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Enhanced artificial ecosystem-based optimization for global optimization and constrained engineering problems

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Abstract

Artificial ecosystem-based optimization (AEO) is a nature-inspired intelligent optimization algorithm that has been widely applied to various real-world optimization problems. However, AEO has several limitations, including slow convergence and difficulty in escaping from local optima. To address these drawbacks, this study proposes an enhanced variant of AEO called enhanced artificial ecosystem-based optimization (EAEO). First, Latin hypercube sampling is introduced to achieve uniform population initialization. Then, a quadratic interpolation mechanism is embedded to accelerate convergence and improve accuracy. Finally, an adaptive neighborhood search inspired by animal migration behavior is designed to help to jump out of local optima. The performance of EAEO is evaluated using twenty-three benchmark functions and the CEC2017 test suite. The impact analysis, statistical analysis, and sensitivity analysis are performed. Experimental results indicate that EAEO outperforms the original AEO and other comparison algorithms in terms of accuracy and stability. Finally, the proposed EAEO is applied to address seven engineering optimization problems, and the results demonstrate the superiority of EAEO for global optimization tasks, constrained engineering problems, search performance, solution accuracy, and convergence speed.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62341303, 52265066, and 62203132, and Guizhou Provincial Science and Technology Projects under Grant No. Qiankehejichu [ZK[2022]Yiban103].

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  1. School of Electrical Engineering, Guizhou University, Guiyang, 550025, Guizhou, China

    Yunpeng Wang, Jixiang Zhang, Deguang Wang & Ming Yang

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China

    Mengjian Zhang

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Appendix A

Appendix A

See Tables 25, 26.

Table 25 Description of twenty-three benchmark functions
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Table 26 Description of the CEC2017 test suite
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Wang, Y., Zhang, J., Zhang, M. et al. Enhanced artificial ecosystem-based optimization for global optimization and constrained engineering problems. Cluster Comput 27, 10053–10092 (2024). https://doi.org/10.1007/s10586-024-04488-2

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