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An Improved Whale Algorithm and Its Application in Truss Optimization
Journal of Bionic Engineering ( IF 4 ) Pub Date : 2021-06-11 , DOI: 10.1007/s42235-021-0041-z
Fengguo Jiang , Lutong Wang , Lili Bai

The current Whale Optimization Algorithm (WOA) has several drawbacks, such as slow convergence, low solution accuracy and easy to fall into the local optimal solution. To overcome these drawbacks, an improved Whale Optimization Algorithm (IWOA) is proposed in this study. IWOA can enhance the global search capability by two measures. First, the crossover and mutation operations in Differential Evolutionary algorithm (DE) are combined with the whale optimization algorithm. Second, the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups, so as to balance the global search ability and local development ability. ANSYS and Matlab are used to establish the structure model. To demonstrate the application of the IWOA, truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed, and the results were are compared with that obtained by other optimization algorithm. It is verified that, compared with WOA, the IWOA has higher efficiency, fast convergence speed, better solution accuracy and stability. So IWOA can be used in the optimization design of large truss structures.



中文翻译:

一种改进的鲸鱼算法及其在桁架优化中的应用

目前的鲸鱼优化算法(WOA)存在收敛速度慢、求解精度低、易陷入局部最优解等缺点。为了克服这些缺点,本研究提出了一种改进的鲸鱼优化算法(IWOA)。IWOA 可以通过两个措施增强全局搜索能力。首先,将差分进化算法(DE)中的交叉和变异操作与鲸鱼优化算法相结合。其次,在WOA的位置更新阶段引入云自适应惯性权重,将种群划分为两个子群,以平衡全局搜索能力和局部发展能力。采用ANSYS和Matlab建立结构模型。为了演示 IWOA 的应用,对52杆平面桁架和25杆空间桁架进行了桁架结构优化,并与其他优化算法得到的结果进行了比较。经验证,与WOA相比,IWOA具有更高的效率、更快的收敛速度、更好的求解精度和稳定性。因此IWOA可用于大型桁架结构的优化设计。

更新日期:2021-06-11
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