Partitioning periodic multi-frames tasks based on improved ant colony algorithm
基于改进蚁群算法的周期多帧任务分配

Given a set of multi-frame tasks and a heterogeneous multi-processor processing platform, the problem was to determining whether the tasks could be partitioned among the processors in such a manner that all timing constraints were met. This paper constructed a heterogeneous multi-processors periodic multi-frame task model with constraints and proposed an improved ant colony algorithm to solve the partition optimization problem of periodic multi-frames tasks among heterogeneous multi-processors. It introduced several genetic operators, such as reproduction, crossover and mutation, into the ant colony algorithm to enhance the converging rate and global search capability. To improve the self-adaptability of the algorithm, it modified pheromone updating strategy by dynamically adjusting the pheromone residual according to the progress of the algorithm convergence. Additionally, it introduced a deterministic search approach into the algorithm to accelerate the converging rate of the heuristic method. The experimental result proves that it can obtain an optimal or nearly optimal solutions to the multi-frame task allocation in heterogeneous multi-processor quickly with the improved ant colonyalgorithm, which has lower time-complexity as well.