An efficient multi-objective optimization approach based on the micro genetic algorithm and its application
In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is...
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| Published in | International journal of mechanics and materials in design Vol. 8; no. 1; pp. 37 - 49 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1569-1713 1573-8841 |
| DOI | 10.1007/s10999-011-9174-2 |
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| Abstract | In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass. |
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| AbstractList | In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass. |
| Author | Liu, G. P. Han, X. Jiang, C. |
| Author_xml | – sequence: 1 givenname: G. P. surname: Liu fullname: Liu, G. P. email: ji_pi@sina.com organization: State Key Laboratory of Advanced Design Manufacturing for Vehicle Body, College of Mechanical and Automotive Engineering, Hunan University – sequence: 2 givenname: X. surname: Han fullname: Han, X. organization: State Key Laboratory of Advanced Design Manufacturing for Vehicle Body, College of Mechanical and Automotive Engineering, Hunan University – sequence: 3 givenname: C. surname: Jiang fullname: Jiang, C. organization: State Key Laboratory of Advanced Design Manufacturing for Vehicle Body, College of Mechanical and Automotive Engineering, Hunan University |
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| Cites_doi | 10.1007/s10732-007-9037-z 10.1109/3468.650320 10.1115/1.2930174 10.1109/ICEC.1994.350037 10.1007/BFb0029752 10.1007/BFb0056872 10.1117/12.969927 10.1201/9780203494486 10.1109/ICSMC.1995.537993 10.1109/TEVC.2005.851274 10.1109/4235.996017 |
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| Keywords | Laminated plates Multi-objective optimization Micro genetic algorithm Non-dominated sorting |
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| References_xml | – reference: LiuGRXiZCElastic waves in anisotropic laminates2001FloridaCRC Press – reference: KnowlesJParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problemsIEEE Trans. Evol. Comput.20061506660312510.1109/TEVC.2005.851274 – reference: Tanaka, M.: GA-based decision support system for multicriteria optimization. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics-2, pp. 1556–1561 (1995) – reference: DebKMulti-objective optimization using evolutionary algorithms2001EnglandWiley0970.90091 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGAIIIEEE Trans. Evol. Comput.2002218219710.1109/4235.996017 – reference: Kursawe, F.: A variant of evolution strategies for vector optimization. In: Parallel Problem Solving from Nature, pp. 193–197. Springer, Berlin (1990) – reference: LuHYenGGRank-density-based multiobjective genetic algorithm and benchmark test function studyIEEE Trans. Evol. Comput.20034325343 – reference: LiuGRHanXComputational inverse techniques in nondestructive evaluation2003FloridaCRC Press1067.7400210.1201/9780203494486 – reference: ZitzlerEThieleLAn evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report 43, Institute TIK1998SwitzerlandETH Zurich – reference: FonsecaCMFlemingPJMultiobjctive optimization and multiple constraint handling with evolutionary algorithms—Part II: application exampleIEEE Trans. Syst. Man Cybern. A199828384710.1109/3468.650320 – reference: ZitzlerEDebKThieleLComparison of multiobjective evolutionary algorithms: empirical resultsIEEE Trans. Evol. Comput.20008173195 – reference: Schafer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. Thesis, Nashville, TN: Vanderbilt University (1984) – reference: Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts (1995) – reference: PulidoGTCoelloCACThe micro genetic algorithm 2: towards on-line adaptation in evolutionary multiobjective optimization. Evolutionary Multi-Criterion Optimization Second International Conference (EMO 2003)2003PortugalFaro252266 – reference: LiuGRTaniJOhyoshiTWatanabeKTransient waves in anisotropic laminated plates. Part 1: theory; Part 2: applicationsJ. Vib. Acoust.199111323023910.1115/1.2930174 – reference: Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423. San Mateo, California (1993) – reference: XuYGLiuGRWuZPA novel hybrid genetic algorithm using local optimizer based on heuristic pattern moveAppl. Artif. Intell.2001760163110.1080/088395101750363966 – reference: EskandariHGeigerCDA fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problemsJ. Heuristics2008142032411211.9020710.1007/s10732-007-9037-z – reference: Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational computation, 1, pp. 82–87. Piscataway, NJ (1994) – reference: SrinivasNDebKMultiobjective optimization using nondominated sorting in genetic algorithmsIEEE Trans. Evol. Comput.19943221248 – reference: Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Fifth international conference on parallel problem solving from nature (PPSN-V): 292–301 (1998b) – reference: Coello, C.A.C., Pulido, G.T.: Multiobjective optimization using a micro-genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), San Francisco, California, pp. 274–282 (2001) – reference: Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: SPIE proceedings: intelligent control and adaptive systems, pp. 289–296 (1989) – ident: 9174_CR1 – ident: 9174_CR18 – ident: 9174_CR19 – ident: 9174_CR6 – volume: 14 start-page: 203 year: 2008 ident: 9174_CR5 publication-title: J. Heuristics doi: 10.1007/s10732-007-9037-z – volume: 28 start-page: 38 year: 1998 ident: 9174_CR7 publication-title: IEEE Trans. Syst. Man Cybern. A doi: 10.1109/3468.650320 – volume: 113 start-page: 230 year: 1991 ident: 9174_CR14 publication-title: J. Vib. Acoust. doi: 10.1115/1.2930174 – volume: 4 start-page: 325 year: 2003 ident: 9174_CR17 publication-title: IEEE Trans. Evol. Comput. – volume-title: Elastic waves in anisotropic laminates year: 2001 ident: 9174_CR15 – volume-title: Multi-objective optimization using evolutionary algorithms year: 2001 ident: 9174_CR2 – ident: 9174_CR10 doi: 10.1109/ICEC.1994.350037 – ident: 9174_CR13 doi: 10.1007/BFb0029752 – ident: 9174_CR24 doi: 10.1007/BFb0056872 – ident: 9174_CR12 doi: 10.1117/12.969927 – start-page: 252 volume-title: The micro genetic algorithm 2: towards on-line adaptation in evolutionary multiobjective optimization. Evolutionary Multi-Criterion Optimization Second International Conference (EMO 2003) year: 2003 ident: 9174_CR9 – volume-title: An evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report 43, Institute TIK year: 1998 ident: 9174_CR23 – volume-title: Computational inverse techniques in nondestructive evaluation year: 2003 ident: 9174_CR16 doi: 10.1201/9780203494486 – volume: 7 start-page: 601 year: 2001 ident: 9174_CR22 publication-title: Appl. Artif. Intell. – volume: 3 start-page: 221 year: 1994 ident: 9174_CR20 publication-title: IEEE Trans. Evol. Comput. – ident: 9174_CR21 doi: 10.1109/ICSMC.1995.537993 – volume: 8 start-page: 173 year: 2000 ident: 9174_CR25 publication-title: IEEE Trans. Evol. Comput. – volume: 1 start-page: 50 year: 2006 ident: 9174_CR11 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2005.851274 – volume: 2 start-page: 182 year: 2002 ident: 9174_CR3 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 |
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| SubjectTerms | Characterization and Evaluation of Materials Classical Mechanics Engineering Engineering Design Solid Mechanics |
| Title | An efficient multi-objective optimization approach based on the micro genetic algorithm and its application |
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