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..
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)
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)
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)
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)
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)
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)
Jin, Y.C., Sendhoff, B.: Constructing dynamic optimization test problems using the multi-objective optimization concept. Applications of Evolutionary Computing 3005, 525–536 (2004)
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)
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)
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)
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)
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)
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)
Morrison, R.: Design evolutionary algorithms for dynamic environments. Doctoral thesis, George Mason University, Fairfax, Virgina (2002)
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)
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)
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)
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)
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)
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)
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)
Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11), 815–834 (2005)
Yaochu, J., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Zheng, B.: Researches on Evolutionary Optimization. Ph.D. thesis, Wuhan University (2006)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)