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Multi-Objective Evolutionary Algorithm Based on Decomposition for Engineering Optimization
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[1]ZHANG Chunjiang,TAN Kay Chen,GAO Liang,et al.Multi-Objective Evolutionary Algorithm Based on Decomposition for Engineering Optimization[J].Journal of Zhengzhou University (Engineering Science),2015,36(06):38-.[doi:10.3969/ j. issn.1671 -6833.2015.06.008]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 36 Number of periods: 2015 06 Page number: 38- Column: Public date: 2015-12-25
Title:
Multi-Objective Evolutionary Algorithm Based on Decomposition for Engineering Optimization
Author(s):
ZHANG Chunjiang1; 2; TAN Kay Chen2; GAO Liang1;  wU qing3
1. School of Mechanical Scinece & Engineering，Huazhong University of Science and’Technology，Wuhan 430074，China，2.Department of Electrical and Computer Engineering，National University of Singapore，Singapore 117583; 3.School of Engineer-ing，Huazhong Agricultural University ，Wuhan 430070，China
Keywords:
multi-objective evolutionary algorithm; MOEA/D;  normalization;  engineering optimization;  dif-ferential evolution;  s-constraint handling
CLC:
-
DOI:
10.3969/ j. issn.1671 -6833.2015.06.008
Abstract:
 In order for effective application of Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D) in engineering optimization，normalization of the range of objective values is needed. A self-a-daptive s constrained Differential Evolution ( gDE) algorithm is proposed to obtain the minimum and maximumvalues of each objective on the Pareto Front ( PF). After normalization，MOEA/D can then be effectively ap-plied. In addition ,the self-adaptive s constraint method is combined with MOEA/D for constraint handling. Abenchmark problem and a weld bean design problem are used to evaluate the performance of the algorithm a-gainst two other normalization methods. One main advantage of the proposed method is the selective concen-trated optimization on some regions on the Pareto front which allows handling of problems where regions of Pa-reto front are difficult to be optimized.
References:
[1]SCHAFFER，DAVID J. Some experiments in machinelearning using vector evaluated genetic algorithmsD]. Nashville，TN ( USA) : Vanderbilt Univ，1985.
[2]ZITZLER E,LAUMANNS M ,THIELE L. SPEA2: Im-proving the strength pareto evolutionary algorithm formultiobjective optimizationR].Zurich: Computer En-gineering and Networks Laboratory( TIK),Swiss FederalInstitute of Technology( ETH)，2001: 19 - 21.
[3]DEB K，PRATAP A，AGARWAL S,et al. A fast andelitist multiobjective genetic algorithm: NSGA - I1.IEEE Transactions on Evolutionary Computation,2002，6( 2): 182 -197.
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