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A Multi-Strategy Parrot Optimization Algorithm and Its Application
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DOI：10.3390/biomimetics10030153.png)
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Abstract
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Intelligent optimization algorithms are crucial for solving complex engineering problems. The Parrot Optimization (PO) algorithm shows potential but has issues like local-optimum trapping and slow convergence. This study presents the Chaotic-Gaussian-Barycenter Parrot Optimization (CGBPO), a modified PO algorithm. CGBPO addresses these problems in three ways: using chaotic logistic mapping for random initialization to boost population diversity, applying Gaussian mutation to updated individual positions to avoid premature local-optimum convergence, and integrating a barycenter opposition-based learning strategy during iterations to expand the search space. Evaluated on the CEC2017 and CEC2022 benchmark suites against seven other algorithms, CGBPO outperforms them in convergence speed, solution accuracy, and stability. When applied to two practical engineering problems, CGBPO demonstrates superior adaptability and robustness. In an indoor visible light positioning simulation, CGBPO's estimated positions are closer to the actual ones compared to PO, with the best coverage and smallest average error.
Keywords:
parrot optimization algorithm
chaotic logistic map
gaussian mutation
barycenter opposition-based learning
industrial refrigeration systems
Himmel Blau's function
indoor visible light positioning
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