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

提出了一种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。其特点包括:采用固定阈值处理表示机组运行状态的0、1整型变量,从而可直接应用粒子群算法求解机组组合问题,避免求解各时段中的经济负荷分配子问题;在粒子群算法迭代过程中应用变异操作更新进化速度缓慢的粒子,增强了算法的搜索能力;算法收敛后,采用基于优先列表的贪婪搜索机制做进一步寻优,既加快了算法收敛速度,又提高了解的质量。算例结果表明所提出的方法在求解机组组合问题时具有很强的搜索能力和适应性。

To solve unit commitment （UC） an improved particle swarm optimization algorithm is proposed, in which greedy search is embedded. The features of the proposed method are as following： the integer variables representing units＇ operation states are processed by fixed threshold, thus UC can be directly solved by algorithm and the sub-problems particle swarm optimization of economic dispatch in each time interval can be avoided; in the iteration of particle swarm optimization the slow evolution particles are renewed by mutation operation, so the search capability of the algorithm is enhanced; after the algorithm is converged, through the further search by use of greedy search based on priority list （PL）, the convergence is accelerated as well as the quality of the solution is improved. The proposed algorithm is tested and verified by two UC cases, the calculation results show that the proposed method possesses efficient search capability and adaptability.