基于CLPSO算法的混合变量桁架形状优化

A CLPSO algorithm for truss structure shape optimization with mixed variables

为了解决混合变量桁架形状优化问题中离散截面面积和连续节点坐标的变量耦合给优化带来的困难,将一种新型智能优化算法——基于"综合学习策略"的粒子群算法(ComprehensiveLearning Particle SwarmOptimization,CLPSO)应用于桁架混合变量形状优化问题中。给出了考虑离散截面面积和连续节点坐标两类不同性质的设计变量的混合变量桁架结构形状优化的数学模型,并对经典桁架结构进行混合变量的形状优化,将所得结果与其他优化算法结果进行了比较。分析结果表明了该方法进行混合变量桁架形状优化设计的有效性。

In order to overcome the difficulties encountered by the coupling of two distinct types of design variables, the discrete section area and continuous node coordinates, in the shape optimization of truss structures with mixed variables, a novel intelligent optimization method, comprehensive learning particle swarm optimization （CLPSO） is introduced in this paper. The basic principle of CLPSO algorithm is presented in detail first, and then mathematical model for shape optimization of truss structures is presented, in which two distinct types of design variables, the discrete section area and continuous node coordinates, are considered simu- ltaneously. Several classical problems were solved for shape optimization with mixed variables, and the results are compared with those using the other optimization methods. The effectiveness of the proposed method is evaluated through the numerical analysis.