【摘要】 为了克服低压断路器体积大、分断性能差等缺陷,结合弹簧约束条件构建触头弹簧小型化数学模型,提出一种改进的鲸鱼优化算法(CA-WOA)进行优化求解,即在标准鲸鱼优化算法(whale optimization algorithm, WOA)的基础上,结合云模型与自适应权重策略改进鲸鱼优化算法,引入云模型加强算法的全局与局部搜索能力,避免陷入局部极值;再引入自适应权重策略,提高算法的收敛速度与精度;通过经典测试函数仿真和对比分析,表明其改进算法具有更好的性能。进而,采用改进的鲸鱼优化对所构建的触头弹簧数学模型进行迭代寻优,实验结果表明其改进算法能够有效地减小触头弹簧质量与体积,实现低压断路器小型化目标。更多还原

【Abstract】 In order to overcome the shortcomings of low-voltage circuit breakers such as large volume and poor breaking performance, a mathematical model of contact spring miniaturization was constructed in combination with spring constraints, and an improved whale optimization algorithm(CA-WOA) was proposed for the model optimization. On the basis of the standard whale optimization algorithm(WOA), the improved whale optimization algorithm was improved by combining the cloud model and adaptive weighting strategy, and the cloud model was introduced to strengthen the algorithm’s global and local search capabilities to avoid falling into local extrema, and then the adaptive weighting strategy was introduced to improve the convergence speed and accuracy of the algorithm. The simulation and comparative analysis of classic test functions show that the improved algorithm has better performance. Furthermore, the improved whale optimization is used to iteratively optimize the constructed contact spring mathematical model, and the experimental results show that the improved algorithm can effectively reduce the mass and volume of the contact spring and achieve the goal of miniaturization of low-voltage circuit breakers.更多还原