## 3 Theoretical Analysis and Model Construction
### Reconstruction Model of Hybrid Intelligent Optimization Algorithm
#### 3.2.2 Options of the Hybrid Intelligent Algorithms  
To solve the extreme value of the objective function, the Monte-Carlo method converges toward the local optimal solution when the number of samples is relatively small and lacks practicality, especially when the dimensionality of the solution parameters is large [35]. Neural network algorithms are also found to be limited in their ability to solve multi-covariate optimization problems. With efficient, parallel, and global search capabilities, the SGA intelligent algorithm is able to adjust the evolutionary direction autonomously through the previously accumulated evolutionary knowledge in the search process until the permissive conditions are satisfied [36] and the optimal solution of the objective function can thus be found. The genetic algorithm is a practical, efficient, and robust optimization technology [37]. Due to the open nature of its algorithm, it has good improvement potential and has thus developed very rapidly into a widely used intelligent search algorithm for optimization. Currently, it is a research hotspot at home and abroad [38]. As a global optimization stochastic search algorithm, the SGA intelligent algorithm's search performance is mainly determined by the crossover operator and variational operator with a probabilistic nature. During the evolution of an SGA search, the performance of the core genetic operation depends mainly on the crossover probability \(Pc\) and the variation probability \(Pm\). Since the final search result of SGA is sensitive to \(Pc\) and \(Pm\), different values will bring different evolutionary results [25]. The choice of genetic parameters is particularly important because an SGA often uses a single fixed genetic parameter. Experts across various sectors have introduced distinct methodologies for determining genetic parameters when employing genetic algorithms to address a range of problems. So far, there is still no uniform rule for parameter selection; therefore, a large number of tests are often required to select the optimal result when faced with a new problem, which is time-consuming and cannot obtain globally optimal results. Therefore, immature convergence (also called \"premature\") in a genetic algorithm has become a nonnegligible problem [39]. In order to solve the aforementioned problem relating to the SGA, this research introduces a multiple-population genetic algorithm (MPGA) to improve the SGA using a multiple-population coevolution strategy. In SGA evolutionary search, crossover and variation operators are the core means of new individual generation, playing an important role in influencing the SGA's global and local search performance. Usually, larger crossover probability \(Pc\) and smaller variation probability \(Pm\) tend to be used in SGA applications. However, it is still confronted with a wide range of selections in terms of genetic parameters, and different values yield different evolutionary results. The MPGA improves SGA by enabling multiple-population collaborative evolutionary searches with different genetic parameters. It ameliorates the population's performance in terms of both global searches and local searches. During the evolution of the MPGA, each population is relatively independent of each other and transmits information mainly through the migration operator. The migration operator exchanges high-quality information between multiple populations with the rule that the highest-quality individual in the current population (i.e., the individual with the highest fitness value) replaces the worst-quality individual in the target population (i.e., the individual with the lowest fitness value). Traditional linear reconstruction algorithms for the electromagnetic wave CT reconstruction of working faces include back projection (BP), the conjugate gradient method (CG), and the least square method (LSQR) for solving the inverse matrix, algebraic iteration (ART), instantaneous iteration method (SIRT), etc. [40].