Research on Transmission Task Static Allocation Based on Intelligence Algorithm

Abstract

Transmission task static allocation (TTSA) is one of the most important issues in the automatic management of radio and television stations. Different transmission tasks are allocated to the most suitable transmission equipment to achieve the overall optimal transmission effect. This study proposes a TTSA mathematical model suitable for solving multiple intelligent algorithms, with the goal of achieving the highest comprehensive evaluation value, and conducts comparative testing of multiple intelligent algorithms. An improved crossover operator is proposed to solve the problem of chromosome conflicts. The operator is applied to improved genetic algorithm (IGA) and hybrid intelligent algorithms. A discrete particle swarm optimization (DPSO) algorithm is proposed, which redefines the particle position, particle movement direction, and particle movement speed for the problem itself. A particle movement update strategy based on a probability selection model is designed to ensure the search range of the DPSO, and random perturbation is designed to improve the diversity of the population. Based on simulation, comparative experiments were conducted on the proposed intelligent algorithms and the results of three aspects were compared: the success rate, convergence speed, and accuracy of the algorithm. The DPSO has the greatest advantage in solving TTSA.