计算机与现代化

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基于链式智能体遗传算法的动态能耗优化算法

  

  1. 四川文理学院，四川达州635000
  • 收稿日期:2014-02-27 出版日期:2014-06-13 发布日期:2014-06-25
  • 作者简介: 周頔（1981-），男，重庆人，四川文理学院助理研究员，硕士，研究方向：人工智能。
  • 基金资助:
     四川省教育厅重点资助项目（14ZB0303）； 达州市重大科技攻关项目（2010zdzx006）

 Algorithm of Dynamic Energy Saving Based on Chain-like Agent Genetic Algorithm

  1. Sichuan University of Arts and Science, Dazhou 635000, China
  • Received:2014-02-27 Online:2014-06-13 Published:2014-06-25

摘要：

能耗优化是一个动态优化问题，在能耗规模较大的情况下，能耗设备间与总能耗间存在一定的非线性关系，即并非每个能耗最优化能使得总的能耗最优化，因此能耗优化是一个动态非线性
优化问题。对于能耗优化问题，传统的节能方法难以奏效。基于此，本文在分析目前节能方法的特点后，设计一种高效的全局优化算法——链式智能体遗传算法，可解决上述的动态非线性优化问题。为
了验证本文提出的算法的优越性，将该算法用于某钢厂的电能节耗中，节耗效果明显且较稳定。实践表明，该算法具有较好的灵活性，当能耗环境和节能要求发生变化时，该算法能在不变动当前设备的
前提下，动态获得较优的节能效率。

关键词:  , 能耗, 节能, 链式智能体遗传算法, 优化

Abstract:

Optimization of energy consumption is a dynamic optimization problem. Under the conditions of larger-scale energy consumption, there exists a nonlinear
relationship between energy consumption equipment and total energy consumption, that is, total energy consumption cannot be achieved only by optimized energy consumption for
every equipment.　Therefore, dynamic optimization problem is still a nonlinear optimization problem. The traditional energy saving method is difficult to solve such a
complicated problem of optimization of energy consumption.　Based on the above-mentioned conditions, the paper analyzes the features of current energy saving　methods and
designs a kind of more efficient global optimization algorithms, i.e. Chain-like agent genetic algorithm, which can effectively solve the above mentioned problem of dynamic
nonlinear optimization method. In order to verify the superiority of algorithm, the author applies the algorithm into the steel mill using for electrical energy　saving and the
effect of energy saving is obvious and stable.　Also, the algorithm has good flexibility. The algorithm can dynamically obtain optimal energy-saving efficiency without altering
the current equipment, when energy consumption environment and energy-saving requirements changes frequently.

Key words:  energy consumption, energy saving, chain-like agent genetic algorithm, optimization

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