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Using competitive population evaluation in a differential evolution algorithm for dynamic environments. (English) Zbl 1244.90246
Summary: We propose two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results.

MSC:
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence
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[1] Angira, R.; Santosh, A., Optimization of dynamic systems: A trigonometric differential evolution approach, Computers and chemical engineering, 31, 9, 1055-1063, (2007)
[2] Angira, R., Santosh, A., 2008. A modified trigonometric differential evolution algorithm for optimization of dynamic systems. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1463-1468.
[3] Blackwell, T.M.; Bentley, P.J., Dynamic search with charged swarms, (), 19-26
[4] Blackwell, T.; Branke, J., Multiswarm optimization in dynamic environments, Applications of evolutionary computing, 3005, 489-500, (2004)
[5] Blackwell, T.; Branke, J., Multiswarms, exclusion, and anti-convergence in dynamic environments, IEEE transactions on evolutionary computation, 10, 4, 459-472, (2006)
[6] Boettcher, S.; Percus, A.G., Extremal optimization: methods derived from co-evolution, (), 825-832
[7] Branke, J., Evolutionary optimization in dynamic environments, (2002), Kluwer Academic Publishers Norwell, MA, USA · Zbl 1047.68160
[8] Branke, J., 2007. The Moving Peaks Benchmark. <http://www.aifb.uni-karlsruhe.de/∼jbr/MovPeaks/>.
[9] Branke, J.; Schmeck, H., Designing evolutionary algorithms for dynamic optimization problems, (), 239-262
[10] Branke, J.; Kaussler, T.; Schmidt, C.; Schmeck, H., A multi-population approach to dynamic optimization problems, (), 299-308
[11] Brest, J.; Greiner, S.; Boskovic, B.; Mernik, M.; Zumer, V., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE transactions on evolutionary computation, 10, 6, 646-657, (2006)
[12] Brest, J.; Zamuda, A.; Boškovic, B.; Maučec, M.S.; Žumer, V., Dynamic optimization using self-adaptive differential evolution, (), 415-422
[13] Cobb, H.G., 1990. An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments, Navy Center for Applied Research in Artificial Intelligence, Technical Report, AIC-90-001.
[14] Das, S., Konar, A., Chakraborty, U., 2005. An improved differential evolution scheme for noisy optimization problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1691-1698.
[15] Fan, H.-Y.; Lampinen, J., A trigonometric mutation operation to differential evolution, Journal of global optimization, 27, 105-129, (2003) · Zbl 1142.90509
[16] Gibbon, T.B.; Wu, L.; Waswa, D.W.; Conibear, A.B.; Leitch, A.W.R., Polarization mode dispersion compensation for the south african optical-fibre telecommunication network, South african journal of science, 104-119, (2008)
[17] Grefenstette, J.J., Evolvability in dynamic fitness landscapes: A genetic algorithm approach, (), 2031-2038
[18] Hu, X., Eberhart, R., 2002. Adaptive particle swarm optimisation: Detection and response to dynamic systems. In: Congress on Evolutionary Computation, pp. 1666-1670.
[19] Janson, S.; Middendorf, M., A hierarchical particle swarm optimizer, (), 770-776
[20] Janson, S.; Middendorf, M., A hierarchical particle swarm optimizer for dynamic optimization problems, (), 513-524
[21] Jin, Y.; Branke, J., Evolutionary optimization in uncertain environments - A survey, IEEE transactions on evolutionary computation, 9, 3, 303-317, (2005)
[22] Kaelo, P.; Ali, M., A numerical study of some modified differential evolution algorithms, European journal of operational research, 169, 3, 1176-1184, (2006) · Zbl 1079.90106
[23] Kennedy, J.; Eberhart, R., Particle swarm optimization, (), 1942-1948
[24] Li, X.; Dam, K.H., Comparing particle swarms for tracking extrema in dynamic environments, (), 1772-1779
[25] Li, C.; Yang, S., A generalized approach to construct benchmark problems for dynamic optimization, (), 391-400
[26] Li, X.; Branke, J.; Blackwell, T., Particle swarm with speciation and adaptation in a dynamic environment, (), 51-58
[27] Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.G., Suganthan, P.N., 2008. Benchmark Generator for CEC2009 Competition on Dynamic Optimization, Technical Report, University of Leicester, University of Birmingham, Nanyang Technological University.
[28] Liu, B., Zhang, X., Ma, H., 2008. Hybrid differential evolution for noisy optimization. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 587-592.
[29] Lung, R.I.; Dumitrescu, D., A new collaborative evolutionary-swarm optimization technique, (), 2817-2820 · Zbl 0729.73865
[30] Lung, R.I.; Dumitrescu, D., Evolutionary swarm cooperative optimization in dynamic environments, Natural computing: an international journal, 9, 1, 83-94, (2010) · Zbl 1206.90224
[31] Mendes, R.; Mohais, A., Dynde: A differential evolution for dynamic optimization problems, (), 2808-2815
[32] Mori, N.; Kita, H.; Nishikawa, Y., Adaptation to a changing environment by means of the thermodynamical genetic algorithm, (), 513-522
[33] Mori, N.; Imanishi, S.; Kita, H.; Nishikawa, Y., Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm, (), 299-306
[34] Mori, N.; Kita, H.; Nishikawa, Y., Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm, (), 149-158
[35] Morrison, R., Designing evolutionary algorithms for dynamic environments, (2004), Springer · Zbl 1070.68130
[36] Moser, I., 2007. Personal communication.
[37] Moser, I.; Hendtlass, T., A simple and efficient multi-component algorithm for solving dynamic function optimisation problems, (), 252-259
[38] Omran, M.G.; Engelbrecht, A.P.; Salman, A., Bare bones differential evolution, European journal of operational research, 196, 1, 128-139, (2009) · Zbl 1165.90693
[39] Oppacher, F.; Wineberg, M., The shifting balance genetic algorithm: improving the GA in a dynamic environment, (), 504-510
[40] Parrott, D.; Li, X., A particle swarm model for tracking multiple peaks in a dynamic environment using speciation, (), 98-103
[41] Parrott, D.; Li, X., Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE transactions on evolutionary computation, 440-458, (2006)
[42] Price, K.; Storn, R.M.; Lampinen, J.A., Differential evolution - A practical approach to global optimization, (2005), Springer · Zbl 1186.90004
[43] Ramsey, C.L.; Grefenstette, J.J., Case-based initialization of genetic algorithms, (), 84-91
[44] Salman, A.; Engelbrecht, A.P.; Omran, M.G., Empirical analysis of self-adaptive differential evolution, European journal of operational research, 183, 2, 785-804, (2007) · Zbl 1179.90314
[45] Sareni, B.; Krahenbuhl, L., Fitness sharing and niching methods revisited, IEEE transactions on evolutionary computation, 2, 3, 97-106, (1998)
[46] Storn, R., On the usage of differential evolution for function optimization, (), 519-523
[47] Storn, R.; Price, K., Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization, 11, 341-359, (1997) · Zbl 0888.90135
[48] Thomsen, R., Multimodal optimization using crowding-based differential evolution, (), 382-1389
[49] Trojanowski, K., 2007. B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM’07, pp. 143-148.
[50] Ursem, R.K., Multinational GA optimization techniques in dynamic environments, (), 19-26
[51] Vavak, F.; Jukes, K.; Fogarty, T.C., Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search, (), 719-726
[52] Yang, S., Memory-based immigrants for genetic algorithms in dynamic environments, (), 1115-1122
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