Skip to main content

A Novel Cultural Algorithm and Its Application to the Constrained Optimization in Ammonia Synthesis

  • Conference paper
Life System Modeling and Intelligent Computing (ICSEE 2010, LSMS 2010)

Abstract

A novel cultural differential evolution algorithm with multiple populations (MCDE) is proposed. The single individual in each population is affected by the situational and normative knowledge from belief space simultaneously. The populations communicate with each other following a rule of knowledge exchange, which helps to enhance the search rate of evolution. The concept of culture fusion is introduced to develop an adaptive mechanism of preserving the population diversity. The mechanism ensures that populations are diverse along the whole evolution and excellent candidate solutions are not rejected. The performance of MCDE algorithm is validated by typical constrained optimization problems. Finally, MCDE is applied to maximizing the net value of ammonia in an ammonia synthesis loop. The results indicate that the proposed algorithm has the potential to be used in other problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Durham, W.: Co-evolution: Genes, Culture, and Human Diversity. Stanford University Press, Stanford (1994)

    Google Scholar 

  2. Reynolds, R.G.: An Introduction to Cultural Algorithm. In: Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, Singapore (1994)

    Google Scholar 

  3. Reynolds, R.G., Peng, B., Brewster, J.J.: Cultural Swarms: Knowledge-driven Problem Solving in Social Systems. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3589–3594. IEEE Press, New York (2003)

    Google Scholar 

  4. Gao, F., Cui, G., Liu, H.: Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 817–825. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Lin, C., Chen, C., Lin, C.: A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications. IEEE Trans. Syst. Man. Cy. C 39, 55–68 (2009)

    Article  Google Scholar 

  6. Ricardo, L.B., Carlos, A.C.C.: A Cultural Algorithm with Differential Evolution to Solve Constrained Optimization Problems. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 881–890. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Jin, X., Reynolds, R.G.: Date Mining using Cultural Algorithms and Regional Schemata. In: 14th IEEE International Conference on Tools with Artificial Intelligence, pp. 33–44. IEEE Press, New York (2002)

    Google Scholar 

  8. Huang, H., Gu, X.: Neural Network based on Cultural Algorithms and Its Application on Modeling. Control and Decision 23, 477–480 (2008) (in Chinese)

    Google Scholar 

  9. Yuan, X., Nie, H., He, L., Li, C., Zhang, Y.: A Cultural Algorithm for Scheduling of Hydro Producer in the Power Market. In: Second International Conference on Genetic and Evolutionary Computing, pp. 364–367. IEEE Press, New York (2008)

    Google Scholar 

  10. Storn, R., Price, K.: Differential Evolution–a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, International Computer Science Institute 8, 22–25 (1995)

    Google Scholar 

  11. Koziel, S., Michalewicz, Z.: Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evol. Comput. 7, 19–44 (1999)

    Article  CAS  PubMed  Google Scholar 

  12. Zangwill, W.I.: Nonlinear Programming via Penalty Functions. Management Science 13, 344–358 (1967)

    Article  Google Scholar 

  13. Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Trans. Evol. Comput. 4, 284–294 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, W., Zhang, L., Gu, X. (2010). A Novel Cultural Algorithm and Its Application to the Constrained Optimization in Ammonia Synthesis. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15859-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15859-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15858-2

  • Online ISBN: 978-3-642-15859-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics