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

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