Advertisement

An EMO Improvement: Opposition-Based Electromagnetism-Like for Global Optimization

  • Diego OlivaEmail author
  • Erik Cuevas
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 117)

Abstract

Electromagnetism-like Optimization (EMO) is a global optimization algorithm, particularly well-suited to solve problems featuring non-linear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact to each other according to electromagnetism’s laws of attraction and repulsion. However, EMO usually requires a large number of iterations for a local search procedure; any reduction or cancelling over such number, critically perturb other issues such as convergence, exploration, population diversity and accuracy. This chapter presents an enhanced EMO algorithm called OBEMO, which employs the Opposition-Based Learning (OBL) approach to accelerate the global convergence speed. OBL is a machine intelligence strategy which considers the current candidate solution and its opposite value at the same time, achieving a faster exploration of the search space. The presented OBEMO method significantly reduces the required computational effort yet avoiding any detriment to the good search capabilities of the original EMO algorithm. Experiments are conducted over a comprehensive set of benchmark functions, showing that OBEMO obtains promising performance for most of the discussed test problems.

Keywords

Particle Swarm Optimization Local Search Artificial Immune System Benchmark Function Local Search Procedure 
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. 1.
    Tan, S., Cheng, X., Hongbo, X.: An efficient global optimization approach for rough set based dimensionality reduction. Int. J. Innovative Comput. Inf. Control 3(3), 725–736 (2007)Google Scholar
  2. 2.
    Borji, A., Hamidi, M.: A new approach to global optimization motivated by parliamentary political competitions. Int. J. Innovative Comput. Inf. Control 5(6), 1643–1653 (2009)Google Scholar
  3. 3.
    Yang, C.N., Huang, K.S., Yang, C.B., Hsu, C.Y.: Error-tolerant minimum finding with DNA computing. Int. J. Innovative Comput. Inf. Control. 5(10(A)), 3045–3057 (2009)Google Scholar
  4. 4.
    Gao, W., Ren, H.: An optimization model based decision support system for distributed energy systems planning. Int. J. Innovative Comput. Inf. Control. 7(5(B)), pp. 2651–2668 (2011)Google Scholar
  5. 5.
    Chunhui, X., Wang, J., Shiba, N.: Multistage portfolio optimization with var as risk measure. Int. J. Innovative Comput. Inf. Control 3(3), 709–724 (2007)Google Scholar
  6. 6.
    Chang, J.F.:. A performance comparison between genetic algorithms and particle swarm optimization applied in constructing equity portfolios. Int. J. Innovative Comput. Inf. Control. 5(12(B)), pp. 5069–5079 (2009)Google Scholar
  7. 7.
    Takeuchi, Y.: Optimization of linear observations for the stationary kalman filter based on a generalized water filling theorem. Int. J. Innovative Comput. Inf. Control 4(1), 211–230 (2008)Google Scholar
  8. 8.
    Borzabadi, A.H., Sadjadi, M.E., Moshiri, B.: A numerical scheme for approximate optimal control of nonlinear hybrid systems. Int. J. Innovative Comput. Inf. Control 6(6), 2715–2724 (2010)Google Scholar
  9. 9.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks (Piscataway, NJ), pp. 1942–1948 (1995)Google Scholar
  11. 11.
    Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy, Technical Report 91-016. Politecnico di Milano, Italy (1991)Google Scholar
  12. 12.
    Price, K., Storn, R., Lampinen, A.: Differential Evolution a Practical Approach to Global Optimization. Springer Natural Computing Series, Berlin (2005)Google Scholar
  13. 13.
    Fyfe, C., Jain, L.: Teams of intelligent agents which learn using artificial immune systems. J Network Comput. Appl. 29(2–3), 147–159 (2005)CrossRefGoogle Scholar
  14. 14.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization, technical report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  15. 15.
    Rashedia, E., Nezamabadi-pour, H., Saryazdi, S.: Filter modeling using Gravitational Search Algorithm. Eng. Appl. Artif. Intell. 24(1), 117–122 (2011)CrossRefGoogle Scholar
  16. 16.
    İlker, S.: Birbil and Shu-Cherng Fang. An electromagnetism-like mechanism for global optimization. J. Global Optim. 25, 263–282 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rocha, A., Fernandes, E.: Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. Int. J. Comput. Math. 86, 1932–1946 (2009)CrossRefzbMATHGoogle Scholar
  18. 18.
    Rocha, A., Fernandes, E.: Modified movement force vector in an electromagnetism-like mechanism for global optimization. Optim. Methods Softw. 24, 253–270 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Tsou, C.S., Kao, C.H.: Multi-objective inventory control using electromagnetism-like metaheuristic. Int. J. Prod. Res. 46, 3859–3874 (2008)CrossRefzbMATHGoogle Scholar
  20. 20.
