Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

Farouq Zitouni, Saad Harous

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.

Original languageEnglish
Article number80
JournalApplied System Innovation
Volume6
Issue number5
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Nelder–Mead algorithm
  • chaotic maps
  • genetic algorithms
  • global optimization
  • opposition-based learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Industrial and Manufacturing Engineering
  • Applied Mathematics
  • Artificial Intelligence

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