The Manta ray foraging optimization (MRFO) is a novel swarm-based metaheuristic optimizer. It is mainly modeled by simulating three foraging behaviors of the Manta rays, which has a good performance. However, several drawbacks of MRFO have been noticed by analyzing its mathematical model. Random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Chain foraging tends to lead the algorithm into local optimum. In addition, the algorithm suffers from the deficiency of decreasing population diversity in the late iteration. To address these shortcomings, a modified MRFO using three strategies, called m-MRFO, is proposed in this paper. An elite search pool (ESP) is established in this paper to enhance exploitation capability. By using adaptive control parameter strategies (ACP), we expand the range of MRFO’s exploration in the early iterations and enhance the accuracy of exploitation in the later iterations, balancing exploiting and exploring capabilities. Furthermore, we use a distribution estimation strategy (DES) to adjust the evolutionary direction using the dominant population information to promote convergence. The m-MRFO performance was verified by selecting 23 classical test functions and CEC2017 test suite. The significance of the results was also verified by Friedman test, Wilcoxon test and Iman-Davenport test. Moreover, we have confirmed the potential of m-MRFO to solve real-world problems by solving three engineering design problems. The simulation results show that the improvement strategy proposed in this paper can effectively improve the performance of MRFO. m-MRFO is highly competitive.

中文翻译：

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全局优化问题的修正蝠鲼觅食优化

蝠鲼觅食优化（MRFO）是一种新颖的基于群的元启发式优化器。它主要通过模拟蝠鲼的三种觅食行为进行建模，具有良好的性能。然而，通过分析其数学模型，已经注意到MRFO的几个缺点。在早期迭代中随机选择参考点削弱了 MRFO 的开发能力。链觅食往往会导致算法进入局部最优。此外，该算法存在迭代后期种群多样性下降的不足。为了解决这些缺点，本文提出了一种使用三种策略的改进 MRFO，称为 m-MRFO。为了提高开发能力，本文建立了精英搜索池（ESP）。通过使用自适应控制参数策略 (ACP)，我们在早期迭代中扩大了MRFO的探索范围，并在后期迭代中提高了开发的准确性，平衡了开发和探索能力。此外，我们使用分布估计策略（DES）利用优势种群信息调整进化方向以促进收敛。通过选择 23 个经典测试函数和 CEC2017 测试套件验证了 m-MRFO 性能。弗里德曼检验、威尔科克森检验和伊曼-达文波特检验也验证了结果的显着性。此外，我们已经通过解决三个工程设计问题证实了 m-MRFO 解决现实世界问题的潜力。仿真结果表明，本文提出的改进策略能够有效提高MRFO的性能。m-MRFO 具有很强的竞争力。