithms
next chapter Artificial Immune Systems
Literature
1.
Akat SB, Gazi V. Decentralized asynchronous particle swarm optimization. In: Proceedings of the IEEE swarm intelligence symposium, St. Louis, MO, USA, September 2008. p. 1–8.
2.
Alatas B, Akin E, Bedri A. Ozer, Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals. 2009;40(5):1715–34.MathSciNetCrossRefMATH
3.
Al-kazemi B, Mohan CK. Multi-phase discrete particle swarm optimization. In: Proceedings of the 4th international workshop on frontiers in evolutionary algorithms, Kinsale, Ireland, January 2002.
4.
Angeline PJ. Using selection to improve particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, Anchorage, AK, USA, May 1998. p. 84–89.
5.
Ardizzon G, Cavazzini G, Pavesi G. Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Inf Sci. 2015;299:337–78.CrossRef
6.
Baskar S, Suganthan P. A novel concurrent particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC), Beijing, China, June 2004. p. 792–796.
7.
Bastos-Filho CJA, Carvalho DF, Figueiredo EMN, de Miranda PBC. Dynamicclan particle swarm optimization. In: Proceedings of the 9th international conference on intelligent systems design and applications (ISDA’09), Pisa, Italy, November 2009. p. 249–254.
8.
Blackwell TM, Bentley P. Don’t push me! Collision-avoiding swarms. In: Proceedings of congress on evolutionary computation, Honolulu, HI, USA, May 2002, vol. 2. p. 1691–1696.
9.
Blackwell T, Branke J. Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput. 2006;10(4):459–72.CrossRef
10.
Bonyadi MR, Michalewicz Z. A locally convergent rotationally invariant particle swarm optimization algorithm. Swarm Intell. 2014;8:159–98.CrossRef
11.
Brits R, Engelbrecht AF, van den Bergh F. A niching particle swarm optimizer. In: Proceedings of the 4th Asia-Pacific conference on simulated evolutions and learning, Singapore, November 2002. p. 692–696.
12.
Carlisle A, Dozier G. An off-the-shelf PSO. In: Proceedings of workshop on particle swarm optimization, Indianapolis, IN, USA, Jannuary 2001. p. 1–6.
13.
Carvalho DF, Bastos-Filho CJA. Clan particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC), Hong Kong, China, June 2008. p. 3044–3051.
14.
Cervantes A, Galvan IM, Isasi P. AMPSO: a new particle swarm method for nearest neighborhood classification. IEEE Trans Syst Man Cybern Part B. 2009;39(5):1082–91.CrossRef
15.
Chatterjee S, Goswami D, Mukherjee S, Das S. Behavioral analysis of the leader particle during stagnation in a particle swarm optimization algorithm. Inf Sci. 2014;279:18–36.MathSciNetCrossRef
16.
17.
Chen W-N, Zhang J, Lin Y, Chen N, Zhan Z-H, Chung HS-H, Li Y, Shi Y-H. Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput. 2013;17(2):241–58.CrossRef
18.
Cheng R, Jin Y. A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci. 2015;291:43–60.MathSciNetCrossRef
19.
Chen G, Yu J. Two sub-swarms particle swarm optimization algorithm. In: Advances in natural computation, vol. 3612 of Lecture notes in computer science. Berlin: Springer; 2005. p. 515–524.
20.
Cleghorn CW, Engelbrecht AP. A generalized theoretical deterministic particle swarm model. Swarm Intell. 2014;8:35–59.CrossRef
21.
Cleghorn CW, Engelbrecht AP. Particle swarm variants: standardized convergence analysis. Swarm Intell. 2015;9:177–203.CrossRef
22.
Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput. 2002;6(1):58–73.CrossRef
23.
Clerc M. Particle swarm optimization. In: International scientific and technical encyclopaedia. Hoboken: Wiley; 2006.
24.
Coelho LS, Krohling RA. Predictive controller tuning using modified particle swarm optimisation based on Cauchy and Gaussian distributions. In: Proceedings of the 8th online world conference soft computing and industrial applications, Dortmund, Germany, September 2003. p. 7–12.
25.
de Oca MAM, Stutzle T, Birattari M, Dorigo M. Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput. 2009;13(5):1120–32.
