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Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

MOEA/D, a representative multi-objective evolutionary algorithm, decomposes a multi-objective optimization problem into a number of single objective optimization problems and tries to approximate Pareto front by simultaneously optimizing each of these single objective problems. MOEA/D has several options to calculate a scalar value from multiple objective function values of a solution. In many-objective optimization problems including four or more objective functions, MOEA/D using the weighted sum scalarizing function achieves high search performance. However, the weighted sum has a serious problem that the entire concave Pareto front cannot be approximated. To overcome this problem of the weighted sum based MOEA/D, in this work we propose a method to adaptively determine update ranges of solutions in the framework of MOEA/D. The experimental results show that the weighted sum based MOEA/D using the proposed solution update method can approximate the entire concave Pareto front and improve the search performance.

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References

  1. Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Boston (2002)

    Book  MATH  Google Scholar 

  2. Zhang, Q., Li, H.: MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition. IEEE Trans. on Evolutionary Computation 11(6), 712–731 (2007)

    Article  Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  4. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

  5. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: Proc. of 2008 IEEE Congress on Evolutionary Computation (CEC2008), pp. 2424–2431 (2008)

    Google Scholar 

  6. Deb, K., Jain, H.: An Evolutionary Many-Objective Optimization Algorithm Using Reference-point Based Non-dominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Trans. on EC PP 99, 1–23 (2013)

    Google Scholar 

  7. Ishibuchi, H., Akedo, N., Nojima, Y.: A Study on the Specification of a Scalarizing Function in MOEA/D for Many-Objective Knapsack Problems. In: Nicosia, G., Pardalos, P. (eds.) LION 7. LNCS, vol. 7997, pp. 231–246. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  8. Huband, S., Hingston, P., Barone, L., While, L.: A Review of Multi-objective Test Problems and a Scalable Test Problem Toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)

    Article  Google Scholar 

  9. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Norwell (1999)

    Google Scholar 

  10. Murata, T., Ishibuchi, H., Gen, M.: Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 82–95. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  11. Deb, K., Goyal, M.: A Combined Genetic Adaptive Search (GeneAS) for Engineering Design. Computer Science and Informatics 26(4), 30–45 (1996)

    Google Scholar 

  12. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Hiroyuki Sato

Authors
  1. Hiroyuki Sato
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Sato, H. (2014). Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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