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Hybridizing Infeasibility Driven and Constrained-
Domination Principle with MOEA/D for Constrained
Multiobjective Evolutionary Optimization
Huibiao Lin 1,4 , Zhun Fan 1,4,* , Xinye Cai 2 , Wenji Li 1,4 ,
Sheng Wang 1,4 , Jian Li 3,4 , and Chengdian Zhang 1,4
1 School of Engineering, Shantou University, Guangdong, 515063 P.R. China
{zfan,13hblin,12cwang2,chengdianzhang}@stu.edu.cn,
wenji_li@126.com
2 College of Computer Science and Technology, Nanjing University of Aeronautics
and Astronautics,Nanjing, Jiangsu, 210016 P.R. China
xinye@nuaa.edu.cn
3 College of Science, Shantou University, Guangdong, 515063 P.R. China
lijian@stu.edu.cn
4 Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques,
Shantou University, Guangdong, 515063 P.R. China
Abstract. This paper presents a novel multiobjective constraint handling
approach, named as MOEA/D-CDP-ID, to tackle constrained optimization
problems. In the proposed method, two mechanisms, namely infeasibility dri-
ven (ID) and constrained-domination principle (CDP) are embedded into a
prominent multiobjective evolutionary algorithm called MOEA/D. Constrained-
domination principle defined a domination relation of two solutions in
constraint handling problem . Infeasibility driven preserves a proportion of mar-
ginally infeasible solutions to join the searching process to evolve offspring.
Such a strategy allows the algorithm to approach the constraint boundary from
both the feasible and infeasible side of the search space, thus resulting in gain-
ing a Pareto solution set with better distribution and convergence. The efficien-
cy and effectiveness of the proposed approach are tested on several well-known
benchmark test functions. In addition, the proposed MOEA/D-CDP-ID is ap-
plied to a real world application, namely design optimization of the two-stage
planetary gear transmission system. Experimental results suggest that
MOEA/D-CDP-ID can outperform other state-of-the-art algorithms for con-
strained multiobjective evolutionary optimization.
Keywords: Multiobjective evolutionary algorithm, Infeasibility driven,
Constrained -domination principle, Constrained multiobjective optimization,
Penalty functions.
* Corresponding author.
 
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