The purpose is to improve the search performance of the particle swarm optimization algorithm applied to solving the optimization design problem of the section size of the truss structure under the condition of stress and displacement constraints. The basic theory about the information entropy and particle swarm optimization algorithm were introduced, the feasibility about combining them for being understood was analyzed, the parameters of the particle swarm optimization algorithm was reasonably set and the information entropy was introduced to playing the twofold roles of the fitness function and terminated standards. In order to test the search performance of the improved particle swarm optimization algorithm using above methods, it and other optimization algorithms are compared with applying to solve two classic truss structure optimization solutions. The results experiment data comparison shows that the particle swarm optimization improved by the information entropy can search the smaller stress variance than references', which reduces stress range of the truss structure and make it safer. The conclusion is that the improved particle swarm optimization algorithm by the information entropy has the better search performance in solving optimization problems comparing with the other similar algorithms.%提出了信息熵改进的粒子群优化算法用于解决有应力约束、位移约束的桁架结构杆件截面尺寸优化设计问题.首先介绍了信息熵基本理论和基本粒子群优化算法理论,然后对粒子群优化算法作了合理的参数设置,并将信息熵引入粒子群优化算法的适应函数和停机判别准则中.最后对2个经典的优化问题进行求解并与其他算法进行了比较.数据结果表明信息熵改进后的粒子群优化算法在桁架结构优化设计中优于其他同类算法.
展开▼
