A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems

Betül Sultan Ylldlz, Pranav Mehta, Natee Panagant, Seyedali Mirjalili, Ali Riza Yildiz

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN's convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.

Original languageEnglish
Article numberqwac113
Pages (from-to)2452-2465
Number of pages14
JournalJournal of Computational Design and Engineering
Issue number6
Publication statusPublished - 1 Dec 2022


  • brake pedal
  • chaotic maps
  • hybrid metaheuristics
  • mechanical design
  • Runge Kutta optimization algorithm


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