关键词: 环境拓扑结构, 任务调度, 依赖约束, 遗传算法, 精英种群, Simhash

Abstract:  Under the background of the deep promotion of the “East-West Computing Requirement Transfer” project in China， the deployment and scheduling of the environment in the computing power network center faces many challenges， such as the uncertainty of the number， size， topology complexity， dependency constraints， and network transmission volume of the environment. This paper proposes a diverses hierarchical difference optimization genetic algorithm （DHDO-GA） to solve these problems. DHDO-GA aims at optimizing the task execution span makespan and resource utilization rate， while considering the load balancing of resources. In order to guide the entire population to quickly converge to the global optimal solution， DHDO-GA distributes chromosomes at different hierarchical levels based on fitness value and similarity， and abstracts and clusters them into elite populations. Simulation experiments show that the DHDO-GA algorithm is superior to traditional genetic algorithms and several improved genetic algorithms， with greater advantages in terms of search capability， algorithm stability， and result quality and reliability.

Key words: environmental topology, task scheduling, dependency constraint, genetic algorithm, elite population, Simhash