Scholarlink

<p>This paper proposes a new single-solution optimization algorithm called Metaheuristic Exponential Search Optimization with Covariance Matrix Adaptation (MES-CM). The method was designed as a non-metaphor-based metaheuristic that uses an exponential pseudo-random sampling mechanism to generate search steps over multiple distance scales. This mechanism enables both short-range refinements and longer exploratory moves without relying on a population of candidate solutions. To improve directional search, MES-CM incorporates a covariance-guided transformation inspired by covariance matrix adaptation. In the proposed framework, however, the covariance information is estimated intermittently from previously accepted solutions and used within a single-solution search model. Therefore, the main contribution of MES-CM lies in the integration of exponential multi-scale sampling, accepted-solution-based covariance estimation, and a compact single-solution optimization structure. The performance of MES-CM was evaluated on the CEC 2017 and CEC 2022 benchmark suites, as well as on four constrained engineering design problems. The experimental study includes accuracy analysis, empirical convergence analysis, running time comparison, and non-parametric statistical tests based on the Wilcoxon signed-rank test and the Friedman mean rank test. The results show that MES-CM achieves highly competitive performance compared with recent metaheuristic algorithms from 2022 to 2026. On the CEC 2017 benchmark, MES-CM obtained the best mean objective value for 24 out of 30 functions and achieved the best overall Friedman mean rank. On the CEC 2022 benchmark, MES-CM achieved the best mean objective value for 7 out of 12 functions and again obtained the best overall Friedman ranking. The engineering design results further confirm that the proposed method can obtain high-quality solutions for constrained practical problems. The source code of MES-CM is publicly available at: <a href="https://github.com/grzegorzbies/MES-CM">https://github.com/grzegorzbies/MES-CM</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Metaheuristic Exponential Search Optimization with Covariance Matrix Adaptation (MES-CM)

  • 期刊论文

  • 发表日期：

    2026年8月4日

查看全文
  • Grzegorz Bieś,
  • Ernest Bieś

摘要

This paper proposes a new single-solution optimization algorithm called Metaheuristic Exponential Search Optimization with Covariance Matrix Adaptation (MES-CM). The method was designed as a non-metaphor-based metaheuristic that uses an exponential pseudo-random sampling mechanism to generate search steps over multiple distance scales. This mechanism enables both short-range refinements and longer exploratory moves without relying on a population of candidate solutions. To improve directional search, MES-CM incorporates a covariance-guided transformation inspired by covariance matrix adaptation. In the proposed framework, however, the covariance information is estimated intermittently from previously accepted solutions and used within a single-solution search model. Therefore, the main contribution of MES-CM lies in the integration of exponential multi-scale sampling, accepted-solution-based covariance estimation, and a compact single-solution optimization structure. The performance of MES-CM was evaluated on the CEC 2017 and CEC 2022 benchmark suites, as well as on four constrained engineering design problems. The experimental study includes accuracy analysis, empirical convergence analysis, running time comparison, and non-parametric statistical tests based on the Wilcoxon signed-rank test and the Friedman mean rank test. The results show that MES-CM achieves highly competitive performance compared with recent metaheuristic algorithms from 2022 to 2026. On the CEC 2017 benchmark, MES-CM obtained the best mean objective value for 24 out of 30 functions and achieved the best overall Friedman mean rank. On the CEC 2022 benchmark, MES-CM achieved the best mean objective value for 7 out of 12 functions and again obtained the best overall Friedman ranking. The engineering design results further confirm that the proposed method can obtain high-quality solutions for constrained practical problems. The source code of MES-CM is publicly available at: https://github.com/grzegorzbies/MES-CM.