26.
de Oca MAM, Stutzle T, Van den Enden K, Dorigo M. Incremental social learning in particle swarms. IEEE Trans Syst Man Cybern Part B. 2011;41(2):368–84.CrossRef
27.
Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC), La Jolla, CA, USA, July 2000. p. 84–88.
28.
El-Abd M, Kamel MS. Information exchange in multiple cooperating swarms. In: Proceedings of IEEE swarm intelligence symposium, Pasadena, CA, USA, June 2005. p. 138–142.
29.
Esquivel SC, Coello CAC. On the use of particle swarm optimization with multimodal functions. In: Proceedings of IEEE congress on evolutionary computation (CEC), Canberra, Australia, 2003. p. 1130–1136.
30.
Fan SKS, Liang YC, Zahara E. Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Eng Optim. 2004;36(4):401–18.CrossRef
31.
Fernandez-Martinez JL, Garcia-Gonzalo E. Stochastic stability analysis of the linear continuous and discrete PSO models. IEEE Trans Evol Comput. 2011;15(3):405–23.CrossRef
32.
Hakli H, Uguz H. A novel particle swarm optimization algorithm with Levy flight. Appl Soft Comput. 2014;23:333–45.CrossRef
33.
He S, Wu QH, Wen JY, Saunders JR, Paton RC. A particle swarm optimizer with passive congregation. Biosystems. 2004;78:135–47.CrossRef
34.
Higashi N, Iba H. Particle swarm optimization with Gaussian mutation. In: Proceedings of IEEE swarm intelligence symposium, Indianapolis, IN, USA, April 2003. p. 72–79.
35.
Ho S-Y, Lin H-S, Liauh W-H, Ho S-J. OPSO: orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans Syst Man Cybern Part A. 2008;38(2):288–98.
36.
Hsieh S-T, Sun T-Y, Liu C-C, Tsai S-J. Efficient population utilization strategy for particle swarm optimizer. IEEE Trans Syst Man Cybern Part B. 2009;39(2):444–56.CrossRef
37.
Huang H, Qin H, Hao Z, Lim A. Example-based learning particle swarm optimization for continuous optimization. Inf Sci. 2012;182:125–38.MathSciNetCrossRefMATH
38.
Janson S, Middendorf M. A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybern Part B. 2005;35(6):1272–82.CrossRef
39.
Juang C-F. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern Part B. 2004;34(2):997–1006.CrossRef
40.
Juang C-F, Chung I-F, Hsu C-H. Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization. Fuzzy Sets Syst. 2007;158(18):1979–96.MathSciNetCrossRefMATH
41.
Kadirkamanathan V, Selvarajah K, Fleming PJ. Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans Evol Comput. 2006;10(3):245–55.CrossRef
42.
Kennedy J. Bare bones particle swarms. In: Proceedings of IEEE swarm intelligence symposium, Indianapolis, IN, USA, April 2003. p. 80–87.
43.
Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE conference on systems, man, and cybernetics, Orlando, FL, USA, October 1997. p. 4104–4109.
44.
Kennedy J, Eberhart RC. Swarm intelligence. San Francisco, CA: Morgan Kaufmann; 2001.
45.
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, WA, USA, November 1995, vol. 4. p. 1942–1948.
46.
Kennedy J, Mendes R. Population structure and particle swarm performance. In: Proceedings of congress on evolutionary computation, Honolulu, HI, USA, May 2002. p. 1671–1676.
47.
Kennedy J. Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. In: Proceedings of congress on evolutionary computation (CEC), Washington, DC, USA, July 1999. p. 1931–1938.
48.
Kennedy J. Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of congress on evolutionary computation (CEC), La Jolla, CA, July 2000. p. 1507–1512.
49.
Kennedy J. The particle swarm: social adaptation of knowledge. In: Proceedings of IEEE international conference on evolutionary computation, Indianapolis, USA, April 1997. p. 303–308.
50.
Koh B-I, George AD, Haftka RT, Fregly BJ. Parallel asynchronous particle swarm optimization. Int J Numer Methods Eng. 2006;67:578–95.CrossRefMATH
51.
