# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 1 Introduction
### Related Works  
Among many intelligence algorithms, the genetic algorithm (GA) has certain advantages in solving COPs because of its simple operation, fewer parameters, and highefficiency. In recent years, there have been many studies on solving COPs using GA for specific problems [5; 6; 7]. [PERSON] et al. used the unified genetic algorithm to solve the flexible job shop scheduling (FJSSP) problem. Through the improvement of the genetic algorithm (GA) by adapting it to the specific operation of FJSSP, the algorithm performance was greatly improved [5]. [PERSON] et al. adopt GA based on multi-layer coding to solve the problem of parking apron support vehicle operation scheduling optimization. The authors designed a mathematical model of vehicle operation scheduling optimization adapted to GA, and improved the algorithm efficiency through multi-level coding [6]. [PERSON] et al. adopted a hybrid intelligent algorithm to solve the FJSSP, established a hybrid intelligent algorithm based on GA and the random restart climbing algorithm, and realized the search in the FJSSP instance, thus obtaining the optimal solution [7]. In another study, an IGA was used to solve the problem of moving edge computing. In this approach, the population uses matrix coding, the selection operator uses a roulette wheel algorithm (the probability of being selected is proportional to the fitness), the annealing operation is completed in the selection operator replication phase, and a temperature parameter is introduced. As the temperature decreases, the number of times that the individuals of the population jump out of the local optimal average increases, and the crossover operator uses multi-row matrix hybridization and the whole row is interchanged. Finally, the mutation operator and feasibility test are completed [8]. [PERSON] et al. applied GA to solve the multi-center VRP. In the design of the two-parent genetic operator, the fixed-length random segments of two parents are used to connect, and the remaining positions are filled according to the sequence rules [9]. [PERSON] et al. used GA to solve the problem of automatic repair sequencing. Because each editing operation of this problem is relatively independent, it is possible to select the random intersection point to divide the position and carry out the crossover replacement operation for the two sequences [10]. Concerning the aspect of task assembly allocation optimization, there are many cases that use GA to solve problems [11; 12; 13], all of which adopt the method of conflict processing after direct crossing. [PERSON] et al. adopted GA to solve the unrelated parallel machine scheduling problem, built a mathematical model with the minimum delay time as the optimization goal, applied a genetic tabu search algorithm (GATS) to solve the problem, and compared the artificial bee colony algorithm (ABC) and genetic simulated annealing algorithm (GASA) to identify problems of different scales [14]. [PERSON] et al. proposed a hybrid algorithm combining fuzzy simulation and GA to solve the facility location assignment (FLA) problem [15]. The above research shows that intelligence algorithms such as GA, SA, ABC, and TS can be used to solve COPs, including task allocation, FJSSP, and FLA. In the process of problem solving, intelligent algorithms are usually improved to adapt to specific problems, or use hybrid computing to improve performance. The particle swarm optimization (PSO) algorithm has certain advantages in solving task allocation problems with its flexibility and global optimization ability [16; 17]. [PERSON] et al. studied integrated production planning and scheduling with a fuzzy start time and processing time. They developed a fuzzy two-level decision technology based on the PSO algorithm and heuristic method [18]. [PERSON] et al. developed a hybrid method combining the PSO algorithm and genetic operator to solve the fuzzy shop scheduling problem with an uncertain processing time [19]. For the scheduling problem of complex products in multi-shop production, [PERSON] et al.