Resource Allocation and Dispensation Impact of Stochastic Diffusion Search on Differential Evolution Algorithm

  • Mohammad Majid al-Rifaie
  • John Mark Bishop
  • Tim Blackwell
Part of the Studies in Computational Intelligence book series (SCI, volume 387)


This work details early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Differential Evolution (DE), effectively merging a nature inspired swarm intelligence algorithm with a biologically inspired evolutionary algorithm. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between the population, has the potential to improve the optimisation capability of classical DE.


Hybrid Algorithm Target Vector Trial Vector Stochastic Diffusion Entire Search Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    al-Rifaie, M.M., Bishop, M.: The mining game: a brief introduction to the stochastic diffusion search metaheuristic. AISB Quarterly (2010)Google Scholar
  2. 2.
    al-Rifaie, M.M., Bishop, M., Aber, A.: Creative or not? birds and ants draw with muscles. In: AISB 2011: Computing and Philosophy, University of York, York, U.K, pp. 23–30 (2011a), ISBN: 978-1-908187-03-1Google Scholar
  3. 3.
    al-Rifaie, M.M., Bishop, M., Blackwell, T.: An investigation into the merger of stochastic diffusion search and particle swarm optimisation. In: GECCO 2011: Proceedings of the, GECCO conference companion on Genetic and evolutionary computation. ACM, New York (2011)Google Scholar
  4. 4.
    el Beltagy, M.A., Keane, A.J.: Evolutionary optimization for computationally expensive problems using gaussian processes. In: Proc. Int. Conf. on Artificial Intelligence 2001, pp. 708–714. CSREA Press (2001)Google Scholar
  5. 5.
    Bishop, J.: Stochastic searching networks. In: Proc. 1st IEE Conf. on Artificial Neural Networks, London, UK, pp. 329–331 (1989)Google Scholar
  6. 6.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 3942 (2000)CrossRefGoogle Scholar
  7. 7.
    Branke, J., Schmidt, C., Schmeck, H.: Efficient fitness estimation in noisy environments. In: Spector, L. (ed.) Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Francisco (2001)Google Scholar
  8. 8.
    Brest, J., Zamuda, A., Boskovic, B., Maucec, M., Zumer, V.: Dynamic optimization using self-adaptive differential evolution. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 415–422. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  9. 9.
    Digalakis, J., Margaritis, K.: An experimental study of benchmarking functions for evolutionary algorithms. International Journal 79, 403–416 (2002)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Gehlhaar, D., Fogel, D.: Tuning evolutionary programming for conformationally flexible molecular docking. In: Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming, pp. 419–429 (1996)Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  12. 12.
    Holldobler, B., Wilson, E.O.: The Ants. Springer, Heidelberg (1990)Google Scholar
  13. 13.
    Huang, V., Suganthan, P., Qin, A., Baskar, S.: Multiobjective differential evolution with external archive and harmonic distance-based diversity measure. School of Electrical and Electronic Engineering Nanyang, Technological University Technical Report (2005)Google Scholar
  14. 14.
    Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9, 3–12 (2005)CrossRefGoogle Scholar
  15. 15.
    Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Jong, K.A.D.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, MI, USA (1975)Google Scholar
  17. 17.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)CrossRefGoogle Scholar
  18. 18.
    Kennedy, J.F., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  19. 19.
    Kozlov, K., Samsonov, A.: New migration scheme for parallel differential evolution. In: Proceedings of the International Conference on Bioinformatics of Genome Regulation and Structure, pp. 141–144 (2006)Google Scholar
  20. 20.
    Mendes, R., Mohais, A.: DynDE: a differential evolution for dynamic optimization problems. In: The 2005 IEEE Congress on Evolutionary Computation CEC 2005, vol. 3, pp. 2808–2815 (2005)Google Scholar
  21. 21.
