基于文化克隆选择算法的梯级水电站联合优化调度

为求解梯级水电站联合优化调度问题,提出文化克隆选择算法（CCSA）。CCSA将克隆选择算法（CSA）嵌入文化算法（CA）框架,并根据克隆选择算法的特点,重新定义了文化算法信念空间的4种知识结构,进而利用这些知识结构指导克隆选择算法的演化过程,避免了高频变异对收敛速度的不利影响,从而提高了算法的收敛速度和搜索效率。函数仿真测试结果表明CCSA在继承CSA多样性好、不易早熟特点的基础上,收敛速度也有进一步提高。将CCSA应用于梯级水电站联合优化调度问题的求解,取得满意效果,为求解该问题提供了一种新的可行方法。

Abstract：

A novel optimization approach-cultured clone select algorithm （CCSA） was proposed to solve optimal dispatch problem of cascade hydroelectric stations.CCSA used cultural algorithm （CA） as its framework and clone select algorithm （CSA） in its population space.Considering the characteristics of CSA,CCSA redefined four knowledge structures in belief space and used these structures to guide the evolution process of CSA.By doing this,CCSA abated the adverse affect caused by high-frequency to convergence rate,thus it converged faster without destroying diversity.CCSA is first tested by several test functions,it is found that CCSA can avoid premature convergence effectively and has fast convergence rate.Then CCSA is applied to an optimal dispatch problem of cascade hydroelectric stations,the results show that it is effective and can be an alternative for this problem.