Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

The paper presents a clear and logical flow, introducing the inspiration, mathematically modeling the algorithm, validating it on benchmarks, and applying it to practical problems. The connection between biological behavior and algorithm operators is well-explained.

Strengths: Detailed mathematical models are provided for the algorithm's search operators., Evaluation uses a standard, recent benchmark (CEC2022)., Multiple statistical tests (Wilcoxon, Friedman) are used for rigorous comparison., Performance is evaluated on diverse problem types (unimodal, multimodal, hybrid, composition, continuous constrained, discrete constrained)., Quantitative metrics specific to applications (SR, AFEs, ACDs, PSNR, SSIM, FSIM) are used., Exploration/exploitation balance is analyzed quantitatively using diversity metrics.
Weaknesses: Detailed methodology for parameter tuning of *all* 32 compared algorithms is not presented within the paper, relying on external references., Constraint handling using only a penalty function is a relatively basic approach.

The claims about APO's performance are strongly supported by extensive experimental results on the CEC2022 benchmark (comparing against 32 algorithms), five engineering design problems (comparing against 8 algorithms), and multilevel image segmentation (comparing against 8 algorithms). Statistical tests further reinforce these claims.

The paper proposes a genuinely novel metaheuristic algorithm inspired by the specific survival mechanisms of protozoa (Euglena), which have not been used as inspiration in this manner before. The mathematical modeling of foraging, dormancy, and reproduction for optimization is original.

The development of a new metaheuristic algorithm that demonstrates superior or highly competitive performance on standard benchmarks and practical engineering/image processing problems has potentially high significance for the optimization field and applied domains. The public availability of the code enhances its potential impact.

Strengths: The language is formal and academic., Key concepts and the algorithm's inspiration are explained clearly., Mathematical models and algorithm steps are described in detail., Experimental setup and results are presented precisely.
Areas for Improvement: Some sentences are complex or slightly awkward, but overall readability is high., Terminology like 'proportion fraction' or specific interpretations of variables (e.g., 'light intensity') could benefit from slightly more explicit connection to the optimization context in the initial explanation.