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.
LIU Jingsen, FAN Wenkai, SUN Lin, ZHOU Huan. Efficient marine predators algorithm for solving multidimensional complex functions and real-world engineering design problems[J]. Chinese Journal of Engineering.
 Citation: LIU Jingsen, FAN Wenkai, SUN Lin, ZHOU Huan. Efficient marine predators algorithm for solving multidimensional complex functions and real-world engineering design problems[J]. Chinese Journal of Engineering.

## Efficient marine predators algorithm for solving multidimensional 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. 为了分析和验证该改进算法的性能，将其和6种代表性算法在进化计算大会（CEC）2017测试套件上进行100维的函数极值测试，并在4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他6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: As optimization problems grow increasingly complex, characterized by their intricate difficulty, larger-scale, and diverse constraints, swarm intelligence optimization algorithms have emerged as an effective solution for addressing these multifaceted challenges. Among these, the marine predators algorithm, a recent innovation in intelligent optimization algorithms, has demonstrated remarkable efficacy in solving optimization issues. However, its application to complex CEC test function sets and engineering constraint problems reveals several limitations, including limited adaptive ability, low optimization accuracy, and high local shackle probability. This paper proposes an enhanced version of the marine predators algorithm designed to overcome its inherent shortcomings. The enhancement begins with the integration of a learning automata guided teaching–learning search mechanism during the marine memory stage. This adjustment aims to strike a better balance between exploration and exploitation across different iteration periods. Subsequently, the introduction of a logarithmic spiral exploration mechanism phase strengthens the algorithm’s ability to conduct nuanced searchers around the optimal solution, thereby improving convergence accuracy. Finally, an improved adaptive relative reflection strategy is added at the end of each iteration to enhance the algorithm’s capability to escape local optima and reduce the risk of local shackling. The optimization performance of this refined algorithm is evaluated through parameter sensitivity analysis, determining the optimal parameter values. To validate its effectiveness, the improved algorithm undergoes testing against six benchmark algorithms, including the basic marine predators algorithm and its variants, as well as other improved algorithms and those recognized with awards in the CEC2017 test suite across 100 dimensions. The evaluation focuses on optimization accuracy, the Wilcoxon rank sum test, and boxplot analysis. The test results indicate that the improved algorithm proposed in this paper outperforms the other six benchmark algorithms in optimization precision, convergence rate, and solution stability, particularly when solving complex functions in high-dimensional (100 dimensions) spaces. Furthermore, the applicability and superior performance of the improved algorithm are demonstrated through comparative analysis with four established algorithms on challenging engineering design optimization problems. These include welded beam design, process synthesis, heat exchanger network design, and design optimization of industrial refrigeration systems. The findings unequivocally showcase the enhanced algorithm’s exceptional ability to solve various engineering constraint problems effectively.

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