基于正交自适应鲸鱼优化的云计算任务调度

针对任务调度中存在的任务完成时间长、系统执行任务成本高且系统负载不均衡等问题，提出了一种基于正交自适应鲸鱼优化算法（OAWOA）的云计算任务调度方法。首先，将正交试验设计（OED）应用于种群初始化和全局搜索阶段，以提升和维持种群的多样性，避免算法过早陷入局部收敛状态；然后，利用自适应指数递减因子和双向搜索机制，来进一步加强算法的全局搜索能力；最后，对适应度函数进行优化，从而使算法实现多目标优化。通过仿真实验将所提的算法与鲸鱼优化算法（WOA）、粒子群优化（PSO）算法、蝙蝠算法（BA）以及其他两种改进的WOA进行比较。实验结果表明，在任务规模为50和500时所提算法都取得了更好的收敛效果，并且得到的系统执行任务的总时间和总成本均低于其他几种算法，同时负载均衡度仅低于BA。可见，所提算法在降低系统执行任务的总时间和总成本以及提高系统负载均衡方面均表现出了显著的优势。

Cloud computing task scheduling based on orthogonal adaptive whale optimization

Aiming at the problems such as long task completion time， high task execution cost and unbalanced system load in task scheduling， a new cloud computing task scheduling method based on Orthogonal Adaptive Whale Optimization Algorithm （OAWOA） was proposed. Firstly， the Orthogonal Experimental Design （OED） was applied to the population initialization and global search stages to improve and maintain the population diversity， avoid the algorithm from falling into local convergence too early. Then， the adaptive exponential decline factor and bidirectional search mechanism were used to further strengthen the global search ability of the algorithm. Finally， the fitness function was optimized to enable the algorithm to achieve multi-objective optimization. Through the simulation experiments， the proposed algorithm was compared with Whale Optimization Algorithm （WOA）， Particle Swarm Optimization （PSO） algorithm， Bat Algorithm （BA） and two other improved WOAs. Experimental results show that， when the task scale is 50 and 500， the proposed algorithm achieves better convergence effect， has the total time and total cost of the obtained system executing tasks lower than those of other algorithms， and has the load balancing degree only lower than that of BA. In conclusion， the proposed algorithm shows significant advantages in reducing the total time and cost of system executing tasks and improving the system load balancing.