DocumentCode
229128
Title
Constrained multi-objective evolutionary algorithm based on decomposition for environmental/economic dispatch
Author
Chixin Xiao ; Jianping Yin ; Xun Zhou ; Zhigang Xue ; Mingyu Yi ; Wenjie Shu
Author_Institution
Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
fYear
2014
fDate
9-12 Dec. 2014
Firstpage
1
Lastpage
8
Abstract
The Environmental/Economic Dispatch EED puzzle of power system is actually a classic constrained multi-objective optimization problem in evolutionary optimization category. However, most of its properties have not been researched by its aboriginal Pateto Front. In a meanwhile, the multi-objective evolutionary algorithm based on decomposition(MOEA/D) is a well-known new rising yet powerful method in multi-objective evolutionary optimization domain, but how to run it under constrained conditions has not been testified sufficiently because it is not easy to embed traditional skills to process constraints in such special frame as MOEA/D. Different from non-dominated sorting relationship as well as simply aggregation, this paper proposes a new multi-objective evolutionary approach motivated by decomposition idea and some equality constrained optimization approaches to handle EED problem. The standard IEEE 30 bus six-generator test system is adopted to test the performance of the new algorithm with several simple parameter setting. Experimental results have shown the new method surpasses or performs similarly to many state-of-the-art multi-objective evolutionary algorithms. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems.
Keywords
decomposition; environmental factors; genetic algorithms; load dispatching; power system economics; EED; MOEA-D; Pateto front; constrained multiobjective evolutionary optimization algorithm; decomposition; environmental-economic dispatch; nondominated sorting relationship; power system; standard IEEE 30 bus six-generator test system; Evolutionary computation; Generators; Optimization; Propagation losses; Sociology; Statistics; Vectors; MOEA/D; Pareto; constrained optimization; decomposition; environmental/economic dispatch (EED); multi-objective evolutionary algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on
Conference_Location
Orlando, FL
Type
conf
DOI
10.1109/CICA.2014.7013241
Filename
7013241
Link To Document