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 1/2019

16.05.2017 | Original Article

A best firework updating information guided adaptive fireworks algorithm

verfasst von: Haitong Zhao, Changsheng Zhang, Jiaxu Ning

Erschienen in: Neural Computing and Applications | Ausgabe 1/2019

Einloggen

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

search-config
loading …

Abstract

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) has significant performance on solving single objective problems, and has been applied broadly on a number of fields. To further improve its performance, a best firework updating information guided adaptive fireworks algorithm (PgAFWA) is proposed, in which the evolving process is guided by the direction from previous best firework to the current best firework from two aspects: amplifying the explosion amplitude on the direction that the best firework is updated, and making more sparks which are generated by the best firework distributed on this direction to further enhance the exploring ability on it. Numerical experiment on CEC2015 test suite was implemented to verify performance of the proposed algorithm. The experiment results indicated that the PgAFWA outperformed the compared algorithms in terms of both convergence speed and solving quality.
Vorheriger Artikel On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays
Nächster Artikel Heuristic computational intelligence approach to solve nonlinear multiple singularity problem of sixth Painlev́e equation

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.
Beni G, Wang J. Swarm Intelligence in Cellular Robotic Systems[M]// Robots and Biological Systems: Towards a New Bionics. 1993:703–712.
2.
Kennedy J, Eberhart R (1995) Particle swarm optimization[C]// IEEE International Conference on Neural Networks, 1995. Proceedings. IEEE, 4:1942–1948
3.
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRef
4.
Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):24–32CrossRef
5.
Tan Y, Zhu Y (2010) Fireworks algorithm for optimization[C]// International conference on advances in swarm intelligence. Springer-Verlag:355–364
6.
Li J, Zheng S, Tan Y (2016) The effect of information utilization: 1105 introducing a novel guiding spark in the fireworks algorithm. IEEE 1106 Trans Evol Comput:1–1
7.
Zheng S, Janecek A, Tan Y. Enhanced Fireworks Algorithm[C]// Evolutionary Computation. 2013:2069–2077.
8.
Zhang B, Zhang MX, Zheng YJ (2014) A hybrid biogeography- 1108 based optimization and fireworks algorithm[C]// evolutionary com- 1109 putation. IEEE:3200–3206
9.
Yu C, Li J, Tan Y (2014) Improve enhanced fireworks algorithm with differential mutation[C]// IEEE International Conference on Systems. Man and Cybernetics, IEEE
10.
Gao H, Diao M (2011) Cultural firework algorithm and its application for digital filters design. International Journal of Modelling Identification & Control 14(4):324–331CrossRef
11.
Zheng YJ, Xu XL, Ling HF et al (2015) A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148(148):75–82CrossRef
12.
Yu C, Tan Y. Fireworks algorithm with covariance mutation[C]// evolutionary computation. IEEE, 2015.
13.
Yu C, Kelley LC, Tan Y, Dynamic search fireworks algorithm with covariance mutation for solving the CEC (2015) Learning based competition problems[C]//2015 I.E. congress on evolutionary computation (CEC). IEEE 2015:1106–1112
14.
Li J, Tan Y. Orienting mutation based fireworks algorithm[C]// Evolutionary Computation. IEEE, 2015.
15.
Zheng S, Li J, Janecek A et al (2015) A cooperative framework for 1072 fireworks algorithm[J]. IEEE/ACMTransactions on Computational 1073 Biology & Bioinformatics:1–1
16.
Zhang B, Zheng Y-J et al (2015) Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM Transactions on Computational Biology & Bioinformatics:1–1
17.
Zheng S, Janecek A, Li J et al (2014) Dynamic search in fireworks algorithm[C]// evolutionary computation. IEEE:3222–3229
18.
Li J, Zheng S, Tan Y (2014) Adaptive fireworks algorithm[C]// evolutionary computation. IEEE:3214–3221
19.
Si T, Ghosh R (2015) Explosion sparks generation using adaptive transfer function in firework algorithm[C]// IEEE third International conference on signal processing. Communications and NETWORKING:305–314
20.
Chen J, Yang Q, Ni J et al (2015) An improved fireworks algorithm with landscape information for balancing exploration and exploitation[C]//2015 I.E. congress on evolutionary computation (CEC). IEEE:1272–1279
21.
Zheng S, Yu C, Li J, et al. Exponentially decreased dimension number strategy based dynamic search fireworks algorithm for solving CEC2015 competition problems[C]// evolutionary computation. IEEE, 2015.
22.
Ding K, Zheng S, Tan Y. A gpu-based parallel fireworks algorithm for optimization[C]//Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013: 9–16.
23.
Liang J J, Qu B Y, Suganthan P N, et al. Problem definitions and 1099 evaluation criteria for the CEC 2015 competition on learning-based 1100 real-parameter single objective optimization[J]. Technical 1101 Report201411A, Computational Intelligence Laboratory, 1102 Zhengzhou University, Zhengzhou China and Technical Report, 1103 Nanyang Technological University, Singapore, 2014.
24.
Nowak K, Märtens M, Izzo D (2014) Empirical performance of the approximation of the least hypervolume contributor[M]//parallel problem solving from nature–PPSN XIII. Springer International Publishing:662–671
Metadaten
Titel
A best firework updating information guided adaptive fireworks algorithm
verfasst von
Haitong Zhao
Changsheng Zhang
Jiaxu Ning
Publikationsdatum
16.05.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2981-0

Weitere Artikel der Ausgabe 1/2019

Neural Computing and Applications 1/2019 Zur Ausgabe

Original Article

On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays

Original Article

Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression

Original Article

An intelligent approach to predict gas compressibility factor using neural network model

Original Article

Epileptic seizure detection using hybrid machine learning methods

Original Article

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Original Article

Heuristic computational intelligence approach to solve nonlinear multiple singularity problem of sixth Painlev́e equation