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<p>In this paper, a set of simple metaheuristic approaches are integrated into an ensemble optimization algorithm, called the Ensemble of Simple Optimizers (EnSO). The simple optimizers share information through a stochastic elite guidance mechanism. The proposed approach is compared with its components and other state-of-the-art metaheuristics on 49 optimization problems. The results show that the ensemble strategy improves the performance of the optimization algorithm, converting the simple and weak optimizers into a stronger one. In addition, this ensemble of simple optimizers compete well with the other more complex and advance optimization methods. In general, the results show that EnSO is a viable and efficient optimization method.</p>

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Ensemble of simple optimizers

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Mahamed G. H. Omran,
  • Ayed Salman,
  • Maurice Clerc

摘要

In this paper, a set of simple metaheuristic approaches are integrated into an ensemble optimization algorithm, called the Ensemble of Simple Optimizers (EnSO). The simple optimizers share information through a stochastic elite guidance mechanism. The proposed approach is compared with its components and other state-of-the-art metaheuristics on 49 optimization problems. The results show that the ensemble strategy improves the performance of the optimization algorithm, converting the simple and weak optimizers into a stronger one. In addition, this ensemble of simple optimizers compete well with the other more complex and advance optimization methods. In general, the results show that EnSO is a viable and efficient optimization method.