部分解约束算法在机组负荷优化组合中的应用

针对实数遗传算法应用于火电厂机组负荷优化组合问题中存在早熟收敛的难题，提出了部分解约束结合惩罚函数的改进实数遗传算法，在解除约束条件的方法、变异操作策略、种群初始化等多方面针对火电厂机组负荷优化组合问题的自身特点对实数遗传算法提出了新的改进思路，解决了实数遗传算法应用于多峰值优化问题中早熟而收敛于局部极值点的难题，对某5台机组的火电厂机组负荷优化组合的优化仿真表明，采用该方法改进后的遗传算法优化成功率能达到100%。

Application of Float Genetic Algorithms-Partially Solved Combined With Punishing Function in Power Plant Units Commitment Problem

To address the optimization premature convergence problem of unit commitment problem (UCP) in power plant with float genetic algorithms (FGA), a refined FGA with the constrained conditions of partially solved combined with punishing function (FGA-PPF) were introduced FGA-PPF refined in the dealing with its constrained conditions, the strategy of mutation, initialization of population of FGA with respect to the features of UCP. FGA-PPF resolved the problem of pre-mature in FGA , in the application to a five units power plant UCP, The results shows that the optimization success rate can reach 100% with the FGA-PPF.