Abstract
Several problems that we face in real word are dynamic in nature. For solving these problems, a novel dynamic evolutionary algorithm based on membrane computing is proposed. In this paper, the partitioning strategy is employed to divide the search space to improve the search efficiency of the algorithm. Furthermore, the four kinds of evolutionary rules are introduced to maintain the diversity of solutions found by the proposed algorithm. The performance of the proposed algorithm has been evaluated over the standard moving peaks benchmark. The simulation results indicate that the proposed algorithm is feasible and effective for solving dynamic optimization problems.
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References
Liu, L., Yang, S., Wang, D.: Particle swarm optimization with composite particles in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40, 1634–1648 (2010)
Wang, H., Yang, S., Ip, W.H., Wang, D.: A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems. International Journal of Systems Science 43, 1268–1283 (2012)
Brest, J., Korošec, P., Šilc, J., Zamuda, A., Boškovic, B., Maučec, M.S.: Differential evolution and differential ant-stigmergy on dynamic optimisation problems. International Journal of Systems Science, 1–17 (2011)
Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation 12, 542–561 (2008)
Korosec, P., Silc, J.: The Continuous Differential Ant-Stigmergy Algorithm Applied to Dynamic Optimization Problems. In: Proceedings of the 2012 Congress on Evolutionary Computation, pp. 1317–1324. IEEE Press, New York (2012)
Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation 6, 1–24 (2012)
Cruz, C., Gonzalez, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing-A Fusion of Foundations, Methodologies and Applications 15, 1427–1448 (2011)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9, 303–317 (2005)
Pǎun, G., Rozenberg, G.: A guide to membrane computing. Theoretical Computer Science 287, 73–100 (2002)
Pǎun, G.: Computing with Membranes. Journal of Computer and System Sciences 61, 108–143 (2000)
Pǎun, G., Rozenberg, G., Salomaa, A.: The Oxford handbook of membrane computing. Oxford University Press (2010)
Liu, C., Han, M., Wang, X.: A novel evolutionary membrane algorithm for global numerical optimization. In: 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP), pp. 727–732 (2012)
Liu, C., Han, M., Wang, X.: A Multi-Objective Evolutionary Algorithm based on Membrane Systems. In: 2011 Fourth International Workshop on Advanced Computational Intelligence (IWACI), pp. 103–109 (2011)
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10, 459–472 (2006)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 2nd edn. Springer, Berlin (1994)
Yang, S., Li, C.: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation 14, 959–974 (2010)
Li, C., Yang, S.: A general framework of multi-population methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation 16, 556–577 (2012)
Lung, R.I., Dumitrescu, D.: Evolutionary swarm cooperative optimization in dynamic environments. Natural Computing 9, 83–94 (2010)
Aragón, V.S., Esquivel, S.C., Coello Coello, C.A.: A T-cell algorithm for solving dynamic optimization problems. Information Sciences 181, 3614–3637 (2011)
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Liu, C., Han, M. (2013). Dynamic Evolutionary Membrane Algorithm in Dynamic Environments. In: Middendorf, M., Blum, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2013. Lecture Notes in Computer Science, vol 7832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37198-1_13
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DOI: https://doi.org/10.1007/978-3-642-37198-1_13
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