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International Symposium on Intelligence Computation and Applications

ISICA 2010: Advances in Computation and Intelligence pp 338–348Cite as

Perspectives in Dynamic Optimization Evolutionary Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6382))

Abstract

Dynamic Optimization Evolutionary Algorithm(DOEA) is an intrinsic development of traditional Evolutionary Algorithm. Different to the traditional Evolutionary Algorithm which is designed for stationary or static optimization functions, it can be used to solve some dynamic optimization problems. The traditional Evolutionary Algorithm is hard to escape from the old optimum after the convergence when dealing with dynamic optimization problems, therefore, it is necessary to develop new algorithms. After reviewing the relative works, three directions are proposed: first,by treating the time variable as a common variable, DOPs can be extended as a kind of special Multi-objective Optimization Problems, therefore, Multi-objective Optimization Evolutionary Algorithm would be useful to develop DOEAs; second, it would be very important to theoretically analyze Dynamic Optimization Evolutionary Algorithm; finally, DOEA can be applied into more fields, such as industrial control etc..

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References

  1. Abbass, H.A., Sastry, K., Goldberg, D.E.: Oiling the wheels of change: The role of adaptive automatic problem decomposition in nonstationary environments. Tech. rep., Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General Engineering, University of Illinois at Urbana-hampaign (May 2004)

    Google Scholar 

  2. Bendtsen, C.N., Krink, T.: Dynamic memory model for non-stationary optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 145–150 (2002)

    Google Scholar 

  3. Branke, J.: Evolutionary approaches to dynamic optimization problems: A survey. In: Proceeding of GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 133–137 (1999)

    Google Scholar 

  4. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3, pp. 1875–1882 (1999)

    Google Scholar 

  5. Branke, J.: A multi-population approach to dynamic optimization problems. In: Parmee, I. (ed.) Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM 2000), pp. 299–308. Springer, Plymouth (2000)

    Google Scholar 

  6. Branke, J.: Evolutionary approaches to dynamic optimization problems: Updated survey. In: Proceeding of GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 27–30 (2001)

    Google Scholar 

  7. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  8. Droste, S.: Analysis of the (1 + 1) EA for a dynamically bitwise changing OneMax. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 909–921. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: Test cases, approximation, and applications. In: Carlos, M.F., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 311–326. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8(5), 425–442 (2004)

    Article  Google Scholar 

  11. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, pp. 137–144. North-Holland, Amsterdam (1992)

    Google Scholar 

  12. Hemert, J., Hoyweghen, C., Lukshandl, E., Verbeeck, K.: A futurist approach to dynamic environments. In: Bc̈k, J.u.B., Thomas (eds.) Evolutionary Algorithms for Dynamic Optimization Problems, San Francisco, California, USA, pp. 35–38 (2001)

    Google Scholar 

  13. Jin, Y.C., Sendhoff, B.: Constructing dynamic optimization test problems using the multi-objective optimization concept. Applications of Evolutionary Computing 3005, 525–536 (2004)

    Google Scholar 

  14. Kang, L.S., Zhou, A.M., McKay, B., Li, Y., Kang, Z.: Benchmarking algorithms for dynamic travelling salesman problems. In: Proceedings of the 2004 Congress on Evolutionary Computation, New York, vol. 1, pp. 1286–1292 (2004)

    Google Scholar 

  15. Kazuo, Y.: Dynamic pareto optimum ga against the changing environments. In: yamasaki::DPOGACE, J.u.B., Dynamic Optimization Problems, San Francisco, California, USA, pp. 47–50 (2001)

    Google Scholar 

  16. Michalewicz, Z., Schmidt, M., Michalewicz, M., Chiriac, C.: Case study: an intelligent decision support system. Intelligent Systems IEEE [see also IEEE Intelligent Systems and Their Applications] 20(4), 44–49 (2005)

    Article  Google Scholar 

  17. Michalewicz, Z., Schmidt, M., Michalewicz, M., Chiriac, C.: Prediction, distribution, and transportation: A case study. In: Lishan, K., Zhihua, C., Xuesong, Y. (eds.) Progress in Intelligence Computation & Applications, Wuhan, China, vol. 1, pp. 545–557 (2005)

    Google Scholar 

  18. Mori, N., Imanishi, S., Kita, H., Nishikawa, Y.: Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm. In: Bäck, T. (ed.) International Conference on Genetic Algorithms, pp. 299–306. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  19. Mori, N., Kita, H., Nishikawa, Y.: Adapation to a changing environment by means of the thermodynamical genetic algorithm. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 513–522. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  20. Morrison, R.: Design evolutionary algorithms for dynamic environments. Doctoral thesis, George Mason University, Fairfax, Virgina (2002)

    Google Scholar 

  21. Smierzchalski, R.: An intelligent method of ship’s trajectory planning at sea. In: Proceedings of the IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, 1999, pp. 907–912 (1999)

    Google Scholar 

  22. Tins, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)

    Article  Google Scholar 

  23. Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3, p. 1850 (1999)

    Google Scholar 

  24. Vavak, F., Jukes, K., Fogarty, T.C.: Learning the local search range for genetic optimisation in nonstationary environments. In: IEEE International Conference on Evolutionary Computation, 1997, pp. 355–360 (1997)

    Google Scholar 

  25. Vavak, F., Jukes, K., Fogarty, T.C.: Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. In: Bäck, T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, pp. 719–726. Morgan Kaufmann, East Lansing (1997)

    Google Scholar 

  26. Yamasaki, K., Kitakaze, K., Sekiguchi, M.: Dynamic optimization by evolutionary algorithms applied to financial time series. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 2017–2022 (2002)

    Google Scholar 

  27. Yang, S.: Genetic algorithms with elitism-based immigrants for changing optimization problems. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)

    Google Scholar 

  28. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11), 815–834 (2005)

    Article  MATH  Google Scholar 

  29. Yaochu, J., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)

    Article  Google Scholar 

  30. Zheng, B.: Researches on Evolutionary Optimization. Ph.D. thesis, Wuhan University (2006)

    Google Scholar 

  31. Zheng, B., Li, Y., Hu, T.: Vector prediction approach to handle dynamical optimization problems. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 353–360. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  32. Zhou, A.M., Kang, L.S., Yan, Z.Y.: Solving dynamic tsp with evolutionary approach in real time. In: 2003 Congress on Evolutionary Computation, New York, vol. 1, pp. 951–957 (2003)

    Google Scholar 

  33. Zou, X., Wang, M., Zhou, A., McKay, B.: Evolutionary optimization based on chaotic sequence in dynamic environments. In: IEEE International Conference on Networking, Sensing and Control, 2004, vol. 2, pp. 1364–1369 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Information Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Zhiqiong Bu

  2. College of Computer Science, South-Central University For Nationalities, Wuhan, 430074, China

    Bojin Zheng

Authors
  1. Zhiqiong Bu
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  2. Bojin Zheng
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Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, Wu-Han, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Chengyu Hu

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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Bu, Z., Zheng, B. (2010). Perspectives in Dynamic Optimization Evolutionary Algorithm. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_35

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  • DOI: https://doi.org/10.1007/978-3-642-16493-4_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16492-7

  • Online ISBN: 978-3-642-16493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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