An enhanced memory-based metaheuristic for optimization of large-scale truss structures

Structural optimization is crucial in engineering design, aiming to determine the optimal configuration and sizing of structural elements while adhering to critical constraints. Although swarm intelligence optimization algorithms are among the most promising for this purpose, they often suffer from a loss of diversity, which significantly compromises performance, particularly as the problem dimension increases. This paper presents the Memory Social Spider Algorithm (MSSA), an enhanced swarm-intelligence algorithm for scalable optimization of truss structures. MSSA employs a modified random walk search strategy with a memory mechanism to enhance both exploration and exploitation, while preserving population diversity. The performance of MSSA is evaluated using benchmark global optimization problems and six well-known truss optimization problems with 10, 18, 25, 72, 200, and 942 nodes, covering a wide range of structural scales. The results are compared with those of several advanced optimization algorithms, and a nonparametric Friedman ranking test is used to analyze performance. The results demonstrate that MSSA is highly competitive, particularly for large-scale truss structures, significantly outperforming the compared algorithms in terms of solution quality and robustness. Furthermore, MSSA effectively addresses complex, nonlinear optimization problems, producing innovative and efficient truss designs that achieve superior solution quality while satisfying all structural constraints.