求解机组组合问题的嵌入贪婪搜索机制的改进粒子群优化算法。
An improved particle swarm optimization algorithm embedded with greedy search for solution of unit commitment.
文中提出了一种基于改进离散粒子群优化算法求解机组组合问题的新方法。
A solution to unit commitment via an enhanced binary particle swarm optimization (BPSO) algorithm is presented.
通过对测试系统的MATLAB仿真实验和结果分析验证了随机聚焦粒子群算法用于求解机组组合问题的有效性和优越性。
By simulating and analysis of the results of test system using MATLAB, effectiveness and superiority of SFPSO for solving the unit commitment problem are confirmed.
在为机组组合问题编制算法的过程中，因需要满足错综复杂的约束条件，容易使程序结构混乱、逻辑判断出错，最终导致算法求解失败。
During the programming of the algorithm for the unit commitment problem, the complex constraints often lead the program to bad structure or wrong result.
最后，将算法进行改进用于求解考虑机组爬坡速率的机组组合问题。
Finally, the proposed algorithm was improved for solving the unit commitment with ramp rate constrains.
最后，将算法进行改进用于求解考虑机组爬坡速率的机组组合问题。
Finally, the proposed algorithm was improved for solving the unit commitment with ramp rate constrains.
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