基于量子群搜索算法的机组组合问题的研究

Research on Unit Commitment Based on Quantum-inspired Group Search Optimizer

为提高群搜索优化算法的优化效果,提出一种新型量子群搜索算法,并应用于电力系统机组组合求解。采用量子位概率幅表示量子当前信息,避免了计算过程中的反复解码过程;利用量子旋转门进行种群更新,进一步简化了算法流程;提出一种改进的种群初始化策略和启发式约束处理策略,有效提高了算法搜索效率。仿真结果表明:与其他智能优化方法相比,所提算法全局收敛性更强,同时能保证较短的寻优时间。

In order to improve optimization effect of group search optimizer （GSO）, a kind of new quantum-inspired group search optimizer （Q（3SO） was proposed for solving unit commitment （UC） problem of power system. Probability amplitude of quantum bit was used to express present information of quantum which might avoid repeated decoding process in calcula- tion. Quantum rotating gate was used for population update which might further simplify algorithm flow. A kind of im- proved population initialization strategy and heuristic constraint handling strategy was proposed which effectively improved search efficiency of the algorithm. Simulation results indicate that compared with other intelligent optimizing methods, the referred algorithm has stronger global contingency and is able to ensure shorter optimizing time.