An opposition-based particle swarm optimization algorithm for noisy environments

Mengchu Zhou, Zeyu Zhao, Caifei Xiong, Qi Kang

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

4 Scopus citations

Abstract

Particle Swarm Optimization (PSO) is a population-based algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly when optimization problems are subjected to noise. PSO is strongly influenced by each particle's own previous best one and global best one, which may lead to premature convergence and fall into local optima. This also holds true for various PSO variants dealing with optimization problems in noisy environments. Opposition-based learning (OBL) is well-known for its ability to increase population diversity. In this paper, we propose hybrid PSO algorithms that introduce OBL into a PSO variant for improving the latter's performance. The proposed hybrid algorithm employs probabilistic OBL for particle swarm. In contrast to other integrations of PSO and OBL, we select the top fittest particles from the current swarm and its opposite swarm to improve the entire swarm's fitness. Experiments on 10 benchmark functions subject to different levels of noise show that the proposed algorithm has better performance in most cases.

Original languageEnglish (US)
Title of host publicationICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538650530
DOIs
StatePublished - May 18 2018
Event15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018 - Zhuhai, China
Duration: Mar 27 2018Mar 29 2018

Publication series

NameICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control

Other

Other15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
Country/TerritoryChina
CityZhuhai
Period3/27/183/29/18

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Optimization
  • Modeling and Simulation

Keywords

  • Particle swarm optimization
  • hybrid algorithm
  • noisy environments
  • opposition-based learning
  • optimization

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