Opposition-Based Hybrid Strategy for Particle Swarm Optimization in Noisy Environments

Qi Kang, Caifei Xiong, Mengchu Zhou, Lingpeng Meng

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


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 its previous best particles 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 PSO variants for improving the latter's performance. The proposed hybrid algorithms employ probabilistic OBL for a 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 20 benchmark functions subject to different levels of noise show that the proposed hybrid PSO algorithms outperform their counterpart PSO variants as well as composite differential evolution in most cases.

Original languageEnglish (US)
Pages (from-to)21888-21900
Number of pages13
JournalIEEE Access
StatePublished - Mar 15 2018

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering


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


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