DocumentCode :
3505309
Title :
Application of Improved MAGA to Water Pollution Control System Planning
Author :
Qian-jin, Dong ; Fan, Lu ; Deng-hua, Yan
Author_Institution :
State Key Lab. of Water Resources & Hydropower Eng. Sci., Wuhan Univ., Wuhan, China
fYear :
2010
fDate :
30-31 May 2010
Firstpage :
80
Lastpage :
84
Abstract :
Combining the ability of apperception and counteractive to environment of agent with search method of genetic algorithm, an improved multi-agent genetic algorithm (MAGA) is advanced. It ensures diversity of population and improves local search ability of genetic algorithm by simulating competition, cooperate and self-learning of different agents using neighboring cross operator, aberrance operator and self-learning operator of agent. The algorithm is applied to the optimal planning for the waste treatment system of Urumqi, Xinjiang. Results show an improved performance in finding the global minimum when water quality requirements have been fulfilled. The result demonstrates nicer performance and factual value of improved MAGA.
Keywords :
genetic algorithms; multi-agent systems; planning (artificial intelligence); search problems; water pollution control; water treatment; MAGA; Urumqi; Xinjiang; aberrance operator; multi-agent genetic algorithm; neighboring cross operator; search method; self-learning operator; waste treatment system; water pollution control system planning; genetic algorithm; multi-agent; optimal planning; water pollution control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics and Design (APED), 2010 Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7079-2
Electronic_ISBN :
978-1-4244-7080-8
Type :
conf
DOI :
10.1109/APPED.2010.28
Filename :
5662657
Link To Document :
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