Krohling RA. Gaussian swarm: a novel particle swarm optimization algorithm. In: Proceedings of IEEE conference cybernetics and intelligent systems, Singapore, December 2004. p. 372–376.
52.
Langdon WB, Poli R. Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput. 2007;11(5):561–78.CrossRef
53.
Lanzarini L, Leza V, De Giusti A. Particle swarm optimization with variable population size. In: Proceedings of the 9th international conference on artificial intelligence and soft computing, Zakopane, Poland, June 2008, vol. 5097 of Lecture notes in computer science. Berlin: Springer; 2008. p. 438–449.
54.
Li X. Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of genetic and evolutionary computation conference (GECCO), Seattle, WA, USA, June 2004. p. 105–116.
55.
Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput. 2006;10(3):281–95.CrossRef
56.
Liao C-J, Tseng C-T, Luarn P. A discrete version of particle swarm optimization for flowshop scheduling problems. Comput Oper Res. 2007;34:3099–111.CrossRefMATH
57.
Liu Y, Qin Z, Shi Z, Lu J. Center particle swarm optimization. Neurocomputing. 2007;70:672–9.CrossRef
58.
Liu H, Abraham A. Fuzzy adaptive turbulent particle swarm optimization. In: Proceedings of the 5th international conference on hybrid intelligent systems (HIS’05), Rio de Janeiro, Brazil, November 2005. p. 445–450.
59.
Loengarov A, Tereshko V. A minimal model of honey bee foraging. In: Proceedings of IEEE swarm intelligence symposium, Indianapolis, IN, USA, May 2006. p. 175–182.
60.
Lovbjerg M, Krink T. Extending particle swarm optimisers with self-organized criticality. In: Proceedings of congress on evolutionary computation (CEC), Honolulu, HI, USA, May 2002. p. 1588–1593.
61.
Lovbjerg M, Rasmussen TK, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations. In: Proceedings of genetic and evolutionary computation conference (GECCO), Menlo Park, CA, USA, August 2001. p. 469–476.
62.
Martinez-Garcia FJ, Moreno-Perez JA. Jumping frogs optimization: a new swarm method for discrete optimization. Technical Report DEIOC 3/2008, Department of Statistics, O.R. and Computing, University of La Laguna, Tenerife, Spain, 2008.
63.
Miranda V, Fonseca N. EPSO—Best of two worlds meta-heuristic applied to power system problems. In: Proceedings of IEEE congress on evolutionary computation, Honolulu, HI, USA, May 2002. p. 1080–1085.
64.
Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput. 2004;8(3):204–10.CrossRef
65.
Netjinda N, Achalakul T, Sirinaovakul B. Particle swarm optimization inspired by starling flock behavior. Appl Soft Comput. 2015;35:411–22.CrossRef
66.
Niu B, Zhu Y, He X. Multi-population cooperative particle swarm optimization. In: Proceedings of European conference on advances in artificial life, Canterbury, UK, September 2005. p. 874–883.
67.
68.
Pan F, Hu X, Eberhart RC, Chen Y. An analysis of bare bones particle swarm. In: Proceedings of the IEEE swarm intelligence symposium, St. Louis, MO, USA, September 2008. p. 21–23.
69.
Parrott D, Li X. Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput. 2006;10(4):440–58.CrossRef
70.
Parsopoulos KE, Vrahatis MN. UPSO: a unified particle swarm optimization scheme. In: Proceedings of the international conference of computational methods in sciences and engineering, 2004. The Netherlands: VSP International Science Publishers; 2004. pp. 868–873.
71.
Parsopoulos KE, Vrahatis MN. On the computation of all global minimizers through particle swarm optimization. IEEE Trans Evol Comput. 2004;8(3):211–24.MathSciNetCrossRef
72.
Passaro A, Starita A. Clustering particles for multimodal function optimization. In: Proceedings of ECAI workshop on evolutionary computation, Riva del Garda, Italy, 2006. p. 124–131.
73.
Pedersen MEH, Chipperfield AJ. Simplifying particle swarm optimization. Appl Soft Comput. 2010;10(2):618–28.CrossRef
74.
Peram T, Veeramachaneni K, Mohan CK. Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the IEEE swarm intelligence symposium, Indianapolis, IN, USA, April 2003. p. 174–181.
