Abstract
With the rapid development of computer technology, more and more research organizations and enterprises have turned their attentions from artificial intelligence algorithm to multi-task scheduling. In the face of complex data calculation and task management, natural computing has gradually shown its superiority. As a new method of natural computation, the chemical reaction optimization algorithm has become a new branch of natural computing. In this paper, an improved chemical reaction optimization algorithm is proposed by introducing the ant colony algorithm, which can effectively improve the accuracy and convergence of the chemical reaction optimization algorithm. Firstly, this paper introduces the classification and implementation of chemical reaction optimization algorithm. Secondly, the ant colony algorithm is introduced into the chemical reaction optimization algorithm by introducing the information conversion factor. Finally, ten TSPLIB cases are used as the test object in order to compare with other algorithms. The experimental results show that the improved chemical reaction optimization algorithm based on ant colony algorithm has higher accuracy and efficiency.
