Chaotic Artificial Bee Colony algorithm based on memory for solving dynamic optimization problems

Document Type : Research Paper

Authors

Abstract

Artificial Bee Colony Algorithm(ABC) is one of the swarm intelligence optimization algorithms that is extensively used for the goals and applications static. Many practical, real-world applications, nevertheless, are dynamic. Thus we need to get used optimization algorithms that could be solved problems in dynamic environments as well. Dynamic optimization problems where change(s) may occur through the time. In this paper we proposed one approach based on chaotic ABC combined with explicit memory method, for solving dynamic optimization problems. In this proposed algorithm, we used the explicit memory for store the aging best solution for the maintaining diversity in the population. Use the aging best solution and diversity in environments helps the speed convergence in algorithm. The proposed approaches have been tested on Moving Peaks Benchmark. The Moving Peaks Benchmark is the suitable function for testing optimization algorithms in dynamic environments. The experimental study on a Moving Peaks Benchmark show that proposed approach has a superior performance in comparison with several other algorithms in dynamic environments.

Keywords

References

5-        
   [1]      Yang, S., Li C. (2010). “A Clustering Particle  Swarm Optimizer  for Locating  and  Tracking  Multiple  Optima  in  Dynamic Environments”.  IEEE  Transactions  on Evolutionary Computation, vol. 14, no. 6, pp. 959-974.
   [2]      Yang . S, (2007)., "Explicit memory schemes for evolutionary algorithms in dynamic environments". In S. Yang, Y.-S. Ong, and Y. Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments, volume 51 of Studies in Computational Intelligence, pages 3-28. Springer-Verlag.
   [3]      Kamos,i M., Hashemi, A.B., Meybodi, M.R., (2010). “A New Particle Swarm Optimization Algorithm for Dynamic Environments”. SEMCCO. pp. 129-138.
   [4]      Blackwell, T., Branke, J. (2006). “Multi-Swarms, Exclusion, and Anti-Convergence in Dynamic Environments”. IEEE Transactions on Evolutionary Computation 10, 459–472.
   [5]      Blackwell, T. and Branke, J. (2004). “Multi-swarm optimization in dynamic environments”. In: G.R. Raidl, editor, Applications of Evolutionary Computing, volume 3005 of Lecture Notes in Computer Science, pp.489–500. Springer, Berlin, Germany. 
   [6]      Blackwell, T. and Branke, J and Li, X. (2008). “Particle swarms for  dynamic optimization problems”. Swarm Intelligence. Springer Berlin Heidelberg,. 193-217.
   [7]      Du, W., Li, B. (2008). "Multi-Strategy Ensemble Particle Swarm Optimization for Dynamic Optimization",.Information Sciences: an International Journal Vol.178, pp.3096–3109.
   [8]      Li, C. and Yang, S. (2009). “A clustering particle swarm optimizer for dynamic optimization,” in Proc. Congr. Evol. Comput, pp. 439–446.
   [9]      Li, C. and Yang, S. (2008)., “Fast Multi-Swarm Optimization for Dynamic Optimization Problems”.  Proc, Int’l Conf.     Natural Computation, vol. 7, no. 3, pp. 624-628.
[10]      Karaboga, D. Basturk, B. (2009). “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”. Journal of Global Optimization, 39, 459–471.
[11]      Krasnogor, N. and Smith, j. (2005). “A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues”.   IEEE Trans. Evolutionary Computation, vol. 9, no.   5, pp. 474-488.
[12]      Yang, S. (2007). “Explicit memory schemes for evolutionary algorithms in dynamic environment”. s. In S. Yang, Y.-S. Ong, and Y. Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments, volume 51 of Studies in Computational Intelligence, pages 3-28. Springer-Verlag.
[13]      Ryan, C. (1997). “Dyploidy without dominance”.   In J. T. Alander, editor, Proceedings of the Nordic Workshop on Genetic Algorithms, pages 6370.
[14]      Yang, S. (2007). “Genetic algorithms with elitism-based immigrants for changing optimization problems”.    In Applications of Evolutionary Computing, Lecture Notes in Computer Science 4448, pages 627–636.
[15]      Ramsey, C. Grefenstette, J. (1993). “Case-based initialization of genetic algorithms”. In S. Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 84-91. Morgan Kaufmann.
[16]      Trojanowski, K. and Michalewicz, Z. (1999). “Searching for optima in non-stationary environments”. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), pages 1843-1850. IEEE Press.
[17]      Wang, H. Yang, S. (2012). Ip D.WH., “A memetic particle swarm optimisation algorithm for dynamic multi-modal optimization problems”. Int J Syst Sci 43(7):1268–1283.
[18]      Branke, J. (1999). “Memory enhanced evolutionary algorithms for changing optimization problems”. In Congress on Evolutionary Computation, pages 1875–1882.    
[19]      Morrison, R and DeJong, K. (1999). “A test problem generator   for non-stationary environments”. In Congress on Evolutionary Computation, pages 2047–2053.
[20]      Branke, J. The Moving Peaks Benchmark Website, http://www.aifb.unikarlsruhe.  De/jbr/movpeaks.
[21]      Parrott, D and Li, X. (2006). “Locating and Tracking MultipleDynamic Optima by A Particle Swarm Model Using  peciation”. in IEEE Transaction on Evolutionary Computation, vol. 10, No. 4, pp. 440-458.
[22]      Hashemi, A. B. and Meybodi, M. R. (2009). “Cellular PSO: A PSO for Dynamic Environments”. Advances in    Computation and Intelligence, pp. 422-433.
[23]      Lung, R. I and Dumitrescu, D. (2010). “Evolutionary swarm cooperative optimization in dynamic environments,” Natural Comput., vol. 9, no. 1, pp. 83–94.
[24]      Bird, S and Li, X. (2007). “Using regression to improve local convergence,” in Proc. Congr. Evol. Comput., pp. 592–599.
[25]      Lung, R. I and Dumitrescu, D. (2007). “A collaborative model for tracking optima in dynamic environments,” in Proc. Congr. Evol. Comput,  pp. 564–567.
[26]      Li, C. and Yang, S. (2012). A general framework of multipopulation methods with clustering in undetectable dynamic environments, Evolutionary Computation, IEEE Transactions on16(4): 556–577.
[27]      Nasiri, B. and Meybodi, M. (2012). "Speciation based firefly algorithm for optimization in dynamic environments", International Journal of Artificial Intelligence 8(S12): 118–132.
[28]      Noroozi, V., Hashemi, A. and Meybodi, M. (2011). Cellularde: a cellular based differential evolution for dynamic optimization problems, Adaptive and Natural Computing Algorithms pp. 340–349.
 
 
 
 
 
Journal of Modeling in Engineering
Volume 15, Issue 51
October 2017
Pages 113-132

Files

History

  • Receive Date: 21 June 2015
  • Revise Date: 20 February 2016
  • Accept Date: 06 April 2016

Share

How to cite

Statistics