改进鲸鱼算法在桁架结构优化中的应用

Application of Improved Whale Algorithm in Truss Structure Optimization

针对鲸鱼算法(WOA)在面对桁架结构优化问题上容易陷入局部最优和收敛精度低的缺点,在原始鲸鱼算法的基础上,引入信息熵,提出了信息熵的改进鲸鱼优化算法。信息熵本身是一种不确定的度量,利用信息熵在路径选择时调控鲸鱼搜索的范围,克服基本鲸鱼优化算法的不足,使算法的全局收敛速度得到提高。选取了2个经典的桁架结构优化问题进行求解并与其他算法对比,结果表明:基于信息熵改进的鲸鱼算法在桁架结构优化设计中优于其他算法,运行更少的迭代次数达到目标函数。

In view of the shortcomings of whale algorithm(WOA) in local optimum and low convergence accuracy in the face of truss structure optimization problems, based on the basic whale algorithm, an improved whale optimization algorithm based on information entropy was proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale search in path selection. It can overcome the shortcomings of the basic whale optimization algorithm, and can improve the global convergence speed of the algorithm. Two classical truss structural optimization problems were selected to solve and compared with other algorithms. The test results shows that the improved whale algorithm based on information entropy is superior to other algorithms in truss structures optimization design, and run fewer iterations to achieve the objective function.