To enhance the convergence efficiency and solution precision of the Red-billed Blue Magpie Optimizer (RBMO), this study proposes a Multi-Strategy Enhanced Red-billed Blue Magpie Optimizer (MRBMO). The principal methodological innovations encompass three aspects: (1) Development of a novel dynamic boundary constraint handling mechanism that strengthens algorithmic exploration capabilities through adaptive regression strategy adjustment for boundary-transgressing particles; (2) Incorporation of an elite guidance strategy during the predation phase, establishing a guided search framework that integrates historical individual optimal information while employing a Lévy Flight strategy to modulate search step sizes, thereby achieving effective balance between global exploration and local exploitation capabilities; (3) Comprehensive experimental evaluations conducted on the CEC2017 and CEC2022 benchmark test suites demonstrate that MRBMO significantly outperforms classical enhanced algorithms and exhibits competitive performance against state-of-the-art optimizers across 41 standardized test functions. The practical efficacy of the algorithm is further validated through successful applications to four classical engineering design problems, confirming its robust problem-solving capabilities.

The Kepler optimization algorithm (KOA) is a metaheuristic algorithm based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets and for various optimization issues. However, the KOA is hampered by the limitations of insufficient convergence accuracy, weak global search ability, and slow convergence speed. To address these deficiencies, this paper presents a multi-strategy fusion Kepler optimization algorithm (MKOA). Firstly, the algorithm initializes the population using Good Point Set, enhancing population diversity. Secondly, Dynamic Opposition-Based Learning is applied for population individuals to further improve its global exploration effectiveness. Furthermore, we introduce the Normal Cloud Model to perturb the best solution, improving its convergence rate and accuracy. Finally, a new position-update strategy is introduced to balance local and global search, helping KOA escape local optima. To test the performance of the MKOA, we uses the CEC2017 and CEC2019 test suites for testing. The data indicate that the MKOA has more advantages than other algorithms in terms of practicality and effectiveness. Aiming at the engineering issue, this study selected three classic engineering cases. The results reveal that the MKOA demonstrates strong applicability in engineering practice.