Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis  
[PERSON]  
1 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (X.Y. ); [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
1 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (X.Y. ); [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
1 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (X.Y. ); [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (Z.Y.) [PERSON]  
2 School of Control Science and Control Engineering, Shandong University, Jinan 250061, China; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] [EMAIL_ADDRESS] 201600800737@mail. (MFO, 2015) [11] and so on. These BIOAs have exhibited great potential to address engineering problems, with no exception to the field of analog circuit fault diagnosis. As electronic systems have found their way into military, medical, aerospace, and other fields, individuals put forward stricter requirements on electronic systems in terms of stability, safety, and maintainability [12; 13; 14; 15]. Research on fault diagnosis methods of circuit systems is an effective way to increase their reliability. When an electronic system fails, it should be able to immediately and effectively identify faults to avoid more severe situations. Therefore, circuit fault diagnosis evaluation has become the focus of intense research in the field of circuit design [16; 17]. Recently, numerous circuit diagnosis methods have been proposed. Among them, support vector machine (SVM) is an excellent method on account of its small sample learning ability and short training time [18; 19; 20]. Thus, SVM is frequently applied to analog circuit fault diagnosis. However, an emerging problem is how to select the parameters of SVM, which have high impacts on classification performance. Against this background, many researchers turned their eyes to BIOAs. In [21], a new mutation enhanced binary particle swarm optimizer (PSO)was proposed to find the optimal SVM parameters. In [22], to improve the prediction accuracy, the ABC technique was employed to optimize the internal parameters of SVM. In [23], [PERSON] et al. applied GWO to adjust the kernel parameter and regularization parameter of SVM. In [24], a novel chaos embedded gravitational search algorithm (GSA) with SVM hybrid method was introduced, which hybridized the chaotic search and GSA with SVM. In [25], the cuckoo search (CS)algorithm was adopted to tune the key parameters of SVM. Despite the success of the above-mentioned BIOAs, they still struggle in escaping from local minimums when the optimization task becomes more challenging, which makes it worth exploring new approaches. The well-designed metaheuristic algorithm--whale optimization algorithm (WOA) [26]--was proposed by [PERSON] and [PERSON] in 2016. Advantages abound, one of which is its strong optimization performance [27; 28]. Therefore, WOA has been extensively applied to various fields, including optimal control problem [29], feature selection [30; 31], image segmentation [32], reactive power scheduling problem [33], parameter extraction of solar photovoltaic models [34], etc. [PERSON] et al. [35] presented a new approach for optimal single mobile robot scheduling based on WOA. [PERSON] et al. [36] described a new task scheduling optimization method for mobile equipment using WOA, which has high-grade performance on both efficiency and operational cost. [PERSON] et al. [37] employed WOA to tune the parameters of extreme learning machines, which were applied to evaluate the aging degree of an electronic component. Although WOA has been successfully adopted in numerous areas, several works figured out that it has the drawbacks of premature convergence and local optima stagnation [38; 39].