For this consideration, many scholars have taken actions to enhance the performance of WOA. As an example, [PERSON] et al. [40] utilized Levy Flight to promote WOA and obtained a better tradeoff between global and local search. [PERSON] et al. [41] put forward a cosine-based dynamic parameter updating method, which facilitated the performances of WOA. [PERSON] et al. [42] introduced some chaotic variants where the parameters of the standard WOA were integrated with chaos maps. In [43], a novel hybrid algorithm that integrated the Tabu search with WOA was developed to improve the convergence speed and local searchability. In fact, many WOA variants can facilitate the performance of the original WOA to a certain extent. But, deficiencies still remain as most of them only improve the ability of a single aspect, e.g., exploration, or maintain diversity, etc. This motivates us to provide novel modifications for enhancing the general performance of WOA. In this paper, a multi-strategy ensemble whale optimization algorithm (MSWOA) is proposed to compensate for the shortcomings of WOA, and it mainly contains four highlights. First, whales are initialized by chaos theory so that they are more evenly distributed in the search domain, and the possibility of obtaining the global optimum is increased. Second, the random search strategy results in a poor convergence rate and stability since whales are searching around a random individual. Thus, an improved random searching strategy was developed to resolve the inefficiency of the previous scheme. Third, WOA has a chronic deficiency in losing diversity, because the original spiral updating position strategy drives the whole whale swarm to the current global best individual, and the search diversity would be hampered, especially in the early stage. Hence, the Levy flight strategy was adopted to make a good balance between global and local search. Finally, the enhanced position revising mechanism was employed in MSWOA to strengthen exploration further. Extensive comparative experiments were conducted to investigate the effectiveness of the proposed algorithm. For a start, numerical optimization experiments were carried out on nineteen widely applied benchmark functions (seven unimodal functions, six multimodal functions, and six fixed-dimensional multimodal functions), and the proposed MSWOA was compared with one promising GWO variants and five other types of well-designed SI algorithms. Experimental results revealed that for most functions, our proposal had considerable advantages in both search accuracy and convergence speed. Moreover, we applied MSWOA to tune the penalty parameter \(C\) and the kernel parameter \(\gamma\) of SVM, which was adopted for the fault diagnosis of analog circuits. Comparison studies showed that, with the comparison to the PSO and WOA methods, SVM optimized by MSWOA got an extraordinary average diagnostic accuracy. Namely, MSWOA has better practicability in circuit fault diagnosis. This paper is organized as follows: Section 2 introduces the WOA algorithm briefly; Section 3 describes the proposed MSWOA algorithm in detail; Section 4 illustrates the experimental setup of the benchmark functions and the comparison results, which prove that the proposed MSWOA has better search performance; Section 5 applies the MSWOA to two diagnostic instances and performs a detailed analysis of results; finally, Section 6 provides conclusions and future works.