引用本文: 刘景森, 范文凯, 孙琳, 周欢. 求解多维复杂函数及真实世界工程设计问题的高效海洋捕食者算法[J]. 工程科学学报.
Efficient Marine Predators Algorithm for Solving Multi-Dimensional Complex Functions and Real-World Engineering Design Problems[J]. Chinese Journal of Engineering.
 Citation: Efficient Marine Predators Algorithm for Solving Multi-Dimensional Complex Functions and Real-World Engineering Design Problems[J]. Chinese Journal of Engineering.

## Efficient Marine Predators Algorithm for Solving Multi-Dimensional Complex Functions and Real-World Engineering Design Problems

• 摘要: 针对海洋捕食者算法在面对复杂函数和工程设计优化问题时存在的自适应能力有限、寻优精度有时较低、局部桎梏概率较高等缺点，提出一种新的高效自适应海洋捕食者算法。首先在海洋记忆存储阶段引入学习自动机引导的教与学搜索机制，更好地平衡算法在不同迭代时期对探索和挖掘能力的不同需求；然后在局部开发阶段，引入对数螺旋探索机制，加强算法在最优解附近的精细挖掘能力，进一步提高收敛精度；最后在算法中每次迭代末尾处加入改进的自适应相对反射策略，提升种群跳出局部最优的能力，降低局部桎梏概率。之后通过理论分析证明了本文改进算法和基本海洋捕食者算法的时间复杂度相同。为了分析和验证该改进算法的性能，将其和5种代表性算法在CEC2017测试套件上进行多维度函数极值测试，以及在5个具有挑战性的工程设计优化问题上进行测试。测试结果表明在求解多维复杂函数和工程设计优化问题时，本文改进算法的寻优精度、收敛性能和求解稳定性明显优于其它5种代表性算法，尤其在高维复杂函数下，其寻优性能的优越性更为显著。

Abstract: This paper proposes a new efficient adaptive marine predators algorithm in light of the shortcomings of the marine predators algorithm in complex functions and engineering design optimization problems, including limited adaptive ability, low optimization accuracy, and high local shackle probability. Firstly, the learning automata-guided teaching-learning search mechanism is introduced in the marine memory stage, which better balances the exploration and exploitation requirements of the algorithm in different iteration periods. Then, in the local exploitation stage, the logarithmic spiral exploration mechanism is introduced to strengthen the local fine mining capacity around the optimal solution and further raise the convergence precision of the algorithm. Finally, an improved adaptive relative reflection strategy is added at the end of each iteration to enhance the population's capacity for escaping the local optimal and decrease the probability of local shackling. After theoretical analysis, it is proven that the improved algorithm proposed in this paper has the same time complexity as the basic marine predators algorithm. To analyze and verify the improved algorithm's performance, it is tested and compared with five representative comparison algorithms on the CEC2017 test suite in multiple dimensions, as well as with five challenging engineering design problems. The test results indicate that the improved algorithm proposed in this paper outperforms the other five representative algorithms in terms of optimization precision, convergence performance, and solution stability when solving complex multi-dimensional functions and engineering design optimization problems. Particularly, the superiority of its optimization performance is even more pronounced when dealing with high-dimensional complex functions.

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