Size optimization of planar truss systems using the modified salp swarm algorithm

This work evaluates the performance of the salp swarm algorithm (SSA) for truss system optimization problems and presents a novel method called the modified salp swarm algorithm (MSSA). Five truss structures, previously optimized by metaheuristics and containing discrete and continuous variables, were used for the evaluations. Size and size–shape optimization types were considered. Although the SSA performs poorly and has convergence issues in initial random solutions, it reaches comparable solutions to previously published results, particularly in continuous problems. In contrast, the MSSA achieves the best solutions for discrete problems and is relatively close to the best results in the reference literature on continuous problems. Moreover, the MSSA convergence curves exhibit a modest increase in convergence rates, especially for discrete problems. It is envisaged that the findings will contribute to improving solution performance, convergence speed and security for future real-world applications.

本研究针对桁架系统优化问题，评估了樽海鞘群算法（Salp Swarm Algorithm，SSA）的性能，并提出了一种名为改进樽海鞘群算法（Modified Salp Swarm Algorithm，MSSA）的新型优化方法。本次评估采用了5种曾通过元启发式算法（Metaheuristics）完成优化的桁架结构，其变量涵盖离散型与连续型两类。本研究考虑了尺寸优化以及尺寸-形状优化两种优化类型。尽管原始SSA在初始随机解场景下表现欠佳且存在收敛缺陷，但其所得解与已公开的同类研究结果相当，尤其在连续型优化问题中。相较之下，改进后的MSSA可在离散型优化问题中获得最优解，且在连续型优化问题上的结果也与参考文献中的最优结果较为接近。此外，MSSA的收敛曲线展现出更优的收敛速率提升效果，尤其针对离散型优化问题。本研究预期该成果可助力提升未来实际工程应用中的求解性能、收敛速度与求解安全性。