FPGA implementation of multi-anchor quasi-reflective learning-based artificial Protozoan optimizer for path planning

Metaheuristic optimization algorithms such as the Artificial Protozoan Optimizer (APO) show promise for complex optimization tasks, but their practical deployment in embedded scenarios is often limited by premature convergence and serial execution inefficiencies. To address these issues, this work presents a hardware-software co-design framework for APO on Field-Programmable Gate Arrays (FPGAs), employing an enhanced APO variant, namely the Multi-Anchor Quasi-Reflective Artificial Protozoan Optimizer (MAQR-APO), as a validation case. The adopted MAQR mechanism incorporates stagnation detection and multi-anchor guidance to improve search robustness while preserving a practical exploration-exploitation balance. By utilizing High-Level Synthesis (HLS), the computationally intensive optimization core is offloaded to the Programmable Logic (PL) for parallel acceleration, while the Processing System (PS) manages control flow and runtime configuration. Experimental results on the CEC2022 benchmarks and a robot path-planning application validate the proposed framework from both algorithmic and system perspectives. Under the tested settings, MAQR-APO shows competitive accuracy and stability relative to the original APO and several representative algorithms. At the system level, the proposed MAQR-APO co-design achieves runtime reductions over the corresponding pure software implementation while maintaining solution quality, and demonstrates practical deployment value through configurable testing and PS–PL collaborative execution.