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

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimizatio...

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Veröffentlicht in:Knowledge-based systems Jg. 295; S. 111737
Hauptverfasser: Wang, Xiaopeng, Snášel, Václav, Mirjalili, Seyedali, Pan, Jeng-Shyang, Kong, Lingping, Shehadeh, Hisham A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 08.07.2024
Schlagworte:
Artificial protozoa optimizer
Constrained optimization
Engineering design
Image segmentation
Metaheuristic algorithm
Engineering design
Image segmentation
Artificial protozoa optimizer
Metaheuristic algorithm
Constrained optimization
ISSN:0950-7051, 1872-7409
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Zusammenfassung:This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer. •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.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2024.111737