Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.12.2013 | Original Article

FAIPSO: fuzzy adaptive informed particle swarm optimization

verfasst von: Mehdi Neshat

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Conventional particle swarm optimization (PSO) is an appropriate optimization method, yet it suffers from some drawbacks. Trapping in local minimums or premature convergence of particles leads to unsatisfactory levels of optimization. In this paper, a new method for improving PSO is provided. In the proposed method (FAIPSO), the acceleration coefficients c 1 and c 2 are adaptively adjusted for each particle in each iteration. For the adaptive controlling of the acceleration coefficients, a fuzzy inference system is used. This fuzzy inference system comprises six inputs, two outputs, and ten rules. In order to reduce inertia weight (ω), a parabolic model is used. In addition to this, a range of vision (Mu) is defined for each of the particles and every one of the particles searches within this range. This range of vision changes adaptively. In order to adaptively control the range of vision, a fuzzy inference system is employed. This system has two inputs, one output, and 14 rules. To test the proposed method, 16 benchmarks, each inheriting special characteristics, are used. The performance of the proposed method was compared with that of ten types of PSOs (each of which are among the reputable works of the PSO subject). According to the results, the proposed method shows a good performance and is more appropriate than other methods.
Vorheriger Artikel Nonlinear control of benchmark problems using TSK-type fuzzy neural network
Nächster Artikel Fuzzy inference to supplier evaluation and selection based on quality index: a flexible approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Proceedings of 7th annual conference on evolutionary programming, pp 591–600
2.
Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization. In: Proceedings of congress on evolutionary computation, pp 1945–1950
3.
Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of congress on evolutionary computation, pp 1958–1962
4.
Kennedy J (2000) Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of IEEE international conference on evolutionary computation, pp 1507–1512
5.
Eberhart R, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE international conference on evolutionary computation, pp 81–86
6.
Angelin e PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceedings of 7th annual conference on evolutionary programming, pp 601–610
7.
Prigogine (1967) Introduction to thermodynamics of irreversible processes. Wiley, NY
8.
9.
Xiao-Feng Xie, Wen-Jun Zhang, Zhi-Lian Yang (2002) A dissipative particle swarm optimization. Congress on evolutionary computation (CEC), Honolulu, pp 1456–1461
10.
Cedeno W, Agrafiotis DK (2003) Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression. J Comput Aided Mol Des 17:255–263CrossRef
11.
Shen Q, Jiang JH, Jiao CX, Huan SY, Shen GL, Yu RQ (2004) Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists. J Chem Inf Comput Sci 44:2027–2031CrossRef
12.
Lin W, Jiang J, Shen Q, Shen G, Yu R (2005) Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies. J Chem Inf Model 45:486–493CrossRef
13.
Shen Q, Jiang JH, Jiao CX, Lin WQ, Shen GL, Yu RQ (2004) Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds. J Comput Chem 25:1726–1735CrossRef
14.
Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on evolutionary computation, pp 58–73
15.
Rasmussen TK, Krink T (2003) Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems 72:5–17CrossRef
16.
Veeramachaneni K, Peram T, Mohan CK, Osadciw LA (2003) Optimization using particle swarms with near neighbor interactions. In: Lecture notes in computer science (LNCS) No 2723: proceedings of the genetic and evolutionary computation conference (GECCO) Chicago, pp 110–121
17.
Coello Coello CA, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation 2002 Honolulu, Hawaii
18.
Hu X, Eberhart RC (2002a) Adaptive particle swarm optimization: detection and response to dynamic systems. In: Proceedings of the IEEE congress on evolutionary computation 2002 (CEC 2002) Honolulu, Hawaii
19.
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRef
20.
Li XD, Engelbrecht AP (2007) Particle swarm optimization: an introduction and its recent developments. In: Proceedings of genetic evoloution computation conference, pp 3391–3414
21.
Ho S-Y, Lin H-S, Liauh W-H, Ho S-J (2008) OPSO: orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans Syst Man Cybern A Syst Humans 38(2):288–298CrossRef
22.
Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37(1):18–27CrossRef
23.
Ciuprina G, Ioan D, Munteanu I (2002) Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag 38(2):1037–1040CrossRef
24.
Yamaguchi T, Yasuda K (2006) Adaptive particle swarm optimization: self-coordinating mechanism with updating information. In: Proceedings of IEEE international conference on system man, cybernet, Taipei, pp 2303–2308
25.
Tripathi PK, Bandyopadhyay S, Pal SK (2007) Adaptive multi-objective particle swarm optimization algorithm. In: Proceedings of IEEE congress on evolutinary computation, Singapore, pp 2281–2288
26.
Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proceedings of IEEE congress on evolutinary computation, Anchorage, pp 84–89
27.
Juang CF (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B Cybern 34(2):997–1006CrossRef
28.
Chen YP, Peng WC, Jian MC (2007) Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans Syst Man Cybern B Cybern 37(6):1460–1470CrossRef
29.
Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: Proceedings of IEEE congress on evolutinary computation, Vancouver, pp 1044–1051
30.
Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: Proceedings of IEEE congress on evolutinary computation, pp 522–528
31.
Zhang WJ, Xie XF (2003) “DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of IEEE conference system, man, cybernetics, Oct. 2003, pp 3816–3821
32.
van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRef
33.
Ratnaweera, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRef
34.
Parsopoulos KE, Vrahatis MN (2004) On the computation of all global minimizers through particle swarm optimization. IEEE Trans Evol Comput 8(3):211–224MathSciNetCrossRef
35.
Brits R, Engelbrecht AP, van den Bergh F (2002) A niching particle swarm optimizer. In: Proceedings of 4th Asia-Pacific conference on simulation evoloution learning, pp 692–696
36.
Brits R, Engelbrecht AP, van den Bergh F (2007) Locating multiple optima using particle swarm optimization. Appl Math Comput 189(2):1859–1883MathSciNetCrossRefMATH
37.
Parrott D, Li XD (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–458CrossRef
38.
Zhi-Hui Zhan, Jun Zhang, Yun Li, Henry Shu-Hung Chung (2009) adaptive particle swarm optimization. IEEE Transactions On Systems, Man, And Cybernetics—part B: Cybernetics, pp 1083–4419
39.
Shi Y, Eberhart RC (2001a) Fuzzy adaptive particle swarm optimization. In: Proceedings of congress on evolutionary computation 2001, IEEE Service Center, Seoul, Korea, Piscataway
40.
Kang Q, Wang L, Wu Q (2006) Research on fuzzy adaptive optimization strategy of particle swarm algorithm. Int J Inform Technol 12(3)
41.
Liu H, Abraham A, Zhang W (2007) Fuzzy adaptive turbulent particle swarm optimization. Int J Innov Comput Appl 1(1)
42.
Niu B, Zhu Y, Xian X, Shen H (2007) A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing. Elsevier, Amsterdam
43.
Zahiri SH, Seyedin SA (2007) Swarm intelligence based classifiers. J Franklin Inst 344:362–376CrossRefMATH
44.
Abdelbar AM, Adbelshahid S, Wunsch DC (2005) Fuzzy PSO: a generalization of particle swarm optimization. In: Proceedings of international joint conference on neural networks, Montreal
45.
Liu H, Abraham A (2007) A hybrid fuzzy variable neighborhood particle swarm optimization algorithm for solving quadratic assignment problems. J Univ Comput Sci 13(7):1032–1054
46.
Afsahi Z, Meybodi M (2010) Improving cooperative PSO using fuzzy logic. Research and development in intelligent systems XXVI, Springer, London, pp 219–232
47.
Juang YT, Tung SL, Chiu HC (2010) Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions. Inform Sci J
48.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural network, vol 4, Perth, pp 1942–1948
49.
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of 6th international symposium on micromachine human science, Nagoya, pp 39–43
50.
Kennedy J, Eberhart RC, Shi YH (2001) Swarm intelligence. Morgan Kaufmann, San Mateo
51.
Wilson EO (1975) Sociobiology: the new synthesis. Belknap Press, Cambridge
52.
Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of IEEE international conference on evolutionary computation, pp 303–308
53.
Mehdi Neshat, Masoud Rezaei (2010) AIPSO: adaptive informed particle swarm optimization. IEEE 5th international conference intelligence systems (SI)
54.
Iwasaki N, Yasuda K, Ueno G (2006) Dynamic parameter tuning of particle swarm optimization. IEEJ Trans Elec Elect Eng 1:353–363CrossRef
55.
Montes de Oca MA, Pena J, Stutzle T, Pinciroli C, Dorigo M (2009) Heterogeneous particle swarm optimizers. In: Proceedings of IEEE congress on evolutionary computation, pp 698–705
56.
Pant M, Radha T, Singh VP (2007) A new particle swarm optimization with quadratic interpolation. In: Proceedings of IEEE international conference on computational intelligence and multimedia applications, pp 55–60
57.
Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings of IEEE on swarm intelligence symposium, pp 68–75
58.
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Technical report, Nanyang Technological University, Singapore
59.
Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39(3):263–278CrossRef
60.
Shi Y, Eberhart RC (1998) A modified particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 69–73
61.
Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm scheme. Lect Series Comput Comput Sci 1:868–873MathSciNet
62.
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210CrossRef
63.
Zhan ZH, Zhang J, Li Y, Shi YH (2010) Orthogonal learning particle swarm optimization. IEEE transactions on evolutionary computation, Issue: 99
Metadaten
Titel
FAIPSO: fuzzy adaptive informed particle swarm optimization
verfasst von
Mehdi Neshat
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1256-z

Weitere Artikel der Sonderheft 1/2013

Neural Computing and Applications 1/2013 Zur Ausgabe

Erratum

Erratum to: Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering

Original Article

Assessment of the effect on technical efficiency of bad loans in banking industry: a principal component analysis and neuro-fuzzy system

Original Article

Fuzzy genetic approach for modeling of the critical submergence of an intake

Original Article

A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction

Original Article

A knowledge-based Mamdani fuzzy logic prediction of the motion resistance coefficient in a soil bin facility for clay loam soil

Original Article

International transmission of stock market movements: an adaptive neuro-fuzzy inference system for analysis of TAIEX forecasting

Premium Partner

    Bildnachweise