Advertisement

Imperialist Competitive Algorithm

  • Bo Xing
  • Wen-Jing Gao
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 62)

Abstract

In this chapter, we present a new optimization algorithm called imperialist competitive algorithm (ICA) which is inspired by the human socio-political evolution process. We first describe the general knowledge of the imperialism in Sect. 15.1. Then, the fundamentals and performance of ICA are introduced in Sect. 15.2. Finally, Sect. 15.3 summarises this chapter.

Keywords

Particle Swarm Optimization Line Balance Assembly Line Balance Imperialist Competitive Algorithm Artificial Neural Network Training 
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.

References

  1. Acharya, D. P., Panda, G., & Lakshmi, Y. V. S. (2010). Effects of finite register length on fast ICA, bacterial foraging optimization based ICA and constrained genetic algorithm based ICA algorithm. Digital Signal Processing, 20, 964–975.CrossRefGoogle Scholar
  2. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In IEEE Congress on Evolutionary Computation (CEC 2007) (pp. 4661–4667). IEEE.Google Scholar
  3. Ayough, A., Zandieh, M., & Farsijani, H. (2012). GA and ICA approaches to job rotation scheduling problem: Considering employee’s boredom. International Journal of Advanced Manufacturing Technology, 60, 651–666.CrossRefGoogle Scholar
  4. Bagher, M., Zandieh, M., & Farsijani, H. (2011). Balancing of stochastic U-type assembly lines: An imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology, 54, 271–285.CrossRefGoogle Scholar
  5. Bijami, E., Abshari, R., Askari, J., Hosseinnia, S., & Farsangi, M. M. (2011). Optimal design of damping controllers for multi-machine power systems using metaheuristic techniques. International Review of Electrical Engineering, 6, 1883–1894.Google Scholar
  6. Forouharfard, S., & Zandieh, M. (2010). An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems. International Journal of Advanced Manufacturing Technology, 51, 1179–1193.CrossRefGoogle Scholar
  7. Ghanavati, M., Gholamian, M. R., Minaei, B., & Davoudi, M. (2011). An efficient cost function for imperialist competitive algorithm to find best clusters. Journal of Theoretical and Applied Information Technology, 29, 22–31.Google Scholar
  8. Kayvanfar, V., & Zandieh, M. (2012). The economic lot scheduling problem with deteriorating items and shortage: An imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-011-3820-6.
  9. Lian, K., Zhang, C., Shao, X., & Gao, L. (2012). Optimization of process planning with various flexibilities using an imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology, 59, 815–828.CrossRefGoogle Scholar
  10. Moadi, S., Mohaymany, A. S., & Babaei, M. (2011). Application of imperialist competitive algorithm to the emergency medical services location problem. International Journal of Artificial Intelligence and Applications (IJAIA), 2, 137–147.CrossRefGoogle Scholar
  11. Mohammadi, M., Tavakkoli-Moghaddam, R., & Rostami, H. (2011). A multi-objective imperialist competitive algorithm for a capacitated hub covering location problem. International Journal of Industrial Engineering Computations, 2, 671–688.CrossRefGoogle Scholar
  12. Nazari-Shirkouhi, S., Eivazy, H., Ghodsi, R., Rezaie, K., & Atashpaz-Gargari, E. (2010). Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm. Expert Systems with Applications, 37, 7615–7626.CrossRefGoogle Scholar
  13. Nejad, H. C., & Jahani, R. (2011). A new approach to economic load dispatch of power system using imperialist competitive algorithm. Australian Journal of Basic and Applied Sciences, 5, 835–843.Google Scholar
  14. Niknam, T., Fard, E. T., Ehrampoosh, S., & Rousta, A. (2011). A new hybrid imperialist competitive algorithm on data clustering. Sādhanā, 36, 293–315.Google Scholar
  15. Ramezani, F., Lotfi, S., & Soltani-Sarvestani, M. A. (2012). A hybrid evolutionary imperialist competitive algorithm (HEICA). In J.-S. Pan, S.-M. Chen & N. T. Nguyen (Eds.) ACIIDS 2012, Part I, LNAI 7196 (pp. 359–368). Berlin: Springer.Google Scholar
  16. Soltani-Sarvestani, M. A., Lotfi, S., & Ramezani, F. (2012). Quad countries algorithm (QCA). In J.-S. Pan, S.-M. Chen & N. T. Nguyen (Eds.) ACIIDS 2012, Part III, LNAI 7198 (pp. 119–129). Berlin: Springer.Google Scholar
  17. Taghavifar, H., Mardani, A., & Taghavifar, L. (2013). A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement, 46, 2288–2299.CrossRefGoogle Scholar
  18. Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. H. (2012a). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 17, 1312–1319.CrossRefzbMATHMathSciNetGoogle Scholar
  19. Talatahari, S., Kaveh, A., & Sheikholeslami, R. (2012b). Chaotic imperialist competitive algorithm for optimum design of truss structures. Structural and Multidisciplinary Optimization. doi: 10.1007/s00158-011-0754-4.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Engineering, Built Environment and Information Technology, Department of Mechanical Engineering and Aeronautical EngineeringUniversity of PretoriaPretoriaSouth Africa
  2. 2.Department of New Product DevelopmentMeiyuan Mould Design and Manufacturing Co., Ltd.XianghePeople’s Republic of China

Personalised recommendations