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<p>Quadratic Interpolation Optimization (QIO) is an innovative meta-heuristic algorithm—more competitive than other meta-heuristic algorithms in numerical optimization and engineering issues. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the QIO remains an open problem. To address these issues, a hyper-heuristic algorithm called Differential Cauchy Vibrational Quadratic Interpolation Optimization (DCVQIO) is proposed by adding three strategies to improve QIO’s performance: Cauchy inverse cumulative distribution function, periodic mutation, and differential evolution (DE) algorithm. First, the Cauchy inverse cumulative distribution function is introduced into the exploration stage of the QIO algorithm to form a Cauchy Quadratic Interpolation Optimization (CQIO) algorithm, enhancing the search mechanism of QIO while retaining its strong exploration ability. Next, periodic mutation is used as a strategy for the first time in the hyper-heuristic algorithm. A periodic mutation strategy is placed between the DE algorithm and the improved QIO algorithm, aimed to increase the diversity of DCVQIO and prevent the algorithm from falling into local optima. Lastly, a hybrid strategy is proposed to organically integrate DE, CQIO, and periodic mutation strategy to obtain an efficient hybrid algorithm for DE and QIO. DCVQIO can provide enhanced advantages of three strategies, as well as QIO, overcoming the shortcomings of DE and QIO and maximizing the performance. Experimentations on 29 CEC2017 test functions reveal that DCVQIO has better optimization performance and stronger universality (compared with the other 21 state-of-the-art algorithms), whilst the results on three practical engineering design problems demonstrate that DCVQIO has significant advantages over similar algorithms.</p>

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Hybrid hyper-heuristic algorithms for global optimization in engineering designs

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Fang Feng,
  • Kuan-Ching Li,
  • Mingjiang Cai,
  • Mingsen Deng,
  • Subhash Bhalla

摘要

Quadratic Interpolation Optimization (QIO) is an innovative meta-heuristic algorithm—more competitive than other meta-heuristic algorithms in numerical optimization and engineering issues. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the QIO remains an open problem. To address these issues, a hyper-heuristic algorithm called Differential Cauchy Vibrational Quadratic Interpolation Optimization (DCVQIO) is proposed by adding three strategies to improve QIO’s performance: Cauchy inverse cumulative distribution function, periodic mutation, and differential evolution (DE) algorithm. First, the Cauchy inverse cumulative distribution function is introduced into the exploration stage of the QIO algorithm to form a Cauchy Quadratic Interpolation Optimization (CQIO) algorithm, enhancing the search mechanism of QIO while retaining its strong exploration ability. Next, periodic mutation is used as a strategy for the first time in the hyper-heuristic algorithm. A periodic mutation strategy is placed between the DE algorithm and the improved QIO algorithm, aimed to increase the diversity of DCVQIO and prevent the algorithm from falling into local optima. Lastly, a hybrid strategy is proposed to organically integrate DE, CQIO, and periodic mutation strategy to obtain an efficient hybrid algorithm for DE and QIO. DCVQIO can provide enhanced advantages of three strategies, as well as QIO, overcoming the shortcomings of DE and QIO and maximizing the performance. Experimentations on 29 CEC2017 test functions reveal that DCVQIO has better optimization performance and stronger universality (compared with the other 21 state-of-the-art algorithms), whilst the results on three practical engineering design problems demonstrate that DCVQIO has significant advantages over similar algorithms.