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Multi-threshold image segmentation algorithm based on Aquila optimization

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Abstract

Aquila Optimization (AO) is a recently proposed meta-heuristic algorithm, which has been proved to be more competitive than other meta-heuristic algorithms in function optimization and practical applications. However, when solving more complex optimization problems, AO still has the shortcomings of local optimal stagnation and low solving accuracy. To overcome these shortcomings, an improved Aquila Optimization algorithm (IAO) is proposed in this paper. During the initialization of IAO population, a hybrid chaotic mapping mechanism was introduced to initialize the population, improving both the population diversity and the uniformity of the population distribution. The elite dimensional lens imaging learning strategy is introduced for elite individual to improve the optimization quality of the algorithm as elite individual has more useful information than ordinary individuals. Then the probabilistic jump mechanism of simulated annealing algorithm is used to select the position update mode to balance local development and global search. The experimental results on the CEC2005 test function verify the viability and effectiveness of IAO. IAO is used to the multi-threshold segmentation problem based on symmetric cross entropy to demonstrate its capacity to resolve practical optimization problems. The segmentation performance on different reference images shows that IAO has good segmentation performance in most cases.

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Acknowledgements

The imagesegmentation datasets employed in this research are kindly provided by https://doi.org/10.1109/TIP.2017.2662206 for which authors express their gratitude. The authors sincerely thank the anonymous reviewers who contributed to the paper through their comments.

Funding

This study is supported by the Natural Science Foundation of China under Grant No. 62273290.

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  1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

    Hairu Guo, Jin’ge Wang & Yongli Liu

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  1. Hairu Guo
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Guo, H., Wang, J. & Liu, Y. Multi-threshold image segmentation algorithm based on Aquila optimization. Vis Comput 40, 2905–2932 (2024). https://doi.org/10.1007/s00371-023-02993-w

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