Table 1 Comparison of metaheuristics and their fields of application.

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

Algorithms

Highlights

Limits

Area of use

PO36

Simplicity of implementation, inspired by parrot behavior

Convergence to local minimums

General optimization, reference functions

GWO46

Excellent exploration capacity and balanced operations

Risk of stagnation in multi-modal landscapes

Complex optimization, multi-objective problems

WOA47

Good convergence for complex problems

Can suffer from low diversity

Design problems and multidimensional optimization

GOA48

Inspired by gazelle dynamics, robustness in certain functions

Less effective on certain multi-objective problems

Biological and ecological applications

SCA49

Easy to implement, fast convergence for simple problems

Lack of robustness for complex landscapes

Simple problems, fast optimization

COOT50

Inspired by COOT birds, increased diversity

Variable performance for different applications

Dynamic applications, natural environment

PLO51

Inspired by polar lights, Stability in complex environments

Less flexible for dynamic environments

Energy optimization, signal analysis

CPO

Balanced exploration-exploitation, high adaptability, robustness, adjustable convergence

slow convergence for large population problems

Applications in various engineering problems and in the field of image segmentation