Advertisement

A Multi-population QUasi-Affine TRansformation Evolution Algorithm for Global Optimization

  • Nengxian Liu
  • Jeng-Shyang PanEmail author
  • Xiangwen Liao
  • Guolong Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

Abstract

In this paper, we propose a new Multi-Population QUasi-Affine TRansformation Evolution (MP-QUATRE) algorithm for global optimization. The proposed MP-QUATRE algorithm divides the population into three sub-populations with a sort strategy to maintain population diversities, and each sub-population adopts a different mutation scheme to make a good balance between exploration and exploitation capability. In the experiments, we compare the proposed algorithm with DE algorithm and QUATRE algorithm on CEC2013 test suite for real-parameter optimization. The experimental results indicate that the proposed MP-QUATRE algorithm has a better performance than the competing algorithms.

Keywords

QUATRE algorithm Differential evolution Multi-population Global optimization 

References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)Google Scholar
  2. 2.
    Wang, K., Liu, Y.Q., et al.: Improved particle swarm optimization algorithm based on gaussian-grid search method. J. Inf. Hiding Multimed. Signal Process. 9(4), 1031–1037 (2018)Google Scholar
  3. 3.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(1), 29–41 (1996)CrossRefGoogle Scholar
  4. 4.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chu, S.C., Tsai, P.W., Pan, J.S.: Cat swarm optimization. In: The 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 854–858 (2006)Google Scholar
  6. 6.
    Meng, Z., Pan, J.S., Alelaiwi, A.: A new meta-heuristic ebb-tide-fish inspired algorithm for traffic navigation. Telecommun. Syst. 62(2), 1–13 (2016)CrossRefGoogle Scholar
  7. 7.
    Meng, Z., Pan, J.S., Xu, H.: QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: a cooperative swarm based algorithm for global optimization. Knowl.-Based Syst. 109, 104–121 (2016)CrossRefGoogle Scholar
  8. 8.
    Meng, Z., Pan, J.S.: QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: the framework analysis for global optimization and application in hand gesture segmentation. In: 2016 IEEE 13th International Conference on Signal Processing, pp. 1832–1837 (2016)Google Scholar
  9. 9.
    Meng, Z., Pan, J.S.: Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl.-Based Syst. 97, 144–157 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Meng Z, Pan J.S.: QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: a parameter-reduced differential evolution algorithm for optimization problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4082–4089. IEEE (2016)Google Scholar
  11. 11.
    Pan, J.S., Meng, Z., Chu, S., Roddick, J.F.: QUATRE algorithm with sort strategy for global optimization in comparison with DE and PSO variants. In: The Euro-China Conference on Intelligent Data Analysis and Applications, pp. 314–323 (2017)CrossRefGoogle Scholar
  12. 12.
    Meng, Z., Pan, J.S., Li, X.: The quasi-affine transformation evolution (QUATRE) algorithm: an overview. In: The Euro-China Conference on Intelligent Data Analysis and Applications, pp. 324–333 (2017)Google Scholar
  13. 13.
    Meng, Z., Pan, J.S.: QUasi-Affine TRansformation Evolution with External ARchive (QUATRE-EAR): an enhanced structure for differential evolution. Knowl.-Based Syst. 155, 35–53 (2018)CrossRefGoogle Scholar
  14. 14.
    Chang, J.F., Chu, S.C., Roddick, J.F., Pan, J.S.: A parallel particle swarm optimization algorithm with communication strategies. J. Inf. Sci. Eng. 21(4), 809–818 (2005)Google Scholar
  15. 15.
    Tsai, P.W., Pan, J.S., Chen, S.M., Liao, B.Y., Hao, S.P.: Parallel cat swarm optimization. In Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, pp. 3328–3333 (2008)Google Scholar
  16. 16.
    Cui, L.Z., Li, G.H., Lin, Q.Z., et al.: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput. Oper. Res. 67, 155–173 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Liang, J.J., et al.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. In: Computational Intelligence Laboratory, Technical Report 201212. Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nengxian Liu
    • 1
  • Jeng-Shyang Pan
    • 1
    • 2
    Email author
  • Xiangwen Liao
    • 1
  • Guolong Chen
    • 1
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Fujian Provincial Key Lab of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina

Personalised recommendations