实际应用中，鲸鱼优化算法存在精度不高、收敛速度慢等劣势，提出了一种基于非线性收敛因子、自适应权重和模拟退火策略的混合鲸鱼退火算法，平衡了算法的收敛速度和寻优能力，从而增强算法性能。构建了基于结构模态参数的识别因子，并引入稀疏约束，通过不同损伤工况下ASCE Benchmark结构模型的损伤识别数值模拟说明改进方法的有效性。结果表明:增加稀疏约束后，误判单元减少，有利于结构损伤识别，其中L0.5稀疏约束对识别精度的提升最为显著；混合鲸鱼退火算法能有效识别损伤位置及程度，其收敛精度和寻优性能有了明显提升，且在噪声环境下性能稳定，具有一定的抗噪鲁棒性。该方法可进一步应用于实际工程的损伤识别。
Abstract
In practical application, the whale optimization algorithm(WOA) has disadvantages of low accuracy and slow convergence speed.Here, a hybrid whale simulated-annealing algorithm(HWSA) based on nonlinear convergence factor, adaptive weight and simulated annealing strategy was proposed to balance convergence speed and optimization ability of the algorithm, and enhance the performance of the algorithm.Identification factors based on structural modal parameters were constructed, and sparse constraints were introduced.The effectiveness of the improved method was illustrated through damage identification numerical simulation for ASCE Benchmark structural models under different damage conditions.The results showed that misjudged elements decrease with increase in sparse constraints to be beneficial to structural damage identification, and the L0.5 sparse constraint has the most significant improvement on identification accuracy; the hybrid whale annealing algorithm can effectively identify damage location and damage level, its convergence accuracy and optimization performance are significantly improved, its performance is stable in noise environment, and it has a certain anti-noise robustness; this method can be further applied in damage identification of practical engineering.
关键词
结构损伤识别 /
混合鲸鱼退火算法(HWSA) /
非线性收敛因子 /
自适应权重 /
模拟退火 /
稀疏正则化
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Key words
structural damage identification /
hybrid whale simulated-annealing algorithm(HWSA) /
nonlinear convergence factor /
adaptive weight /
simulated annealing /
sparse regularization
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