Night Jasmine Blooming Optimization (NJBO): A Novel Nature-Inspired Algorithm for Electrical Engineering Applications

Optimization algorithms have become vital to the solution of complex optimization problems that are difficult to solve using conventional methods. In this paper, we introduce a novel nature-inspired metaheuristic algorithm called the Night Jasmine Blooming Optimization (NJBO) algorithm, which is inspired by the blooming of the night jasmine plant (Cestrum nocturnum). The suggested algorithm combines adaptive hormonal regulation, Gibberellin-based disturbance, scent-guided exploitation, elite preservation, and Nelder–Mead local refinement, which allows us to have a balanced exploration–exploitation process. It was tested with thirteen classical benchmark functions and the CEC2017 benchmark suites and benchmarked with several well-known and recent metaheuristic methods. As a practical application, NJBO was used for identification of an IIR system and optimal design of a five-stage Cockcroft–Walton voltage multiplier circuit. Additionally, the scalability analysis, runtime evaluation, component contribution analysis, parameter sensitivity analysis, and statistical validation with the Friedman ranking test and Wilcoxon signed-rank test were performed to assess the system completely. The results show that NJBO is a very effective general-purpose optimization algorithm with competitive convergence performance, good optimization accuracy, and stability for the benchmark and engineering optimization problems that were investigated.