Summary
Many real world optimization problems are dynamic. On the other hand, there are many optimization problems whose solutions must optimize several objectives that are in conflict. In these dynamic multi-objective problems the concept of optimum must be redefined, because instead of providing only one optimal solution, the procedures applied to these multi-objective optimization problems should obtain a set of non-dominated solutions (known as Pareto optimal solutions) that change with time. As evolutionary algorithms steer a population of solutions in a concurrent way by making use of cooperative searching techniques, it could be relatively direct to adapt these algorithms to obtain sets of Pareto optimal solutions. This contribution deals with parallel evolutionary algorithms on dynamic multi-objective optimization (DMO) problems. In this kind of problems, the speed of the reaction to changes is a quite important topic in the context of dynamic optimization, and high-performance computing approaches, such as parallel processing, should be applied to meet the given solution constraints and quality requirements.
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References
Alba, E.: Parallel evolutionary algorithms can achieve super-linear performance. Inf. Process. Lett. 82(1), 7–13 (2002)
Alba, E., Saucedo, J.F., Luque, G.: A Study of Canonical GAs for NSOPs. In: Panmictic versus Decentralized Genetic Algorithms for Non-Stationary Problems, ch. 13, pp. 246–260. Springer, Heidelberg (2007)
Branke, J., Mattfeld, D.C.: Anticipation and flexibility in dynamic scheduling. International Journal of Production Research 43(15), 3103–3129 (2005)
Cámara, M., Ortega, J., de Toro, F.J.: A diversity enhanced single front multiobjective algorithm for dynamic optimization problems. In: Proceedings of the 1st International Conference on Metaheuristics and Nature Inspired Computing, META 2008 (2008)
Cámara, M., Ortega, J., de Toro, F.J.: Parallel processing for multi-objective optimization in dynamic environments. In: Proceedings of The 21st International Parallel and Distributed Processing Symposium, IPDPS 2007 (2007), doi:10.1109/IPDPS.2007.370433
Coello, C.A.C., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer-Verlag New York, Inc., Secaucus (2006)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)
Deb, K., Udaya Bhaskara Rao, N., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007)
Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: Test cases, approximations, and applications. IEEE Trans. on Evolutionary Computation 8, 425–442 (2004)
Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1201–1208. ACM, New York (2006)
Horn, J., Nafpliotis, N.: Multiobjective Optimization using the Niched Pareto Genetic Algorithm. Tech. Rep. IlliGAl Report 93005, Urbana, Illinois, USA (1993)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Trans. on Evolutionary Computation 9(3), 303–317 (2005)
Knowles, J., Corne, D.: The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 98–105. IEEE Press, Mayflower Hotel (1999)
Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2006)
Li, X., Branke, J., Kirley, M.: On performance metrics and particle swarm methods for dynamic multiobjective optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 576–583 (2007)
Luna, F., Nebro, A.J., Alba, E.: Parallel evolutionary multiobjective optimization. In: Nedjah, N., de Macedo Mourelle, L., Alba, E. (eds.) Parallel Evolutionary Computations. Studies in Computational Intelligence, vol. 22, pp. 33–56. Springer, Heidelberg (2006)
Mehnen, J., Wagner, T., Rudolph, G.: Evolutionary optimization of dynamic multiobjective functions. In: Interner Bericht des Sonderforschungsbereichs 531. Computational Intelligence CI–204/06, Universität Dortmund (2006)
Mori, N., Kita, H., Nishikawa, Y.: Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In: PPSN V: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp. 149–158. Springer, London (1998)
Morrison, R.: Performance measurement in dynamic environments. In: Branke, J. (ed.) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 5–8 (2003)
de Toro, F.J., Ortega, J., Ros, E., Mota, S., Paechter, B., Martn, J.M.: PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing 30, 721–739 (2004)
Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proceedings of the Congress on Evolutionary Computation CEC 1999, pp. 1843–1850. IEEE Press, Piscataway (1999)
Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Ph.D. thesis, Wright-Patterson AFB, OH (1999)
Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. on Evolutionary Computation 7, 144–173 (2003)
Weicker, K.: Performance measures for dynamic environments. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 64–76. Springer, Heidelberg (2002)
Zitzler, E., Laumanns, M., Thiele, L., Fonseca, C.M., da Fonseca, V.G.: Why quality assessment of multiobjective optimizers is difficult. In: GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 666–674. Morgan Kaufmann Publishers Inc., San Francisco (2002)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003), http://www.tik.ee.ethz.ch/sop/publicationListFiles/ztlf2002a.pdf
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Cámara, M., Ortega, J., de Toro, F. (2010). Approaching Dynamic Multi-Objective Optimization Problems by Using Parallel Evolutionary Algorithms. In: Coello Coello, C.A., Dhaenens, C., Jourdan, L. (eds) Advances in Multi-Objective Nature Inspired Computing. Studies in Computational Intelligence, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11218-8_4
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DOI: https://doi.org/10.1007/978-3-642-11218-8_4
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