To effectively solve the unit optimal commitment of power system, an improved parthenogenetic algorithm (IPGA) is presented.
为了有效地解决火电厂机组优化组合问题，作者提出了一种改进的单亲遗传算法。
Comparing with traditional unit optimal commitment method, this method can manage the complicated constraints of unit optimal commitment expediently.
与传统的机组优化组合方法相比，该方法能方便地处理机组优化组合问题的复杂约束条件。
Power system unit commitment, a problem of nonlinear commitment optimization with typical large-scale hybrid integers, is difficult to get an optimal solution in theory.
电力系统机组组合问题是一个典型的大规模混合整数的非线性组合优化问题，很难得到理论上的最优解。
The optimal unit commitment is solved via the Ant Colony Optimization algorithm in this paper.
采用进化优化算法——蚁群优化算法来求解机组最优启停问题。
Optimal unit commitment is determined by dynamic programming on the basis of using priority list to limit the combination states.
先按优先顺序法压缩各时段机组组合状态，再用动态规划法进行计算。
Computing time is greatly decreased and the global optimal solution is gained by using hybrid heuristic progressive optimality approach to unit commitment.
将该启发式方法与逐步动态优化法相结合进行机组最优组合，使计算时间大为减少，同时可保证得到最优解。
Computing time is greatly decreased and the global optimal solution is gained by using hybrid heuristic progressive optimality approach to unit commitment.
将该启发式方法与逐步动态优化法相结合进行机组最优组合，使计算时间大为减少，同时可保证得到最优解。
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