A learning automata-based particle swarm optimization algorithm for noisy environment

Junqi Zhang, Linwei Xu, Ji Ma, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Particle Swarm Optimization (PSO) is an outstanding evolutionary algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly in noisy environments. Some studies have addressed this issue by introducing a resampling method. Most existing methods allocate a fixed and predetermined budget of re-evaluations for every iteration, but cannot change the budget according to different environments adaptively. Our previous work proposed a PSO-LA to integrate PSO with a Learning Automaton (LA) variant. PSO-LA utilizes LA's flexible self-adaption and automatic learning capability to learn the budget allocation for each iteration. This work further improves PSO-LA by the introduction of a subset scheme based LA (subLA) into PSO to further increase the probability of correctly finding the best particle through the pursuit on the a subset of particles with better performance, yielding a new method called LAPSO. LAPSO does not record the historical global best solution but finds it from the subset learned by subLA to jump out of the trapped area that may have a false global best solution. It can also adaptively consume computing budgets for every particle per iteration and, accordingly, total iteration times. Through experiments on 20 large-scale benchmark functions subject to different levels of noise, this work convincingly shows that LAPSO outperforms the existing ones in both accuracy and convergence rate of the optimization problems in noisy environments.

Original languageEnglish (US)
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages141-147
Number of pages7
ISBN (Electronic)9781479974924
DOIs
StatePublished - Sep 10 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: May 25 2015May 28 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
CountryJapan
CitySendai
Period5/25/155/28/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Mathematics

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