基于模糊遗传算法的机组组合问题的求解

为求解机组组合问题,提出一种模糊优化与遗传算法紧密结合的新的模糊遗传算法.通过建立模糊推理规则,对交叉率和变异率进行模糊控制,从而提高了收敛速度,避免了不成熟收敛.将该模糊遗传算法应用于一工程算例中求解机组组合问题,与传统遗传算法相比,在同样的种群规模和终止准则下,采用该算法的收敛迭代次数减少,减幅最大达122次,而每次迭代计算时间最多仅增加约0.01 s;优化组合的发电成本减小,减幅最大时达总发电成本的0.73%.

Solving Unit Commitment Problem Based on Fuzzy Genetic Algorithm

In order to solve the unit commitment problem, a new fuzzy genetic algorithm that closely combines the fuzzy optimization and the genetic algorithm is proposed. Then, some fuzzy inference rules are established to control the crossover rate and the mutation rate, thus improving the convergence speed and avoiding the premature convergence. The proposed fuzzy genetic algorithm is finally adopted to solve the unit commitment problem in a practical case. The results show that, as compared with the traditional genetic algorithm, in the same population scale and termination criteria, the convergence iterative times of the proposed algorithm decrease even by 122, while the com- putation time cost per population only increases by about 0.01 s. Moreover, the electricity-generating cost with the optimal unit commitment decreases, with the largest contraction up to 0.73% of the corresponding total operation cost.