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

Xiaopeng Wang, Václav Snásel, Seyedali Mirjalili, Jeng-Shyang Pan, Lingping Kong, Hisham A. Shehadeh

Abstract: Highlights•A new bio-inspired artificial protozoa optimizer (APO) is designed to model the survival behavior of protozoa.•The APO algorithm mimics foraging, dormancy, and reproduction. Autotrophic foraging and dormancy contribute to exploration, however heterotrophic foraging and reproduction contribute to exploitation.•The APO is implemented and evaluated under the CEC2022 benchmark. The experimental results verified that the APO is superior to 32 state-of-the-art algorithms.•The effectiveness of APO is tested by challenging real-world problems, including five engineering designs and a multilevel image segmentation task.