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<p>This paper presents a novel metaheuristic algorithm called the Seismic Exploration Optimizer (SEO), which is designed to solve global optimization problems in continuous search spaces. The SEO is inspired by the synergistic interaction between seismic sources and geophones, the attenuation and scattering of seismic waves, and the propagation characteristics of shear waves. It incorporates these physical mechanisms into the search process through mathematical modeling, thereby achieving a balance between exploration and exploitation in the search space. The performance of SEO was comprehensively evaluated across multiple dimensions using 29 benchmark functions from CEC2017, with further validation conducted on the CEC2019 benchmark functions. Based on the obtained solutions, the performance of SEO was compared with that of ten advanced metaheuristic algorithms through multiple statistical methods. The results show that SEO outperforms other comparative algorithms in reaching the global optimal solution. The results of the Wilcoxon rank-sum test and the Friedman test further show that SEO demonstrates strong competitiveness compared to other algorithms. SEO was also applied to six real-world engineering problems to demonstrate its applicability.</p>

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Seismic Exploration Optimizer: a novel meta-heuristic algorithm for solving engineering problems

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Jiwu Li,
  • Zhiyuan Li,
  • Renjie He,
  • Xiaohua Zhou,
  • Zubin Chen

摘要

This paper presents a novel metaheuristic algorithm called the Seismic Exploration Optimizer (SEO), which is designed to solve global optimization problems in continuous search spaces. The SEO is inspired by the synergistic interaction between seismic sources and geophones, the attenuation and scattering of seismic waves, and the propagation characteristics of shear waves. It incorporates these physical mechanisms into the search process through mathematical modeling, thereby achieving a balance between exploration and exploitation in the search space. The performance of SEO was comprehensively evaluated across multiple dimensions using 29 benchmark functions from CEC2017, with further validation conducted on the CEC2019 benchmark functions. Based on the obtained solutions, the performance of SEO was compared with that of ten advanced metaheuristic algorithms through multiple statistical methods. The results show that SEO outperforms other comparative algorithms in reaching the global optimal solution. The results of the Wilcoxon rank-sum test and the Friedman test further show that SEO demonstrates strong competitiveness compared to other algorithms. SEO was also applied to six real-world engineering problems to demonstrate its applicability.