75.
Pulido GT, Coello CAC. Using clustering techniques to improve the performance of a particle swarm optimizer. In: Proceedings of genetic and evolutionary computation conference (GECCO), Seattle, WA, USA, June 2004. p. 225–237.
76.
Qin Q, Cheng S, Zhang Q, Li L, Shi Y. Biomimicry of parasitic behavior in a coevolutionary particle swarm optimization algorithm for global optimization. Appl Soft Comput. 2015;32:224–40.CrossRef
77.
Rada-Vilela J, Zhang M, Seah W. A performance study on synchronicity and neighborhood size in particle swarm optimization. Soft Comput. 2013;17:1019–30.CrossRef
78.
Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240–55.CrossRef
79.
Reeves WT. Particle systems—a technique for modeling a class of fuzzy objects. ACM Trans Graph. 1983;2(2):91–108.
80.
Secrest BR, Lamont GB. Visualizing particle swarm optimizationGaussian particle swarm optimization. In: Proceedings of the IEEE swarm intelligence symposium, Indianapolis, IN, USA, April 2003. p. 198–204.
81.
Seo JH, Lim CH, Heo CG, Kim JK, Jung HK, Lee CC. Multimodal function optimization based on particle swarm optimization. IEEE Trans Magn. 2006;42(4):1095–8.CrossRef
82.
Settles M, Soule T. Breeding swarms: a GA/PSO hybrid. In: Proceedings of genetic and evolutionary computation conference (GECCO), Washington, DC, USA, June 2005. p. 161–168.
83.
Shi Y, Eberhart RC. A modified particle swarm optimizer. In: Proceedings of IEEE congress on evolutionary computation, Anchorage, AK, USA, May 1998. p. 69–73.
84.
Silva A, Neves A, Goncalves T. An heterogeneous particle swarm optimizer with predator and scout particles. In: Proceedings of the 3rd international conference on autonomous and intelligent systems (AIS 2012), Aveiro, Portugal, June 2012. p. 200–208.
85.
Stacey A, Jancic M, Grundy I. Particle swarm optimization with mutation. In: Proceedings of IEEE congress on evolutionary computation (CEC), Canberra, Australia, December 2003. p. 1425–1430.
86.
Suganthan PN. Particle swarm optimizer with neighborhood operator. In: Proceedings of IEEE congress on evolutionary computation (CEC), Washington, DC, USA, July 1999. p. 1958–1962.
87.
van den Bergh F, Engelbrecht AP. A new locally convergent particle swarm optimizer. In: Proceedings of IEEE conference on systems, man, and cybernetics, Hammamet, Tunisia, October 2002, vol. 3. p. 96–101.
88.
van den Bergh F, Engelbrecht AP. A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput. 2004;3:225–39.
89.
90.
Vrugt JA, Robinson BA, Hyman JM. Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput. 2009;13(2):243–59.CrossRef
91.
Wang H, Liu Y, Zeng S, Li C. Opposition-based particle swarm algorithm with Cauchy mutation. In: Proceedings of the IEEE congress on evolutionary computation (CEC), Singapore, September 2007. p. 4750–4756.
92.
Yang C, Simon D. A new particle swarm optimization technique. In: Proceedings of the 18th IEEE international conference on systems engineering, Las Vegas, NV, USA, August 2005. p. 164–169.
93.
Zhan Z-H, Zhang J, Li Y, Chung HS-H. Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B. 2009;39(6):1362–81.CrossRef
94.
Zhang J, Huang DS, Lok TM, Lyu MR. A novel adaptive sequential niche technique for multimodal function optimization. Neurocomputing. 2006;69:2396–401.CrossRef
95.
Zhang J, Liu K, Tan Y, He X. Random black hole particle swarm optimization and its application. In: Proceedings on IEEE international conference on neural networks and signal processing, Nanjing, China, June 2008. p. 359–365.
Metadata
Title
Particle Swarm Optimization
Authors
Ke-Lin Du
M. N. S. Swamy
Copyright Year
2016
Book
Search and Optimization by Metaheuristics

Print ISBN: 978-3-319-41191-0

Electronic ISBN: 978-3-319-41192-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-41192-7_9

Premium Partner

    Image Credits