Optimal Reservoir Operation Based on Improved Particle Swarm Optimization Algorithm

Jiao Tian; Jian Cang Xie; Xiao Hong Xing

doi:10.4028/www.scientific.net/AMM.212-213.502

Paper Titles

Development and Utilization Status in the Basin of Shiyang River and Water Quality Evaluation
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Summary on River Basin Water Resources Allocation Practice and Institutional Arrangements of China
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A Study on the Watershed Chemical Weathering in the Water Source Area of the Central Guizhou Water Control Projects
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Study on Calculation Methods of Agricultural Water Supply Cost in the Plain Irrigation Area of the Yellow River
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Optimal Reservoir Operation Based on Improved Particle Swarm Optimization Algorithm
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Application of Contrary Degree and Identity Degree Optimization Model to Determine Reservoir Normal Storage Water Table
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The Impact of Irrigation Diverting from Yellow River Water on Regional Water Cycle in Shandong Province
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Evaluating the Characteristics of Hydraulic Driven Non-Point Source Pollution Loss in Agricultural Watershed
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The Fuzzy Irrigation Control System Based on PC and C8051F
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 212-213Optimal Reservoir Operation Based on Improved...

Optimal Reservoir Operation Based on Improved Particle Swarm Optimization Algorithm

Abstract:

In order to solve the problems of prematurity of particle swarm optimization algorithm and local optimization, a novel particle swarm optimization algorithm based on the organizational evolutionary (OE-PSO) is presented. The evolutional operations are acted on organizations directly in the algorithm, and gained the global convergence ends through competition and cooperation, and overcome the shortcomings of the traditional PSO. Based on analysis of the reservoir operation optimization model and the traits of OE-PSO, the mathematical model and the procedures for solving the optimized reservoir operation optimization by using OE-PSO were proposed in detail. A case study indicates that OE-PSO has better convergence speed and computational accuracy, whereby providing a novel effective method or way for the settlement of the problem of reservoir optimal operation.

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Periodical:

Applied Mechanics and Materials (Volumes 212-213)

Pages:

502-508

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.212-213.502

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Cite this paper

Online since:

October 2012

Authors:

Jiao Tian, Jian Cang Xie, Xiao Hong Xing

Keywords:

Evolutionary Computation, Organisation, Particle Swarm Optimization (PSO), Reservoir Optimal Operation

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