## 1 Introduction
However, classical methods still have shortcomings in computing speed, fast real-time fault processing, and online prediction evaluation for large dynamic systems under asymmetric conditions. The development and evolution of intelligent computing methods have led to great progress in the application of many industrial technology fields and provides the possibility to solve complex engineering problems [26; 27; 28]. A synchronous generator connected to a smart grid was improved by the novel recurrent Chebyshev neural network and amended particle swarm optimization to avoid the generation of negative-sequence currents in [29]. A new control strategy was proposed to minimize the significantly unbalanced effect caused by the negative-sequence component on the voltage and current of the power winding during the unbalanced operation of a generator in [30]. To overcome the shortage of cooling technology to manage the overheating of large generator rotors, the impacts of cooling modes, operating parameters, and ambient conditions were investigated in [31]. In order to further improve the calculation accuracy and prediction accuracy of the process neural network, this paper improves the fireworks algorithm (FWA) at the same  
Figure 1: Example of a rotor of a synchronous generator of hundreds of MVA. time and applies the improved algorithm to the process neural network to optimize the network parameters and realize the effective monitoring and analysis of the different working conditions of the generator. Inspired by the physical phenomenon of fireworks generating sparks around the explosion center in the night sky, [PERSON] et al. proposed the FWA [32]. Compared with the genetic algorithm and particle swarm optimization algorithm, the FWA shows a different search mechanism by adopting an explosion search mechanism, calculating the explosion radius and the number of sparks of each firework through the interaction mechanism, so that fireworks with good fitness value obtain more resources; on the contrary, fireworks with poor fitness value obtain fewer resources [33; 34]. The FWA has the ability to balance the global search and local search, but the traditional FWA also has defects, such as high time complexity, mainly because the firework explosions produce a large number of explosion sparks; with the increase in the number of iterations, the calculation cost becomes higher, and the Euclidean distance is used in the selection strategy to calculate the distance between any two individuals, which consumes a lot of time [35]. There is a lack of information on the interaction mechanisms between fireworks and explosion sparks. Due to the lack of utilization of optimal firework information, the population diversity decreases with the increase in iteration times, so it is easy to fall into local optimality and other shortcomings. The Loser-Out Tournament-Based Fireworks Algorithm (LoTFWA) is a multimodal fireworks algorithm based on the elimination of losers as proposed by [PERSON] and [PERSON] in 2018 [36], which introduces a loser elimination mechanism. First, it predicts the fitness value of the \(i\)th fireworks in the final generation and, then, compares it with the fireworks with the best fitness of the current generation. If the value is inferior to the best fitness value of the current generation, it is considered that the fireworks have failed and are thus reinitialized; that is, a new position is randomly generated in the search space, and its explosion amplitude is set to the initial value. This makes the algorithm search more promising areas so as to enhance the local search ability of the algorithm. This algorithm not only avoids searching the same region many times but also avoids searching regions with poor prospects. The LoTFWA improves the generation equation and selection strategy of explosive sparks. The experimental results show that its search effect is far better than other typical improved FWAs, especially in multimodal functions, where its advantages are more prominent, and good optimization results are obtained.