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<p>Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it has been developed to solve global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization and engineering design problems. However, it suffers from getting stuck in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for benchmark problem suits with 30 and 50 dimensions. According to Friedman Test statistics, the QSHO algorithm ranked first and second in solving CEC2013 30D and 50D, respectively, whereas it ranked first in both solving CEC2017 30D and 50D. In addition, we have assessed the QSHO in four real-world engineering optimization problems, and the QSHO statistically outperforms the competitive algorithms.</p>

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QSHO: Quantum spotted hyena optimizer for global optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月6日

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  • Tapas Si,
  • Péricles B. C. Miranda,
  • Utpal Nandi,
  • Nanda Dulal Jana,
  • Ujjwal Maulik,
  • Saurav Mallik,
  • Mohd Asif Shah

摘要

Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it has been developed to solve global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization and engineering design problems. However, it suffers from getting stuck in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for benchmark problem suits with 30 and 50 dimensions. According to Friedman Test statistics, the QSHO algorithm ranked first and second in solving CEC2013 30D and 50D, respectively, whereas it ranked first in both solving CEC2017 30D and 50D. In addition, we have assessed the QSHO in four real-world engineering optimization problems, and the QSHO statistically outperforms the competitive algorithms.