Skip to main content
SpringerLink
Log in

An Effective Genetic Algorithm with Uniform Crossover for Bi-objective Unconstrained Binary Quadratic Programming Problem

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

  • 1888 Accesses

Abstract

The unconstrained binary quadratic programming problem is one of the most studied NP-hard problem with its various practical applications. In this paper, we propose an effective multi-objective genetic algorithm with uniform crossover for solving bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the uniform crossover within the hypervolume-based multi-objective optimization framework for further improvements. The computational studies on 10 benchmark instances reveal that the proposed algorithm is very effective in comparison with the original multi-objective optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.tik.ee.ethz.ch/pisa/assessment.html.

References

  1. Alidaee, B., Kochenberger, G.A., Ahmadian, A.: 0–1 quadratic programming approach for the optimal solution of two scheduling problems. Int. J. Syst. Sci. 25, 401–408 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  2. Alkhamis, T.M., Hasan, M., Ahmed, M.A.: Simulated annealing for the unconstrained binary quadratic pseudo-Boolean function. Eur. J. Oper. Res. 108, 641–652 (1998)

    Article  MATH  Google Scholar 

  3. Amini, M., Alidaee, B., Kochenberger, G.: A scatter search approach to unconstrained quadratic binary programs. New Meth. Optim. 108, 317–330 (1999)

    Google Scholar 

  4. Basseur, M., Liefooghe, A., Le, K., Burke, E.: The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems. J. Heuristics 18(2), 263–296 (2012)

    Article  Google Scholar 

  5. Basseur, M., Zeng, R.-Q., Hao, J.-K.: Hypervolume-based multi-objective local search. Neural Comput. Appl. 21(8), 1917–1929 (2012)

    Article  Google Scholar 

  6. Benlic, U., Hao, J.-K.: Memetic search for the quadratic assignment problem. Expert Syst. Appl. 42, 584–595 (2015)

    Article  Google Scholar 

  7. Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer, New York (2007)

    MATH  Google Scholar 

  8. Gallo, G., Hammer, P.: Quadratic knapsack problems. Math. Program. 12, 132–149 (1980)

    MathSciNet  MATH  Google Scholar 

  9. Glover, F., Kochenberger, G., Alidaee, B.: Adaptive memory tabu search for binary quadratic programs. Manag. Sci. 44, 336–345 (1998)

    Article  MATH  Google Scholar 

  10. Hu, X.-B., Paolo, E.D.: An efficient genetic algorithm with uniform crossover for the multi-objective airport gate assignment problem. Multi Objective Memetic Algorithm 171, 71–89 (2009)

    Article  MATH  Google Scholar 

  11. Kochenberger, G., Hao, J.-K., Glover, F., Lewis, M., Lü, Z., Wang, H., Wang, Y.: The unconstrained binary quadratic programming problem: a survey. J. Comb. Optim. 28, 58–81 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Krarup, J.: Computer aided layout design. Math. Program. Study 9, 75–94 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  13. Liefooghe, A., Verel, S., Hao, J.-K.: A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Appl. Soft Comput. 16, 10–19 (2014)

    Article  Google Scholar 

  14. Liefooghe, A., Verel, S., Paquete, L., Hao, J.-K.: Experiments on local search for bi-objective unconstrained binary quadratic programming. In: Proceedings of the 8th International Conference on Evolutionary Multi-criterion Optimization (EMO 2015), Guimarães, Portugal, pp. 171–186 (2015)

    Google Scholar 

  15. Lü, Z., Hao, J.-K., Glover, F.: A study of memetic search with multi-parent combination for UBQP. In: Prodeedings of the 10th International Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2010), Istanbul, Turkey, pp. 154–165 (2010)

    Google Scholar 

  16. McBride, R.D., Yormark, J.S.: An implicit enumeration algorithm for quadratic integer programming. Manag. Sci. 26, 282–296 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  17. Merz, P., Katayama, K.: Memetic algorithms for the unconstrained binary quadratic programming problem. BioSystems 78, 99–118 (2004)

    Article  Google Scholar 

  18. Wang, Y., Lü, Z., Hao, J.-K.: A study of multi-parent crossover operators in a memetic algorithm. In: Prodeedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN XI), Krakow, Poland, pp. 556–565 (2010)

    Google Scholar 

  19. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. Evol. Comput. 3, 257–271 (1999)

    Article  Google Scholar 

Download references

Acknowledgments

The work in this paper was supported by the Fundamental Research Funds for the Central Universities (Grant No. A0920502051408-25), supported by the Research Foundation for International Young Scientists of China (Grant No. 61450110443), supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars (Grant No. 2015S03007) and supported by National Natural Science Foundation of China (Grant No. 61370150 and 71501157).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, People’s Republic of China

    Chao Huo & Rong-Qiang Zeng

  2. School of Mathematics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, People’s Republic of China

    Rong-Qiang Zeng

  3. School of Management, Northwestern Polytechnical University, Xi’an, 710072, Shanxi, People’s Republic of China

    Yang Wang

  4. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, People’s Republic of China

    Ming-Sheng Shang

Authors
  1. Chao Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong-Qiang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming-Sheng Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong-Qiang Zeng .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Huo, C., Zeng, RQ., Wang, Y., Shang, MS. (2016). An Effective Genetic Algorithm with Uniform Crossover for Bi-objective Unconstrained Binary Quadratic Programming Problem. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46257-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation