Multi-Objective Evolutionary Algorithm Based on Decomposition for Engineering Optimization

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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.

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[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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