Multi-objective constraint task scheduling algorithm for multi-core processors

Abstract

A task scheduling algorithm is an effective means to ensure multi-core processor system efficiency. This paper defines the task scheduling problem for multi-core processors and proposes a multi-objective constraint task scheduling algorithm based on artificial immune theory (MOCTS-AI). The MOCTS-AI uses vaccine extraction and vaccination to add prior knowledge to the problem and performs vaccine selection and population updating based on the Pareto optimum, thereby accelerating the convergence of the algorithm. In the MOCTS-AI, the crossover and mutation operators and the corresponding use probability for the task scheduling problem are designed to guarantee both the global and local search ability of the algorithm. Additionally, the antibody concentration in the the MOCTS-AI is designed based on the bivariate entropy. By designing the selection probability in consideration of the concentration probability and fitness probability, antibodies with high fitness and low concentration are selected, thereby optimizing the population and ensuring its diversity. A simulation experiment was performed to analyze the convergence of the algorithm and the solution diversity. Compared with other algorithms, the MOCTS-AI effectively optimizes the scheduling length, system energy consumption and system utilization.