Harnessing dynamic turbulent dynamics in parrot optimization algorithm for complex high-dimensional engineering problems

Abstract

The Parrot Optimization Algorithm (PO) is a nature-inspired metaheuristic algorithm developed based on the social and adaptive behaviors of Pyrrhura molinae parrots. PO demonstrates robust optimization performance by balancing exploration and exploitation, mimicking foraging and cooperative activities. However, as the algorithm progresses through iterations, it faces critical challenges in maintaining search diversity and movement efficiency diminishes, leading to premature convergence and a reduced ability to find optimal solutions in complex search space. To address these limitations, this work introduces the Dynamic Turbulent-based Parrot Optimization Algorithm (DTPO), which represents a significant advancement over the original PO by incorporating three novel strategies: a novel Differential Mutation (DM), Dynamic Opposite Learning (DOL), and Turbulent Operator (TO). The DM Strategy enhances exploration by introducing controlled variations in the population, allowing DTPO to escape local optima. Also, the DOL Strategy dynamically generates opposite solutions to refresh stagnated populations, expanding the search space and maintaining adaptability. Finally, the TO strategy simulates chaotic movements inspired by turbulence, ensuring a thorough local search while preserving population diversity. Together, these strategies improve the algorithm's ability to explore, exploit, and converge efficiently. Furthermore, the DTPO's performance was rigorously evaluated on benchmark functions from CEC2017 and CEC2022, comparing it against 23 state-of-the-art algorithms. The results demonstrate DTPO's superior convergence speed, search efficiency, and optimization accuracy. Additionally, DTPO was tested on seven engineering design problems, achieving significant improvements over the original PO algorithm, with superior performance gains compared to other algorithms in real-world scenarios. Particularly, DTPO outperformed competing algorithms in 37 out of 41 benchmark functions, achieving an overall success rate of 90.24%. Moreover, DTPO obtained the best Friedman ranks across all comparisons, with values ranging from 3.03 to 1.18, demonstrating its superiority over classical, advanced, and recent algorithms. These results validate the proposed enhancements and highlight DTPO's robustness and effectiveness in solving complex optimization problems.