    de Meyer, K.: Explorations in stochastic diffusion search: Soft- and hardware implementations of biologically inspired spiking neuron stochastic diffusion networks. Tech. Rep. KDM/JMB/2000/1, University of Reading (2000)Google Scholar
  22. 22.
    de Meyer, K., Bishop, J.M., Nasuto, S.J.: Stochastic diffusion: Using recruitment for search. In: McOwan, P., Dautenhahn, K., Nehaniv, C.L. (eds.) Evolvability and interaction: evolutionary substrates of communication, signalling, and perception in the dynamics of social complexity, Technical Report 393, pp. 60–65 (2003)Google Scholar
  23. 23.
    de Meyer, K., Nasuto, S., Bishop, J.: Stochastic diffusion optimisation: the application of partial function evaluation and stochastic recruitment in swarm intelligence optimisation. In: Abraham, A., Grosam, C., Ramos, V. (eds.) Swarm Intelligence and Data Mining, ch. 12. Springer, Heidelberg (2006)Google Scholar
  24. 24.
    Moglich, M., Maschwitz, U., Holldobler, B.: Tandem calling: A new kind of signal in ant communication. Science 186(4168), 1046–1047 (1974)CrossRefGoogle Scholar
  25. 25.
    Myatt, D.R., Bishop, J.M., Nasuto, S.J.: Minimum stable convergence criteria for stochastic diffusion search. Electronics Letters 40(2), 112–113 (2004)CrossRefGoogle Scholar
  26. 26.
    Nasuto, S.J.: Resource allocation analysis of the stochastic diffusion search. PhD thesis, University of Reading, Reading, UK (1999)Google Scholar
  27. 27.
    Nasuto, S.J., Bishop, J.M.: Convergence analysis of stochastic diffusion search. Parallel Algorithms and Applications 14(2) (1999)Google Scholar
  28. 28.
    Nasuto, S.J., Bishop, M.J.: Steady state resource allocation analysis of the stochastic diffusion search. csAI/0202007 (2002)Google Scholar
  29. 29.
    Nasuto, S.J., Bishop, J.M., Lauria, S.: Time complexity of stochastic diffusion search. Neural Computation NC98 (1998)Google Scholar
  30. 30.
    Smuc, T.: Improving convergence properties of the differential evolution algorithm. In: Proceedings of the MENDEL 2002 - 8th International Conference on Soft Computing, pp. 80–86 (2002)Google Scholar
  31. 31.
    Stoean, C., Preuss, M., Stoean, R., Dumitrescu, D.: Multimodal optimization by means of a topological species conservation algorithm. IEEE Transactions on Evolutionary Computation 14(6), 842–864 (2010)CrossRefGoogle Scholar
  32. 32.
    Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces TR-95-012 (1995),
  33. 33.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Tasgetiren, M., Suganthan, P.: A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE Congress on Evolutionary Computation CEC 2006, pp. 33–40. IEEE, Los Alamitos (2006)Google Scholar
  35. 35.
    Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: Congress on Evolutionary Computation CEC 2004, vol. 2, pp. 2023–2029. IEEE, Los Alamitos (2004)Google Scholar
  36. 36.
    Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: Congress on Evolutionary Computation, CEC 2004, vol. 2, pp. 1382–1389. IEEE, Los Alamitos (2004)Google Scholar
  37. 37.
    Weber, M., Neri, F., Tirronen, V.: Parallel Random Injection Differential Evolution. In: Applications of Evolutionary Computation, pp. 471–480 (2010)Google Scholar
  38. 38.
    Whitaker, R., Hurley, S.: An agent based approach to site selection for wireless networks. In: 1st IEE Conf. on Artificial Neural Networks. Proc. ACM Symposium on Applied Computing, Madrid Spain. ACM Press, New York (2002)Google Scholar
  39. 39.
    Whitley, D., Rana, S., Dzubera, J., Mathias, K.E.: Evaluating evolutionary algorithms. Artificial Intelligence 85(1-2), 245–276 (1996)CrossRefGoogle Scholar
  40. 40.
    Zaharie, D.: Control of population diversity and adaptation in differential evolution algorithms. In: Proc. of 9th International Conference on Soft Computing, MENDEL, pp. 41–46 (2003)Google Scholar
  41. 41.
    Zhang, J., Sanderson, A.: JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammad Majid al-Rifaie
    • 1
  • John Mark Bishop
    • 1
  • Tim Blackwell
    • 1
  1. 1.GoldsmithsUniversity of LondonLondonUnited Kingdom

Personalised recommendations