This paper focuses on optimizing truss structures while propose best PSO variants. Truss optimization is one way to make the design efficient. There are three types of optimization, size optimization, shape optimization, and topology optimization. By combining size, shape and topology optimization, we can obtain the most efficient structure. Metaheuristics have the ability to solve this problem. Particle swarm optimization (PSO) is metaheuristic algorithm which is frequently used to solve many optimization problems. PSO mimics the behavior of flocking birds looking for food. But PSO has three parameters that can interfere with its performance, so this algorithm is not adaptive to diverse problems. Many PSO variants have been introduced to solve this problem, including linearly decreasing inertia weight particles swarm optimization (LDWPSO) and bare bones particles swarm optimization (BBPSO). The metaheuristic method is used to find the solution, while DSM s used to analyze the structure. A 10-bar truss structure and a 39-bar truss structure are considered as case studies. The result indicates that BBPSO beat other two algorithms in terms of best result, consistency, and convergence behaviour in both cases. LDWPSO took second place for the three categories, leaving PSO as the worst algorithm that tested.

本文侧重于优化桁架结构，同时提出最佳 PSO 变体。桁架优化是提高设计效率的一种方法。优化分为三种类型，尺寸优化、形状优化和拓扑优化。通过结合尺寸、形状和拓扑优化，我们可以获得最有效的结构。元启发式有能力解决这个问题。粒子群优化（PSO）是一种元启发式算法，经常用于解决许多优化问题。PSO 模仿成群的鸟类寻找食物的行为。但是 PSO 有 3 个参数会影响其性能，因此该算法对多样化问题的适应性不强。已经引入了许多 PSO 变体来解决这个问题，包括线性递减惯性权重粒子群优化（LDWPSO）和裸骨粒子群优化（BBPSO）。元启发式方法用于寻找解决方案，而 DSM 用于分析结构。10 杆桁架结构和 39 杆桁架结构被视为案例研究。结果表明，在两种情况下，BBPSO 在最佳结果、一致性和收敛行为方面都优于其他两种算法。LDWPSO 在三个类别中排名第二，使 PSO 成为测试过的最差算法。结果表明，在两种情况下，BBPSO 在最佳结果、一致性和收敛行为方面都优于其他两种算法。LDWPSO 在三个类别中排名第二，使 PSO 成为测试过的最差算法。结果表明，在两种情况下，BBPSO 在最佳结果、一致性和收敛行为方面都优于其他两种算法。LDWPSO 在三个类别中排名第二，使 PSO 成为测试过的最差算法。