托管型数据中心激励机制优化算法分析与研究

发布时间：2018-10-05 13:13

【摘要】：针对紧急需求响应下托管型数据中心激励机制中胜标方决策效率低的问题,分别采用动态规划、遗传算法、粒子群优化及混合算法等方法对其激励机制的优化展开分析与研究。根据紧急需求响应的特点和算法需求建立托管型数据中心的激励机制优化模型,证明激励机制中的胜标方决策问题为一个NPC问题。从理论上分析优化算法使用的可行性及复杂度性,为算法在激励机制中的应用提供了理论基础。通过实验仿真,分别从4种算法的处理规模、性能以及时间复杂度的角度进行阐述和对比,实验结果表明,4种算法优化了胜标方选择最大化可供电力并满足管理员的最大支付,体现了优化算法解决问题的有效性及高效率性,提高了决胜标方决策的效率。
[Abstract]:In order to solve the problem of low efficiency of decision making in incentive mechanism of managed data center under emergency demand response, dynamic programming, genetic algorithm, particle swarm optimization and hybrid algorithm are used to analyze and study the optimization of incentive mechanism. According to the characteristics of emergency demand response and algorithm requirements, the incentive mechanism optimization model of managed data center is established. It is proved that the winner decision problem in incentive mechanism is a NPC problem. The feasibility and complexity of the optimization algorithm are analyzed theoretically, which provides a theoretical basis for the application of the algorithm in the excitation mechanism. Through the experimental simulation, the processing scale, performance and time complexity of the four algorithms are discussed and compared respectively. The experimental results show that the four algorithms optimize the choice of the winning party to maximize the power supply and meet the maximum payment of the administrator. It reflects the efficiency and efficiency of the optimization algorithm, and improves the efficiency of decision making.
【作者单位】：桂林理工大学"嵌入式技术与智能信息处理"广西高校重点实验室;桂林理工大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61563012、61540054) 广西自然科学基金项目(2015GXNSFBA139260) 桂林理工大学科研启动基金项目(002401003456) “嵌入式技术与智能信息处理”广西高校重点实验室主任基金项目(2016-01-05)
【分类号】：TP18;TP308

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