Advertisement

A Multi-objective Optimization Algorithm Based on Monarch Butterfly Optimization

  • Rui Hu
  • Jian GaoEmail author
  • Rong Chen
  • Jiahao Jiang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Swarm intelligence optimization algorithm is an important technology that solves the complex optimization problem by simulating the behavior of biological groups in nature. Monarch butterfly optimization (MBO) algorithm is such a swarm intelligence algorithm that simulates the migration behavior of the monarch butterflies in nature. It has received great success on solving single-objective optimization problems, but few contributions on multi-objective problems. In this paper, we modify MBO to solve multi-objective problems, and then propose a new multi-objective optimization algorithm based by combining effective strategies from other swarm-based algorithms. A series of benchmark functions are employed to evaluate the performance of this algorithm. We compare the experimental results with three basic algorithms and state-of-the-art algorithms. It is shown that the proposed algorithm performs better than the compared algorithms on most of the benchmark functions.

Keywords

Hybrid swarm intelligence Monarch butterfly optimization Multi-objective optimization 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 61672122, No. 61602077), the Public Welfare Funds for Scientific Research of Liaoning Province of China (No. 20170005), the Natural Science Foundation of Liaoning Province of China (No. 20170540097), and the Fundamental Research Funds for the Central Universities (No. 3132016348, No. 3132018194).

References

  1. 1.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)CrossRefGoogle Scholar
  4. 4.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, G.G., Deb, S., Cui, Z.: Monarch butterfly optimization. Neural Comput. Appl. 31, 1–20 (2015)Google Scholar
  6. 6.
    Wang, G.G., Deb, S., Zhao, X., Cui, Z.: A new monarch butterfly optimization with an improved crossover operator. Oper. Res. Int. J. 18(3), 731–755 (2018)CrossRefGoogle Scholar
  7. 7.
    Hu, H., Cai, Z., Hu, S., Cai, Y., Chen, J., Huang, S.: Improving monarch butterfly optimization algorithm with self-adaptive population. Algorithms 11(5), 71 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ghetas, M., Yong, C.H., Sumari, P.: Harmony-based monarch butterfly optimization algorithm. In: 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, pp. 156–161. IEEE (2015)Google Scholar
  9. 9.
    Ghanem, W.A., Jantan, A.: Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems. Neural Comput. Appl. 30(1), 163–181 (2018)CrossRefGoogle Scholar
  10. 10.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  11. 11.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Evolutionary Multiobjective Optimization, pp. 105–145. Springer, London (2005)Google Scholar
  12. 12.
    Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary computation and convergence to a pareto front. In: Late Breaking Papers at the Genetic Programming 1998 Conference, pp. 221–228 (1998)Google Scholar
  13. 13.
    Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Cell. Immunol. 37(1), 1–13 (1995)MathSciNetGoogle Scholar
  14. 14.
    Czyzżak, P., Jaszkiewicz, A.: Pareto simulated annealing—a metaheuristic technique for multiple−objective combinatorial optimization. J. Multi-Criteria Decis. Anal. 7(1), 34–47 (1998)CrossRefGoogle Scholar
  15. 15.
    Ma, H.P., Ruan, X.Y., Pan, Z.X.: Handling multiple objectives with biogeography-based optimization. Int. J. Autom. Comput. 9(1), 30–36 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhang, Q., Zhou, A., Jin, Y.: RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41–63 (2008)CrossRefGoogle Scholar
  17. 17.
    Zou, F., Wang, L., Hei, X., Chen, D., Wang, B.: Multi-objective optimization using teaching-learning-based optimization algorithm. Eng. Appl. Artif. Intell. 26(4), 1291–1300 (2013)CrossRefGoogle Scholar
  18. 18.
    Siling, F., Ziqiang, Y., Mengxing, H.: Hybridizing adaptive biogeography-based optimization with differential evolution for multi-objective optimization problems. Information 8(3), 83 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dalian Maritime UniversityDalianChina

Personalised recommendations