Chaotic enhanced leader slime mold algorithm for dome structures with frequency constraints

This paper introduces the chaotic enhanced leader slime mold algorithm (CELSMA), an advanced bio-inspired optimization technique aimed at addressing high-dimensional engineering challenges. Building on the traditional slime mold algorithm (SMA), CELSMA implements a multi-leader strategy that utilizes three candidate leaders to enhance both exploration and exploitation capabilities. Additionally, CELSMA harnesses the ergodic and non-repetitive characteristics of chaotic maps to improve global search behavior and reduce the risk of premature convergence to local optima. The proposed algorithm is applied to the size optimization of truss structures under frequency constraints, a computationally intensive task due to the repeated evaluation of structural eigenvalues. To tackle this issue, the largest eigenvalues of sparse matrix (LESM) technique is employed to significantly decrease computational time, facilitating the practical optimization of large-scale truss systems. Comprehensive numerical experiments were conducted on large-scale dome truss structures and benchmarked against established metaheuristic algorithms. The results clearly indicate that the CELSMA-LESM approach achieves superior accuracy and convergence speed, consistently yielding optimal solutions with fewer iterations. The CELSMA source code is publicly available at: https://github.com/nut123456/CELSMA.git .