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<p>The Manta Ray Foraging Optimization (MRFO), inspired by the foraging behaviors of manta rays, has gained widespread application across various fields. However, MRFO often suffers from slow convergence and a tendency to fall into local optima. To address these issues, this paper introduces a multi-strategy enhanced manta ray foraging optimization algorithm (MLMRFO). The MLMRFO incorporates a dynamic somersault factor, a multi-leader mechanism with modified memory, and a Laplace crossover operator into the original MRFO framework. The dynamic somersault factor effectively balances exploration and exploitation during the iterative process, mitigating premature and slow convergence. Additionally, the multi-leader mechanism with improved memory and the Laplace crossover operator prevent the algorithm from falling into local optima. We used an ablation experiment to evaluate the impact of each strategy on the optimization ability of MRFO. The experimental results showed that each adopted strategy improved the performance of MRFO, with all three strategies improving MRFO most significantly. Furthermore, MLMRFO is compared with four popular optimization algorithms and two MRFO variants using the CEC2017 test suite, and four engineering optimization problems, where this paper is the first application of a variant of MRFO to the quality of service-aware manufacturing cloud service composition (QoS-MCSC) problem. The experimental results confirm the superior performance of MLMRFO in both numerical and engineering optimization problems.</p>

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A multi-strategy improved manta ray foraging optimization for engineering applications

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Kewen Wang,
  • Ting Shu,
  • Xuesong Yin,
  • Jinsong Xia

摘要

The Manta Ray Foraging Optimization (MRFO), inspired by the foraging behaviors of manta rays, has gained widespread application across various fields. However, MRFO often suffers from slow convergence and a tendency to fall into local optima. To address these issues, this paper introduces a multi-strategy enhanced manta ray foraging optimization algorithm (MLMRFO). The MLMRFO incorporates a dynamic somersault factor, a multi-leader mechanism with modified memory, and a Laplace crossover operator into the original MRFO framework. The dynamic somersault factor effectively balances exploration and exploitation during the iterative process, mitigating premature and slow convergence. Additionally, the multi-leader mechanism with improved memory and the Laplace crossover operator prevent the algorithm from falling into local optima. We used an ablation experiment to evaluate the impact of each strategy on the optimization ability of MRFO. The experimental results showed that each adopted strategy improved the performance of MRFO, with all three strategies improving MRFO most significantly. Furthermore, MLMRFO is compared with four popular optimization algorithms and two MRFO variants using the CEC2017 test suite, and four engineering optimization problems, where this paper is the first application of a variant of MRFO to the quality of service-aware manufacturing cloud service composition (QoS-MCSC) problem. The experimental results confirm the superior performance of MLMRFO in both numerical and engineering optimization problems.