    Wu, P., Wen-Hung, Y., Nai-Chieh, W.: An electromagnetism algorithm of neural network analysis an application to textile retail operation. J. Chin. Inst. Ind. Eng. 21, 59–67 (2004)Google Scholar
  21. 21.
    Birbil, S.I., Fang, S.C., Sheu, R.L.: On the convergence of a population-based global optimization algorithm. J. Global Optim. 30(2), 301–318 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Naderi, B., Tavakkoli-Moghaddam, R., Khalili, M.: Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan. Knowl. Based Syst. 23, 77–85 (2010)CrossRefGoogle Scholar
  23. 23.
    Hung H.L., Huang, Y.F.: Peak to average power ratio reduction of multicarrier transmission systems using electromagnetism-like method. Int. J. Innovative Comput. Inf. Control. 7(5(A)), 2037–2050 (2011)Google Scholar
  24. 24.
    Yurtkuran, A., Emel, E.: A new hybrid electromagnetism-like algorithm for capacitated vehicle routing problems. Expert Syst. Appl. 37, 3427–3433 (2010)CrossRefGoogle Scholar
  25. 25.
    Jhen-Yan, J., Kun-Chou, L.: Array pattern optimization using electromagnetism-like algorithm. AEU Int. J. Electron. Commun. 63, 491–496 (2009)CrossRefGoogle Scholar
  26. 26.
    Wu, P., Wen-Hung, Y., Nai-Chieh, W.: An electromagnetism algorithm of neural network analysis an application to textile retail operation. J. Chin. Inst. Ind. Eng. 21, 59–67 (2004)Google Scholar
  27. 27.
    Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Syst. Appl. 37, 8871–8878 (2010)CrossRefGoogle Scholar
  28. 28.
    Cuevas, E., Oliva, D., Zaldivar, D., Pérez-Cisneros, M., Sossa, H.: Circle detection using electro-magnetism optimization. Inf. Sci. 182(1), 40–55 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Guan, X., Dai, X., Li, J.: Revised electromagnetism-like mechanism for flow path design of unidirectional AGV systems. Int. J. Prod. Res. 49(2), 401–429 (2011)CrossRefGoogle Scholar
  30. 30.
    Rocha, A.M.A.C., Fernandes, E.M.G.P.: Numerical experiments with a population shrinking strategy within a electromagnetism-like algorithm. J. Math. Comput Simul. 1(3), 238–243 (2007)Google Scholar
  31. 31.
    Rocha, A.M.A.C., Fernandes, E.M.G.P.: Numerical study of augmented Lagrangian algorithms for constrained global optimization. Optimization. 60(10–11), 1359–1378 (2011)Google Scholar
  32. 32.
    Lee, C.H., Chang, F.K., Kuo, C.T., Chang, H.H.: A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design. Int. J. Syst. Sci. 43(2), 231–247 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of International Conference on Computational Intelligence for Modeling Control and Automation, pp. 695–701 (2005)Google Scholar
  34. 34.
    Rahnamayn, S., Tizhoosh, H.R., Salama, M.: A novel population initialization method for accelerating evolutionary algorithms. Comput. Math Appl. 53(10), 1605–1614 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition versus randomness in soft computing techniques. Elsevier J. Appl. Soft Comput. 8, 906–918 (2008)CrossRefGoogle Scholar
  36. 36.
    Wang, H., Zhijian, W., Rahnamayan, S.: Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft. Comput. (2010). doi: 10.1007/s00500-010-0642-7 Google Scholar
  37. 37.
    Iqbal, M.A., Khan, N.K., Multaba, H., Rauf Baig, A.: A novel function optimization approach using opposition based genetic algorithm with gene excitation. Int. J. Innovative Comput. Inf. Control. 7(7(B)), 4263–4276 (2011)Google Scholar
  38. 38.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRefGoogle Scholar
  39. 39.
    Wanga, H., Wua, Z., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181, 4699–4714 (2011)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Shaw, B., Mukherjee, V., Ghoshal, S.P.: A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems. Electr. Power Energy Syst. 35, 21–33 (2012) Google Scholar
  41. 41.
    Tizhoosh, H.R.: Opposition-based reinforcement learning. J. Adv. Comput. Intell. Intell. Inform. 10(3), 578–585 (2006)Google Scholar
  42. 42.
    Shokri, M., Tizhoosh, H.R., Kamel, M.: Opposition-based Q(k) algorithm. In: Proceedings of IEEE World Congress Computational Intelligence, pp. 646–53 (2006)Google Scholar
  43. 43.
    Dixon, L.C.W., Szegö, G.P.: The global optimization problem: An introduction. Towards Global Optimization 2, North-Holland, Amsterdam, pp. 1–15 (1978)Google Scholar
  44. 44.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Garcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 Special session on real parameter optimization. J. Heurist (2008). doi: 10.1007/s10732-008-9080-4 zbMATHGoogle Scholar
  46. 46.
    Santamaría, J., Cordón, O., Damas, S., García-Torres, J.M., Quirin, A.: Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft Comput. doi: 10.1007/s00500-008-0351-7, in press (2008)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Departamento de Electrónica, CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  2. 2.Tecnológico de Monterrey, Campus GuadalajaraZapopanMexico

Personalised recommendations