Selective Opposition based Grey Wolf Optimization

Souvik Dhargupta, Manosij Ghosh, Seyedali Mirjalili, Ram Sarkar

Research output: Contribution to journalArticlepeer-review

142 Citations (Scopus)


The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area. In this work, opposition-based learning (OBL) is combined with GWO to enhance its exploratory behavior while maintaining a fast convergence rate. Spearman's correlation coefficient is used to determine the omega (ω) wolves (wolves with the lowest social status in the pack) on which to perform opposition learning. Instead of opposing all the dimensions in the wolf, a few dimensions of the wolf are selected on which opposition is applied. This assists with avoiding unnecessary exploration and achieving a fast convergence without deteriorating the probability of finding optimum solutions. The proposed algorithm is tested on 23 optimization functions. An extensive comparative study demonstrates the superiority of the proposed method. The source code for this algorithm is available at ""

Original languageEnglish
Article number113389
JournalExpert Systems with Applications
Publication statusPublished - 1 Aug 2020


  • Grey Wolf Optimizer
  • Opposition-based Learning
  • Selective opposition
  • Spearman's coefficient

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