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
Soft Computing
Published in: Soft Computing 5/2013

01-05-2013 | Methodologies and Application

Neighborhood field for cooperative optimization

Authors: Zhou Wu, Tommy W. S. Chow

Published in: Soft Computing | Issue 5/2013

Log in

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

search-config
loading …

Abstract

Inspired by the biological evolution, local cooperation behaviors have been modeled in function optimizations for providing effective search methods. This paper proposes a new meta-heuristic algorithm named Neighborhood Field Optimization algorithm (NFO), which totally utilizes the local cooperation of individuals. This paper also analyzes how the local cooperation helps optimization, which is modeled as the neighborhood field. The proposed NFO is compared with other widely used evolutionary algorithms in intensive simulation under different benchmark functions. The presented results show that NFO is able to solve multimodal problems globally, and thus the cooperation behavior is proven its significance to model a search method.
previous article Financial time series forecasting using LPP and SVM optimized by PSO
next article An automatic approach to extract goal plans from soccer simulated matches

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 "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

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
Literature
Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, pp 1769–1776
Barraquand J, Langlois B, Latombe JC (1992) Numerical potential field techniques for robot path planning. IEEE Trans Syst Man Cybern 22(2):224–241MathSciNetCrossRef
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657CrossRef
Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8–9):811–831CrossRef
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro machine and Human Science, Nagoya, Japan, pp 39–43
Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 IEEE Congress on Evolutionary Computation 2000 (CEC2000). Newyork, pp 84–89
Eberhart R, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 IEEE Congress on  Evolutionary Computation 2001 (CEC2001), Seoul, Korea, pp 81–86
Garcia-Martinez C, Lozano M, Herrera F, Molina D, Sanchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113MATHCrossRef
Godoy A, Von Zuben FJ (2009) A complex neighborhood based particle swarm optimization. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC2009), Trondheim, Norway, pp. 720–727
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, 1st edn. Addison-Wesley Professional, Reading
Hansen N, Ostermeier A (2001) Completely derandomized selfadaptation in evolution strategies. Evol Comput 9(2):159–195CrossRef
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, WA, pp 1942–1948
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 IEEE Congress of Evolutionary Computation (CEC2002), vol 2 Oregon, pp 1671–1676
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation (CEC 2002). Hawaii, pp 1671–1676
Lampinen J, Storn R (2004) Differential evolution. In: Onwubolu G, Babu BV (eds) New optimization techniques in engineering. Springer, Germany, pp. 123–166
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006a) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRef
Salomon R (1996) Reevaluating genetic algorithm performance under coordinated rotation of benchmark functions. BioSystems 39:263–278CrossRef
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE Congress on Evolutionary Computation (CEC1998). Alaska, pp 69–73
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous space. J Global Optim 11(4):341–359MathSciNetMATHCrossRef
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the cec2005 special session on real parameter optimization. Technical report, Nanyang Technological University
Vesterstrom J, Thomsen (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC2004), vol 2. Hawaii, pp 1980–1987
Vose MD (1999) Simple genetic algorithm: foundation and theory. MIT Press, MI
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442CrossRef
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
Wu Z, Chow TWS (2012) A local multiobjective optimization algorithm using neighborhood field. Struct Multidiscip Optim 45(6):853–870
Xu L, Chow TWS (2010) Self-organizing potential field network: a new optimization algorithm. IEEE Trans Neural Netw 21(9):1482–1495CrossRef
Zelinka I (2004) SOMA-self-organizing migrating algorithm. In: Onwubolu G, Babu BV (eds) New optimization techniques in engineering. Springer, Germany, pp 167–217
Zhong W, Liu J, Xue M, Jiao L (2004) A multiagent genetic algorithm for global numerical optimization. IEEE Trans Syst Man Cybern Part B: Cybern 34(2):1128–1141CrossRef
Metadata
Title
Neighborhood field for cooperative optimization
Authors
Zhou Wu
Tommy W. S. Chow
Publication date
01-05-2013
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 5/2013
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0955-9

Other articles of this Issue 5/2013

Soft Computing 5/2013 Go to the issue

Methodologies and Application

A knowledge-based cooperative differential evolution for neural fuzzy inference systems

Methodologies and Application

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

Methodologies and Application

Large data sets classification using convex–concave hull and support vector machine

Methodologies and Application

An examination of different fitness and novelty based selection methods for the evolution of neural networks

Foundations

Weakly semi-preopen and semi-preclosed functions in L-fuzzy topological spaces

Methodologies and Application

Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

Premium Partner

    Image Credits