Research on a multi-strategy enhanced parrot optimization algorithm STPO for Complex optimization problems and its applications

Abstract

Abstract Metaheuristic optimization algorithms are widely used to tackle complex, high-dimensional, and nonlinear problems by mimicking natural or social behaviors, showing great potential for future development. Among them, the Parrot Optimization (PO) algorithm, inspired by the green-cheeked conure, exhibits strong adaptability. However, in high-dimensional scenarios, it often converges slowly and is prone to getting trapped in local optima. To address these limitations, this study proposes a multi-strategy enhanced parrot optimizer, termed STPO. STPO integrates an alert protection mechanism inspired by the Sparrow Search Algorithm, an experience exchange strategy, and a worst-guided differential scale perturbation operator to improve population guidance, strengthen perturbation-based search, facilitate the transition from exploration to exploitation, and enhance convergence stability. Comprehensive experiments on the CEC2017 and CEC2022 benchmark suites demonstrate that STPO achieves highly competitive average rankings. Specifically, on CEC2017, STPO obtains average ranks of 1.76, 1.21, 1.21, and 1.46 under 10-, 30-, 50-, and 100-dimensional settings, respectively. On CEC2022, STPO achieves average ranks of 2.15 and 1.74 under 10- and 20-dimensional settings, respectively. These quantitative results indicate that STPO provides stable and accurate optimization performance compared with the twelve competing algorithms. Furthermore, statistical tests, ablation experiments, population diversity analysis, and exploration–exploitation analysis are conducted to further examine the effectiveness and dynamic search behavior of STPO. When applied to five classical engineering design problems and mobile robot path planning tasks, STPO also achieves competitive solution accuracy and convergence behavior, further confirming its applicability to constrained engineering optimization and practical path planning scenarios. The Source code for this work is openly available at https://github.com/MingXuanJian/STPO.git .