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Artificial Bee Colony Algorithm Combined with Uniform Design

  • Jie Zhang
  • Junhong FengEmail author
  • Guoqiang Chen
  • Xiani Yang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)

Abstract

As artificial bee colony algorithm is sensitive to the initial solutions, and is easy to fall into local optimum and premature convergence, this study presents a novel artificial bee colony algorithm based on uniform design to acquire the better initial solutions. It introduces an initialization method with uniform design to replace random initialization, and selects the better ones of those initial bees generated by the initialization method as the initial bee colony. This study also introduces a crossover operator based on uniform design, which can search evenly the solutions in the small vector space formed by two parents. This can increase searching efficiency and accuracy. The best two of the offsprings generated by the crossover operator based on uniform design are taken as new offsprings, and they are compared with their parents to determine whether to update their patents or not. The crossover operator can ensure that the proposed algorithm searches uniformly the solution space. Experimental results performed on several frequently used test functions demonstrate that the proposed algorithm has more outstanding performance and better global searching ability than standard artificial bee colony algorithm.

Keywords

Bee colony Artificial bee colony Uniform design Uniform crossover 

Notes

Acknowledgements

This research was supported by National Natural Science Foundation of China (No. 61841603), Guangxi Natural Science Foundation (No. 2018JJA170050), Improvement Project of Basic Ability for Young and Middle-aged Teachers in Guangxi Colleges and Universities (No. 2017KY0541), and Open Foundation for Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data Processing (No. 2017CSOBDP0301).

References

  1. 1.
    Cao, Y., et al.: An improved global best guided artificial bee colony algorithm for continuous optimization problems. Clust. Comput. 2018(2018), 1–9 (2018)Google Scholar
  2. 2.
    Cui, L., et al.: Modified Gbest-guided artificial bee colony algorithm with new probability model. Soft. Comput. 22(7), 2217–2243 (2018)CrossRefGoogle Scholar
  3. 3.
    Ning, J., et al.: A food source-updating information-guided artificial bee colony algorithm. Neural Comput. Appl. 30(3), 775–787 (2018)CrossRefGoogle Scholar
  4. 4.
    Bharti, K.K., Singh, P.K.: Chaotic gradient artificial bee colony for text clustering. Soft Comput. 20(3), 1113–1126 2016CrossRefGoogle Scholar
  5. 5.
    Liu, X., Wang, Y., Liu, H.: A hybrid genetic algorithm based on variable grouping and uniform design for global optimization. J. Comput. 28(3), 93–107 (2017)Google Scholar
  6. 6.
    Leung, Y.-W., Wang, Y.: Multiobjective programming using uniform design and genetic algorithm. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30(3), 293–304 (2000)CrossRefGoogle Scholar
  7. 7.
    Zhang, J., Wang, Y., Feng, J.: Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm. Sci. World J. 2013(2013), 1–16 (2013)Google Scholar
  8. 8.
    Dai, C., Wang, Y.: A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization. Appl. Soft Comput. 30(1), 238–248 (2015)CrossRefGoogle Scholar
  9. 9.
    Zhu, X., Zhang, J., Feng, J.: Multi-objective particle swarm optimization based on PAM and uniform design. Math. Probl. Eng. 2015(2), 1–17 (2015)Google Scholar
  10. 10.
    Jia, L., Wang, Y., Fan, L.: An improved uniform design-based genetic algorithm for multi-objective bilevel convex programming. Int. J. Comput. Sci. Eng. 12(1), 38–46 (2016)Google Scholar
  11. 11.
    Dai, C., Wang, Y.: A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization. Knowl. Based Syst. 85(1), 131–142 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jie Zhang
    • 1
  • Junhong Feng
    • 1
    Email author
  • Guoqiang Chen
    • 2
  • Xiani Yang
    • 1
  1. 1.School of Computer Science and Engineering, Guangxi Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulinChina
  2. 2.School of Computer and Information EngineeringHenan UniversityKaifengChina

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