Volume 7 Number 8 (Aug. 2012)
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JCP 2012 Vol.7(8): 2039-2046 ISSN: 1796-203X
doi: 10.4304/jcp.7.8.2039-2046

A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

Junjie Yang
School of Information and Technology, Zhanjiang Normal University, Zhanjiang, China
Abstract—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.

Index Terms—Multi-objective optimization, particle swarm optimization algorithm, daily generation scheduling.

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Cite: Junjie Yang, "A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations," Journal of Computers vol. 7, no. 8, pp. 2039-2046, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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