Artificial protozoa optimizer: A bio-inspired metaheuristic for complex engineering optimization

This study proposes a novel bio-inspired metaheuristic algorithm, named the Artificial Protozoa Optimizer (APO), with the primary objective of solving high-dimensional and complex optimization problems more effectively than existing methods. Inspired by the adaptive foraging behavior of protozoa, APO incorporates three core mechanisms: chemotactic navigation for exploration, pseudopodial movement for exploitation, and adaptive feedback learning for trajectory refinement. To evaluate its performance, APO is tested on 20 classical benchmark functions, the IEEE CEC 2019 benchmark suite, and six real-world engineering design problems. The results reveal that APO achieves superior performance in 18 out of 20 classical benchmarks and ranks among the top three algorithms in 17 of the CEC 2019 functions. Furthermore, APO outperforms well-known algorithms such as Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) in five out of six engineering problems, demonstrating its robust- ness, fast convergence, and high solution accuracy. These findings confirm that APO is an effective and reliable optimization tool suitable for a wide range of complex real-world applications.