# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 1 Introduction
### Related Works
proposed the multi-level process network diagram construction method and built a comprehensive mathematical model of multi-shop production scheduling. An improved PSO algorithm was proposed to solve this problem. By constructing the network subgraph, the invalid search path of the algorithm is avoided, and the efficiency of the algorithm is improved. For scheduling problems with product time constraints, a path search rescheduling strategy was proposed to ensure that the algorithm can obtain an effective search path. Through the analysis of complex product scheduling in the multi-shop environment, the effectiveness and practicability of the above methods were verified [20]. [PERSON] et al. used PSO to solve the problem of arrangement optimization in the field of software testing. By mapping the class test sequence to a one-dimensional space to form a particle position code, the speed and displacement of each particle were calculated according to the fitness function, and then the PSO algorithm was used to select the optimal position and fitness of the particles to obtain the optimal particle. Finally, according to the mapping relationship, the optimal particle code was mapped to the optimal class test sequence [21]. [PERSON] et al. proposed the use of the distribution estimation discrete particle swarm optimization (DPSO) algorithm to solve the permutation problem, and combined the PSO and the distribution estimation algorithm (EDA) to solve the permutation optimization problem, breaking through the speed-displacement update mode of the standard PSO algorithm. Based on the PSO idea, the specific update operation was processed by the distribution estimation algorithm, so as to calculate the optimal solution of the permutation COP [22]. [PERSON] et al. used the DPSO algorithm to solve the multi-objective service path construction problem, and proposed a new particle location initialization and update strategy, which effectively optimized the quality of the service path compared with existing algorithms [23]. [PERSON] et al. proposed the combination of DPSO and non-dominated sorting GA based on elite strategy (multi-objective optimization), introduced the idea of PSO, and completed the update and iteration based on genetic operations at the bottom [24]. For the rectangular optimal layout problem (a permutation coding COP), a hybrid PSO algorithm was proposed. The concept of commutator and commutation order was introduced to solve the problem that the particle update is difficult to describe when the standard PSO solves the COP, and to improve the convergence speed of the processing algorithm in application problems [25]. In addition, research using cluster search algorithms has included solving the disassembly line balancing problem (DLBP) based on the discrete whale swarm algorithm [26], and solving human-robot collaborative disassembly (HRCD) based on the discrete bees algorithm [27, 28]. The above research shows that PSO can be used to solve discrete problems through certain adaptive improvements, but there is no universal PSO solution for different specific application problems. To address the problem that particle swarm operations are difficult to describe, this paper first proposes a hybrid intelligent algorithm using improved genetic crossover operators instead of particle movement operations; second, a probability selection model for alternative vector computation and the definition of the movement direction and target with the largest D-value are proposed, both of which are not mentioned in the abovementioned literature.