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Parrot optimizer: Algorithm and applications to medical problems

PO

37 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open source codes of the proposed Parrot Optimizer (PO) is available at https://aliasgharheidari.com/PO.html

Code snippet in the archive: a link on aliasgharheidari.com (archive link, not checked and not linked: not a code host this site links to).

Papers archive 2025-07-28

30 shown of 37, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 57 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Reinforcement Learning (RL)4
Diversity2
GPU2
Management2
Portfolio Optimization2
Position2
counterfactual2
3D Reconstruction1
Benchmarking1
Causal Inference1
Clustering1
Code Generation1
Community Detection1
Computational Efficiency1
Continuous Control1
Contrastive Learning1
Counterfactual Inference1
Counterfactual Reasoning1
Data Integration1
Dataset Generation1

Usage over time archive 2025-07-28

Papers per year tagged with PO: 2023 to 2025, peak 22 22 0 2023: 3 papers 2023 2024: 22 papers 2024 2025: 12 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (37 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Stochastic Optimization

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