Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

01.09.2015 | Methodologies and Application | Ausgabe 9/2015

Soft Computing 9/2015

Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problems

Zeitschrift:
Soft Computing > Ausgabe 9/2015
Autoren:
Juan Rada-Vilela, Mark Johnston, Mengjie Zhang
Wichtige Hinweise
Communicated by V. Loia.

Abstract

Particle swarm optimization (PSO) is a metaheuristic whose quality of results deteriorates significantly in optimization problems subject to noise. The underlying reason to such a deterioration is that the effect of noise hinders the ability of particles to distinguish good from bad solutions, leading them to suffer from deception, blindness and disorientation. A deceived particle is not partially attracted to the true best solution in its neighborhood, a blinded particle misses an opportunity to improve upon its personal best solution, and a disoriented particle mistakenly prefers a worse solution. These conditions need to be addressed via noise mitigation mechanisms to prevent (or at least reduce) such a deterioration. Single-evaluation methods are the name by which we refer to PSO algorithms that address the effect of noise without performing additional function evaluations. The first of these algorithms was PSO with evaporation (PSO-E), which was proposed to reduce blindness in the swarms, and reports have suggested that it succeeds at finding better solutions than the regular PSO in different stochastic and dynamic optimization problems. However, PSO-E depends on an evaporation factor whose value is determined empirically, and the swarm is always at risk of exhibiting divergent behaviour. In this article, we propose a method to determine a priori the evaporation factor for PSO-E, and we also propose a new PSO with probabilistic updates (PSO-PU) to prevent the risk of divergence. Additionally, we take a different approach and develop a new PSO with average neighborhoods (PSO-AN) to blur the effect of noise and thereby reduce deception. Experiments on 20 large-scale benchmark functions subject to different levels of noise show that the regular PSO (lacking a noise mitigation mechanism) generally finds better solutions than PSO-E and PSO-PU because their approaches cause too much disorientation. However, PSO-AN finds better solutions than the regular PSO thanks to the improved quality of its neighborhood best solutions that partially attract the swarm towards better regions of the search space.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

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

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

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

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

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




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

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




Testen Sie jetzt 30 Tage kostenlos.

Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 9/2015

Soft Computing 9/2015 Zur Ausgabe

Premium Partner

    Bildnachweise