Abstract
Most real-world problems involve objectives, constraints and parameters which constantly change with time. Treating such problems as static problems requires knowledge of the prior time but the computational cost is still high. In this paper, a simplex model based evolutionary algorithm is proposed for dynamic multi-objective optimization, which uses a modified simplex model to predict the optimal solutions (in variable space) of the next time step. Thereafter, a modified evolutionary algorithm which borrows ideas from particle swarm optimization is applied to solve multi-objective problems when the time step is fixed. This method is tested and compared on a set of benchmarks. The results show that the method can effectively track varying Pareto fronts over time.
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References
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation 6, 182–197 (2003)
Deb, K., Rao, U.B.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)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. Evolutionary Computation 3, 257–271 (1999)
Zhang, Z.H., Qian, S.Q.: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Computing 7, 1333–1349 (2011)
Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the Genetic and Evolutioanry Computation Conference, GECCO, pp. 1201–1208. ACM Press (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1941–1948. IEEE Press (1995)
Farina, M., Deb, K., Amato, P.: Dynamic mutiobjective optimization problems: test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8, 425–442 (2004)
Bui, L.T., Abbass, H.A., Brabke, J.: Multiobjective optimization for dynamic environments. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, pp. 2349–2356. IEEE Press (2005)
Yang, S.: Genetic algorithms with memory and elitism-based immigrants in dynamic environments. Evolutionary Computation 16, 385–416 (2008)
Wei, J.X., Zhang, M.J.: A memetic particle swarm optimization for costrained multi-objective optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, pp. 45–53. IEEE Press (2011)
Tsutsui, S., Yamamura, M., Higuchi, T.: Multi-parent recombination with simplex crossover in real coded genetic algorithm. In: Proceedings of the Genetic and Evolutioanry Computation Conference, GECCO, pp. 657–664. ACM Press (1999)
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Wei, J., Zhang, M. (2011). Simplex Model Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_38
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DOI: https://doi.org/10.1007/978-3-642-25832-9_38
Publisher Name: Springer, Berlin, Heidelberg
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