# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 1 Introduction
### Related Works  
This is a research paper written by Chinese radio and television researchers on the preparation of a transmission plan. In the process of preparing the plan, the greedy algorithm (GR) is used to find the priority allocation of the tasks that can obtain the optimal allocation of all tasks. According to this principle, until all tasks are scheduled, the number of algorithm iterations is related to the task period, and the backtracking algorithm is used to deal with conflicts in the scheduling. Although the algorithm has been recognized by the industry and has made considerable progress compared to manual compilation based on experience, there is still a lot of room for improvement in the transmission effect [4]. In recent years, related research has made some progress in solving dynamic-task-scheduling problems. [PERSON] et al. summarize the application of an immune-optimization algorithm in unmanned aerial vehicles (UAVs)' scheduling problem. In the optimization process, the immune algorithm introduces an affinity-evaluation operator, an individual-concentration-evaluation operator and an incentive-evaluation operator before searching for the global-optimal solution using the mechanisms of population-diversity maintenance and parallel-distributed search [5]. [PERSON] et al. use queuing theory to deal with the access-task-scheduling problems of database node services [6]. [PERSON] et al. combine the theories of individual concentration and individual incentive degree in the immune algorithm with the fitness function in the genetic algorithm (GA) to both guide the algorithm's search process and achieve multi-objective task scheduling optimization [7]. [PERSON] et al. use the evolutionary algorithm (EA) as a search engine for large-scale global optimization (LSGO) technology to find the global-optimal solution in complex high-dimensional spaces [8]. [PERSON] et al. propose distributed cooperative co-evolution algorithms (DCC) to solve the optimization-evaluation problem, evaluate the contribution of each subgroup to the global fitness, and allocate the computing resources of the subpopulation according to their degree of contribution [9]. [PERSON] et al. propose a threshold-based grouping strategy and grouping variables by pre-setting the relevant threshold of subgroups [10]. [PERSON] et al. solve the overlapping LSGO problem by setting a threshold to specify the size of subgroups. The decision variables are grouped by clustering to avoid the problem of uneven groupings [11]. In solving the multi-objective problems (MOP), the multi-objective evolutionary algorithm (MOEA) is adopted and the algorithm is improved, providing many ideas when studying the dynamic-task-scheduling problem [12; 13; 14; 15; 16; 17; 18; 19]. [PERSON] et al. introduce a reinforced strategy-dynamic-opposition learning based on social-spider-optimization algorithms to enhance individual superiority and schedule workflow in fog computing [20]. [PERSON] et al. propose a job-scheduling algorithm based on the workload prediction of computing nodes, analyze the causes of workload imbalance and the feasibility of reallocating computing resources, and design an application and a workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction models, and propose a parallel-job scheduling method based on computing-node-workload predictions [21]. [PERSON] et al. study single-objective flexible job shop scheduling problems (JSP) by combining genetic algorithms and whale-swarm algorithms; reorganizing whale individuals and genetic codes to improve the local-search ability; and making comparisons with standard examples. Based on this, they conclude that the optimal solution can be obtained [22]. In the above research using EAs, GAs and other intelligent algorithms to solve the task-dynamic-scheduling problem, we found that the DSTT is most similar to the JSP. The algorithm for solving JSP problems has reference significance for the DSTT.