Improved optimization based on parrot’s chaotic optimizer for solving complex problems in engineering and medical image segmentation

Metaheuristics, which are general-purpose algorithms, are commonly used to solve complex optimization problems. These algorithms manipulate multiple potential solutions to converge on the optimum, balancing the exploration and exploitation phases. A recent algorithm, the Parrot Optimizer (PO), is inspired by the behavior of domestic parrots to improve the diversity of solutions. However, while promising, the PO may encounter difficulties such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to the PO algorithm by integrating chaotic maps to solve complex optimization problems. The improved algorithm, called Chaotic Parrot Optimizer (CPO), is characterized by a better ability to avoid local minima and reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. The effectiveness of the CPO algorithm has been rigorously evaluated through in-depth statistical analysis, using 23 benchmark functions as well as IEEE CEC 2019 and CEC 2020 benchmarks, covering a wide range of optimization challenges. The results show that CPO outperforms not only the original PO algorithm, but also six recent metaheuristics in terms of convergence speed and solution quality. In addition, it has been successfully applied to three complex engineering illustrating its ability to solve real-world, multi-constraint problems. Its integration with Kapur entropy also enabled precise segmentation of medical images, underlining its strong potential for critical biomedical applications. The CPO source code will be available on the Github account: [email protected] after acceptance.