基于混沌遗传算法的水电站优化调度模型及应用

利用混沌运动的遍历性、随机性和规律性等特点,提出了一种求解水电站水库优化调度问题的基于浮点数编码的混沌遗传(CGA)算法。该算法的思想是采用混沌优化进行改善初始种群质量和利用混沌退化变异算子代替常规算法中的变异算子,避免搜索过程陷入局部极值。与动态规划、常规遗传算法进行比较,文中实例计算结果表明,该算法计算速度快、收敛性好,提高了常规遗传算法的收敛速度和优化效果。

The optimal operation model based on chaos genetic algorithm for hydropower station and its application

By use of the properties of ergodicity,randomicity and regularity of chaos,a chaos genetic algorithm(CGA) based on float encoding is proposed to solve the optimal operation of hydropower reservoir.CGA adopts chaos optimization of the initialization to improve the species quality and utilizes the annealing chaotic mutation operator to replace the standard mutation operator in order to avoid the search being trapped in local optimum.Comparison of the results with the dynamic programming and the standard GA,CGA can significantly reduce the overall optimization time and improve the convergence quality,it has obvious advantages over the standard GA in convergence speed and solution quality.