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.
