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首页> 外文期刊>Journal of Computers >A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations
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A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

机译：一种新的粒子群优化算法及其在水电站最优运行中的应用及其应用

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—Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.
机译：- 一些工程优化问题，如多目标日常代的水电站调度，客观函数具有明显的层次和优先级，同时，约束非常复杂。通过采用传统的最优技术，难以解决操作问题。本文提出了一种解决层次结构多目标时序化问题的新粒子群优化算法。该算法可以有效地处理级别的多目标优化问题。通过采用自适应惯性重量算法（AWA）和突变尺度本地搜索算法（MSLSA），提高了算法的收敛性能。然后，建立了一种用于水电站的多目标日常发电调度模型，其中涉及包括最大限度的峰值能量容量益处和发电最大化的两个目标功能。最后，利用所提出的算法研究了在低流量期间三峡级联水电系统的多目标日常发电调度问题，以获得最大的峰值能量效益，以及三峡级联站的发电效益。

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