Parrot optimizer: Algorithm and applications to medical problems

Abstract: 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. .

Parrot Optimizer (PO): The Parrot Optimizer (PO) algorithm is inspired by the behaviors of the *Pyrrhura Molinae* parrot, integrating foraging, staying, communicating, and fear of strangers into its optimization strategy. This approach effectively balances exploration and exploitation to solve complex optimization problems. Below is an overview of the core components and mechanisms of PO.

Conceptual Foundation: PO emulates four key behaviors of the *Pyrrhura Molinae*—foraging for food, staying on different parts of the owner's body, communicating with the flock, and avoiding strangers. These behaviors guide the algorithm's search strategy to balance global exploration and local refinement.

Algorithm Structure: PO operates through the following behaviors:

Foraging Behavior: Emulates the search for food, using both local and global information to improve solution quality.

Staying Behavior: Focuses on refining solutions locally, akin to perching behavior observed in parrots.

Communicating Behavior: Enhances convergence by simulating interaction within the flock and adjusting positions accordingly.

Fear of Strangers Behavior: Helps the algorithm avoid local optima and move towards safer areas, improving global search capabilities.

Summary of PO Behaviors: PO integrates:

The Parrot Optimizer, inspired by *Pyrrhura Molinae* behaviors, balances global exploration with local exploitation. It shows significant potential for solving complex optimization problems, including those in medical fields.