Improved optimization based on parrot's chaotic optimizer for solving complex problems in engineering and medical...

Area of Science:

• Computational intelligence
• Optimization algorithms
• Metaheuristics

Background:

• Metaheuristics are vital for solving complex optimization problems by balancing exploration and exploitation.
• The Parrot Optimizer (PO) enhances solution diversity but can face sub-optimal convergence.
• Addressing PO limitations is crucial for advancing optimization techniques.

Purpose of the Study:

• To enhance the Parrot Optimizer (PO) algorithm for improved performance in complex optimization.
• To introduce a novel Chaotic Parrot Optimizer (CPO) integrating chaotic maps for dynamic diversification.
• To evaluate CPO's effectiveness against benchmark functions and real-world engineering problems.

Main Methods:

• Integration of chaotic maps into the Parrot Optimizer (PO) to create the Chaotic Parrot Optimizer (CPO).
• Rigorous evaluation using 23 benchmark functions and IEEE CEC 2019/2020 benchmarks.
• Application to complex engineering problems and medical image segmentation using Kapur entropy.

Main Results:

• CPO demonstrated superior performance compared to the original PO and six other recent metaheuristics.
• The algorithm achieved faster convergence and higher solution quality across diverse optimization challenges.
• CPO successfully solved complex engineering problems and enabled precise medical image segmentation.

Conclusions:

• The Chaotic Parrot Optimizer (CPO) offers a significant advancement in metaheuristic optimization.
• CPO's dynamic diversification strategy effectively avoids local minima and enhances global optimality.
• CPO shows strong potential for both engineering applications and critical biomedical image analysis.