基于历史认知的鲸鱼算法求解动态能耗

为提高嵌入式实时系统的能耗管理效率，降低传统动态电压缩放对系统稳定性的影响，提出了基于历史认知的鲸鱼算法支持下的动态能耗优化方案。首先提出非线性动态控制收敛因子的策略，有效加快了算法收敛速度。其次采用历史最优解作为收缩包围机制中的种群干扰因子，设计了混合引导策略来平衡算法的局部开发和全局搜索能力。最后根据动态电压缩放技术可以实时改变处理器频率的特征，利用改进算法对任务量10、30和50进行优化，验证了改进算法的有效性。

Whale algorithm based on historical cognition forsolving dynamic energy consumption

In order to improve the energy consumption management efficiency of the embedded real-time system and reduce the impact oftraditional dynamic voltage scaling technology on system stability, a dynamic energy consumption optimization scheme supported by whalealgorithm based on historical cognition is proposed. Firstly, a nonlinear dynamic convergence factor control strategy is proposed, whichcan effectively accelerate the convergence speed of the algorithm. Secondly, using the historical optimum solutions as interferencefactors, a hybrid guided strategy is designed in the constriction and envelopment mechanism to balance the local development and globalsearch capability of the algorithm. Finally, the frequency characteristics of the processor can be changed in real time according to thedynamic voltage scaling technology, the tasks 10, 30 and 50 are optimized by the algorithm, so as to verify the effectiveness of theimproved algorithm.