Miniaturization design of the universal circuit breaker is very necessary, but it is not enough to consider only the miniaturization in the design but also consider the energy consumption and breaking capacity of the universal circuit breaker. To this end, a comprehensive optimization design method in this paper is proposed and studied. Firstly, based on the analysis of the universal circuit breaker miniaturization model, combines with the universal circuit breaker’s low energy consumption model and high-segmentation model, a comprehensive optimization model for designing universal circuit breakers is constructed. Secondly, for the comprehensive model solution, an improved gray wolf optimization (GWO) algorithm is proposed, that is, a “cloud model” is introduced to balance the local search and global search capabilities to improve the convergence speed; also, a weight strategy is introduced to avoid falling into the local minimum, and simulations of typical test functions show that the improved algorithm is superior to other algorithms. Finally, the improved gray wolf optimization algorithm is applied to the comprehensive optimization design of universal circuit breakers. The experimental results show that the proposed comprehensive design method is feasible and improves the design accuracy and efficiency of the universal circuit breaker.

Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear. The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable. Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation. Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data. Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods.

To solve the imperfect springs structure parameters in the design of energy storage springs of the universal circuit breakers, and problems such as large volume of circuit breakers and low design efficiency, an approach to optimize the parameters of the energy storage springs of the circuit breakers is proposed based on the Artificial Bee Colony (ABC) algorithm. First, the mathematical optimization model of energy storage springs and the constraints of the spring parameters are derived in accordance with the working principle of energy storage springs. Then combined with cloud model and cross operation, the ABC algorithm is improved, which can adjust the cross factor, accelerate the convergence speed of ABC algorithm and improve the global search ability. And the classical test-function simulations verify that the improved ABC algorithm is superior to other evolutionary algorithms. Finally, the two different types of energy storage springs optimization models of universal circuit breakers are experimentally analyzed by use of the improved ABC algorithm, and the corresponding springs’ parameters are calculated. The experiment results show that the proposed approach is effective, which can reasonably design the parameters of energy storage springs of circuit breakers and improve the design efficiency.