Distributed Task Scheduling Algorithm Based on Intelligent Computing

Intelligent optimization algorithms are a class of bionic algorithms and are well characterized by its self-organizing, self-learning, self-adaptive, implicit parallelism and guided search, etc. These algorithms have the preponderance over solving complex issues and have been widely used in engineering technology, nonlinear optimization, structural design, parallel computing, social science as well as many other fields. Based on improved ant colony algorithm, this paper studies task matching and scheduling for parallel computing. The experiment results show that the performance of proposed task scheduling algorithm is better than traditional ant colony algorithm.