基于链式智能体遗传算法的动态能耗优化算法

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。

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

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 opti- mization problem is still a nonlinear optimization problem. The traditional energy saving method is difficult to solve such a compli- cated 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 ener- gy-saving efficiency without altering the current equipment, when energy consumption environment and energy-saving require- ments changes frequently.