关键词: 资源分配, 智能优化算法, 烟花算法, 模拟退火, 遗传算法

Abstract: As a NP-hard problem, the resource allocation problem is a common mathematical problem in cloud computing, radio, satellite scheduling, multi-UAV collaborative task allocation, and many other fields. As an intelligent optimization algorithm, the fireworks algorithm has the ability to solve large-scale resource allocation problems, but also has some problems, such as low solving precision and probability to fall into locally optimal solution. To improve the computational efficiency and global optimization ability of the traditional fireworks algorithm, this paper proposes an improved fireworks algorithm, which uses the mutation operator in genetic algorithm to replace Gaussian mutation operation in the traditional fireworks algorithm and adds the simulated annealing process in each iteration. Finally, the performance of the algorithm is verified by simulation on a mathematical model for multi-UAV cooperative task assignment, and the results show that the algorithm is superior to other three fireworks algorithms in terms of convergence speed and solving precision.

Key words: resource allocation, intelligent optimization algorithm, fireworks algorithm, simulated annealing, genetic algorithm