Zebra optimization algorithm incorporating opposition-based learning and dynamic elite-pooling strategies and its applications

To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. Additionally, a real-time information synchronization mechanism is incorporated into the position update process, enabling the exchange of position and state information between the optimal individual (Xbest) and the vigilante agent (Xworse). This eliminates information silos, thereby improving global search capability and convergence speed. Furthermore, a dynamic elite-pooling strategy is introduced, incorporating three distinct fitness factors. The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. During experimental evaluation, the efficiency of OP-ZOA was verified using the CEC2017 test functions, demonstrating superior performance compared to seven recently proposed meta-heuristic algorithms (Bloodsucking Leech Algorithm (BSLO), Parrot Optimization Algorithm (PO), Polar Lights Algorithm (PLO), Red-tailed Hawk Optimization Algorithm (RTH), Bitterling Fish Optimization Algorithm (BFO), Spider Wasp Optimization Algorithm (SWO) and Zebra Optimization Algorithm (ZOA)). Finally, OP-ZOA exhibits distinct advantages in optimizing the APF (artificial potential field) method to address local optimum convergence issues. Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.