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Solving a kind of high complexity multi-objective problems by a fast algorithm

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Wuhan University Journal of Natural Sciences

Abstract

A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions.

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References

  1. Bock T, Fogel D B, Michalewicz Z.Handbook of Evolutionary Computation. Bristol (UK): Institute of Physics Publishing, 1997.

    Google Scholar 

  2. Schaffer J D.Multiple Objective Optimization with Vector Evaluated Genetic Algorithms: [Ph. D Thesis], Vanderbilt: Vanderbilt University, 1984.

    Google Scholar 

  3. Schaffer J D. Multiple Objective Optimization with Vector E-valuated Genetic Algorithms. In: J J Grefenstette Ed.Proceedings of an International Conference on Genetic Algorithms and Their Applications. Pitts-burgh, PA, 1985. 93–100. Sponsored by Texas Instruments and U. S. Navy Center for Applied Research in Artificial Intelligence (NCAR-AI).

  4. Hajela P C, Lin Y. Genetic Search Strategies in Multicriteri-on Optimal Design.Structural Optimization, 1992,4: 99–107.

    Article  Google Scholar 

  5. Fonseca C M, Fleming P J. Genetic Algorithms for Multiob-jective Optimization: Formulation, discussion and generalization. In: S Forrest edEd.Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California: Morgan Kaufmann, 1993. 416–423.

    Google Scholar 

  6. Horn J, Nafpliotis N. Multiobjective Optimization Using the Niched Pareto Genetic algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign. 1993.

    Google Scholar 

  7. Horn J, Nafpliotis N, Goldberg D E. A Niched Pareto Genetic Algorithm for Multiobjective Optimization.Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, 1994,1: 82–87.

    Google Scholar 

  8. Srinivas N, Deb K. Multiobjective Optimization Using Non-dominated Sorting in Genetic Algorithms.Evolutionary Computation, 1994,2(3): 221–248.

    Article  Google Scholar 

  9. Zitzler E.Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications: [Ph. D Thesis], Zurich Switzerland; Swiss Federal Institute of Technology (ETH). 1999.

    Google Scholar 

  10. Deb K, Agrawal S, Pratap A,et al. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGAI1. In: M. S.et al. Ed. Parallel Problem Solving from Nature-PPSN VI, Berlin; Springer. 2000. 849–858.

    Chapter  Google Scholar 

  11. Zitzler E, Laumanns, M, Thiele L. SPEA2; Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103. ETH Zentrum, Gloriastrasse 35, CH-8092 Zurich, Switzerland. 2001.

    Google Scholar 

  12. Purshouse R C, Fleming P J.The Multi-objective Genetic Algorithm Applied to Benchmark Problems—An Analysis. Research Report No. 796. Department of Automatic Control and Systems, Engineering University of Sheffield, Sheffield, S1 3JD, UK. 2001.

    Google Scholar 

  13. Tiwari A, Roy R. Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications.Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature. ( PPSN VII). Granada, Spain,2002.

  14. Montgomery D C.Design and Analysis of Experiments. 3rd ed. New York: Wiley, 1991.

    MATH  Google Scholar 

  15. Deb K, Agrawal S, Pratap A,et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGAII. Technical Report 200001, Indian Institute of Technology, Kanpur: Danpur Genetic Algorithms Laboratory (KanGAL). 2000.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, China University of GeoSciences, 430074, Wuhan, Hubei, China

    Zeng San-you, Ding Li-xin & Kang Li-shan

  2. Department of Computer Science, Zhuzhou Institute of Technology, 412008, Zhuzhou, Hunan, China

    Zeng San-you

  3. State Key Laboratory of Software Engineering, Wuhan University, 430072, Wuhan, Hubei, China

    Ding Li-xin & Kang Li-shan

Authors
  1. Zeng San-you
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  2. Ding Li-xin
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  3. Kang Li-shan
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Corresponding author

Correspondence to Zeng San-you.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (60204001, 70071042, 60073043, 60133010) and Youth Chengguang Project of Science and Technology of Wuhan City (20025001002).

Biography: Zeng San-you ( 1963-), male, Associate professor, research direction: evolutionary computing, parallel computing

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San-you, Z., Li-xin, D. & Li-shan, K. Solving a kind of high complexity multi-objective problems by a fast algorithm. Wuhan Univ. J. of Nat. Sci. 8, 183–188 (2003). https://doi.org/10.1007/BF02899476

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  • DOI: https://doi.org/10.1007/BF02899476

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