Discrete equilibrium optimizer combined with simulated annealing for feature selection

Ritam Guha, Kushal Kanti Ghosh, Suman Kumar Bera, Ram Sarkar, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper proposes a binary adaptation of the recently proposed meta-heuristic, Equilibrium Optimizer (EO), called Discrete EO (DEO), to solve binary optimization problems. A U-shaped transfer function is used to map the continuous values of EO into the binary domain. To further improve the exploitation capability of DEO, Simulated Annealing (SA) is used as a local search procedure and the combination is named as DEOSA. The proposed DEOSA algorithm is applied to 18 well-known UCI datasets and compared with a wide range of algorithms. The results are statistically validated using Wilcoxon rank-sum test and Friedman test. In order to test the scalability and robustness of DEOSA, it is additionally tested over seven high-dimensional Microarray datasets and 25 binary Knapsack problems. The results evidently demonstrate the superiority and merits of DEOSA when solving binary optimization problems.

Original languageEnglish
Article number101942
JournalJournal of Computational Science
Publication statusPublished - Mar 2023


  • Algorithm
  • Equilibrium optimizer
  • Feature selection
  • Knapsack problem
  • Microarray dataset
  • Optimization
  • Simulated annealing
  • UCI dataset


Dive into the research topics of 'Discrete equilibrium optimizer combined with simulated annealing for feature selection'. Together they form a unique fingerprint.

Cite this