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

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 C

SUBMITTER: Liu J 

PROVIDER: S-EPMC12025011 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature