Abstract
In order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporated into the improved framework: triangular walk operators to balance localized exploitation and global exploration across optimization phases; Levy flight mechanisms to strengthen solution space traversal capabilities; and lens imaging inversion learning to improve population diversity and avoid local convergence stagnation. The enhanced solution accuracy of the MZOA over modern metaheuristics is empirically validated using the CEC2005 and CEC2017 benchmark suites. The proposed MZOA's performance improved by 15.8% compared to the basic ZOA The algorithm's practical effectiveness across a range of environmental difficulties is confirmed by extensive assessment in engineering optimization and robotic route planning scenarios. It routinely achieves optimal solutions in both simple and complicated setups. In robot path planning, the proposed MZOA reduces the movement path by 8.7% compared to the basic ZOA. These comprehensive evaluations establish the MZOA as a robust computational algorithm for complex optimization challenges, demonstrating enhanced convergence characteristics and operational reliability in synthetic and real-world applications.