Self-adaptive improved PSO algorithm to solve optimal operation of cascade reservoir systems

被引：0
|
作者：
Xiang, Y. Q. [1 ,2 ]
Mo, L. [1 ,2 ]
机构：
[1] Huazhong Univ Sci & Technol, Shcool Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China
[2] Hubei Key Lab Digital Valley Sci & Technol, Wuhan 430074, Peoples R China
来源：
5TH INTERNATIONAL CONFERENCE ON WATER RESOURCE AND ENVIRONMENT (WRE 2019) | 2019年 / 344卷
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
D O I：
10.1088/1755-1315/344/1/012110
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The optimal scheduling problem of cascade reservoirs is an optimal control problem for a dynamic complex nonlinear system with a large number of constraints, which has not been satisfactorily solved so far. This paper presents a new improved particle swarm optimization (PSO) algorithm, which based on self-adaptive improvement of relative progress, to solve the problem of optimal scheduling of cascade reservoirs. The results obtained by the improved PSO algorithm are found to be encouraging when compared with PSO algorithm under similar test condition.
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页数：8
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