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Improved optimal foraging algorithm for global optimization

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Abstract

The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems.

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All data generated or analysed during this study are included in this article.

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Funding

This work was supported by the Intelligent Manufacturing Integrated Standardization and New Model Application Project in 2016 of MIIT, under Grant (2016) 213 and the Natural Science Foundation of Fujian Province, under Grant 2023J01256.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, People’s Republic of China

    Chen Ding

  2. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, People’s Republic of China

    GuangYu Zhu

  3. Qi Shan Campus of Fuzhou University, No.2 Xue Yuan Road, Fuzhou City, Fujian Province, People’s Republic of China

    GuangYu Zhu

Authors
  1. Chen Ding
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Contributions

All authors contributed to this study. Conceptualization, Software, Investigation, Writing - original draft, Formal analysis, Resources were performed by Chen Ding. Funding acquisition, Project administration, Writing - review and editing were performed by Guangyu Zhu. All authors read and approved the final manuscript.

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Correspondence to GuangYu Zhu.

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Ding, C., Zhu, G. Improved optimal foraging algorithm for global optimization. Computing (2024). https://doi.org/10.1007/s00607-024-01290-1

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