mESC：一种融合多种策略的工程优化增强逃逸算法。

mESC: An Enhanced Escape Algorithm Fusing Multiple Strategies for Engineering Optimization.

作者信息

Liu Jia, Yang Jianwei, Cui Lele

机构信息

Faculty of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723000, China.

Faculty of Art and Design, Xi'an University of Technology, Xi'an 710054, China.

出版信息

Biomimetics (Basel). 2025 Apr 8;10(4):232. doi: 10.3390/biomimetics10040232.

DOI:10.3390/biomimetics10040232

PMID:40277631

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12025011/

Abstract

A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (ESC). Firstly, an adaptive perturbation factor strategy was employed to maintain population diversity. Secondly, introducing a restart mechanism to enhance the exploration capability of mESC. Finally, a dynamic centroid reverse learning strategy was designed to balance local development. In addition, in order to accelerate the global convergence speed, a boundary adjustment strategy based on the elite pool is proposed, which selects elite individuals to replace bad individuals. Comparing mESC with the latest metaheuristic algorithm and high-performance winner algorithm in the CEC2022 testing suite, numerical results confirmed that mESC outperforms other competitors. Finally, the superiority of mESC in handling problems was verified through several classic real-world optimization problems.

摘要

为应对逃逸算法（ESC）在平衡探索与开发阶段以及收敛精度较低方面的挑战，提出了一种多策略增强版的逃逸算法（简称为mESC）。首先，采用自适应扰动因子策略来保持种群多样性。其次，引入重启机制以增强mESC的探索能力。最后，设计了一种动态质心反向学习策略来平衡局部开发。此外，为加快全局收敛速度，提出了一种基于精英池的边界调整策略，该策略选择精英个体来替换较差个体。在CEC2022测试套件中，将mESC与最新的元启发式算法和高性能优胜算法进行比较，数值结果证实mESC优于其他竞争对手。最后，通过几个经典的实际优化问题验证了mESC在处理问题方面的优越性。