SpringerLink
Log in

Multi-objectives optimal model of heavy equipment using improved Strength Pareto Evolutionary Algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The problem of injection molding machine’s multi-objective optimization is very important. A triple-objective optimization model with the largest mould moving speed and injecting capacities and the smallest injecting power has been created. The optimized design constraints of the optimal model are summarized. The computational efficiency of Strength Pareto Evolutionary Algorithm (SPEA) is improved by using rough set-based support vector clustering method. The number of external stocks is reduced. The optimal Pareto solution is determined by eliminating the uncertainty in the artificial priority election. The multi-objective optimization of the HT1600X1N injection molding machine is taken as an example. The SPEA-RSVC-II which is the mixed algorithm of Strength Pareto Evolutionary Algorithm and Ro′ugh-based Support Vector Clustering is applied. It shows that the new method could accelerate the population clustering operation effectively and improves the efficiency of optimized calculation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Knowles J, Corne D (2003) Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Trans Evol Comput 7(2):100–116. doi:10.1109/TEVC.2003.810755

    Article  Google Scholar 

  2. Debasis S, Jayant M (2005) Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm. Chem Eng Sci 60(3):481–492

    Google Scholar 

  3. Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multi-objective optimization. Evol Comput 3(1):1–16. doi:10.1162/evco.1995.3.1.1

    Article  Google Scholar 

  4. Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. doi:10.1162/evco.1994.2.3.221

    Article  Google Scholar 

  5. Deb K, Pratap A, Agarwal S (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. doi:10.1109/4235.996017

    Article  Google Scholar 

  6. Horn J, Nafpliotis N, Goldberg DE (1994) A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 11 Piscataway, New Jersey

  7. Corne DW, Knowles JD, Oates MJ (2000) The Pareto Envelopebased Selection Algorithm for Multiobjective Optimization. In: Proceedings of the Parallel Problem Soving from Nature VI Conference. Paris, France

  8. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271. doi:10.1109/4235.797969

    Article  Google Scholar 

  9. Lun GC, Chueh CH (2004) Multi-objective optimal design of truss structure with immune algorithm. Comput Struc 82(11–12):829–844. doi:10.1016/j.compstruc.2004.03.003

    Google Scholar 

  10. Liu YM, Wang CJ (1999) A modified genetic algorithm based optimisation of milling parameters. Int J Adv Manuf Technol 18(15):796–799. doi:10.1007/s001700050134

    Article  Google Scholar 

  11. Wang N (1993) Multiple-objective optimization of machining operations based on neural networks. Int J Adv Manuf Technol 12(8):235–243. doi:10.1007/BF01748633

    Article  Google Scholar 

  12. Jerald J, Asokan P, Prabaharan G, Saravanan R (2005) Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int J Adv Manuf Technol 24(25):964–971. doi:10.1007/s00170-003-1933-2

    Article  Google Scholar 

  13. Jaewook L, Daewon L (2005) An improved cluster labeling method for support vector clustering. IEEE Trans Pattern Anal Mach Intell 27(3):461–464. doi:10.1109/TPAMI.2005.47

    Article  Google Scholar 

  14. Wigotsky V (2001) Injection molding machinery. Plastics Eng 57(12):342–371

    Google Scholar 

  15. Schlottmann F, Seese D (2004) Financial applications of multi-objective evolutionary algorithms: Recent developments and future research directions. In: Coello CAC, Lamont GB (eds) Applications of multi-objective evolutionary algorithms. World Scientific, Singapore

    Google Scholar 

  16. Sankar SS, Ponnambalam SG, Gurumarimuthu M (2006) Scheduling flexible manufacturing systems using parallelization of multi-objective evolutionary algorithms. Int J Adv Manuf Technol 30(3–4):279–285. doi:10.1007/s00170-005-0045-6

    Article  Google Scholar 

  17. Knowles JD, Corne DW (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evol Comput 8(2):149–172. doi:10.1162/106365600568167

    Article  Google Scholar 

  18. Kitayama S, Arakawa M, Yamazaki K (2006) Penalty function approach for the mixed discrete nonlinear problems by particle swarm optimization. Struct Multidisciplinary Optim 32(3):191–202. doi:10.1007/s00158-006-0021-2

    Article  MathSciNet  Google Scholar 

  19. Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evol Comput 10(3):315–329. doi:10.1109/TEVC.2005.857073

    Article  Google Scholar 

  20. Laumanns M, Thiele L, Deb K (2002) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10(3):263–282. doi:10.1162/106365602760234108

    Article  Google Scholar 

  21. Purshouse RC (2003) On t he Evolutionary Optimisation of Many Objectives: [PhD thesis]. Sheffield, U K:Department of Automatic Control and Systems Engineering, The University of Sheffield

  22. Hirani H (2004) Multiobjective optimization of a journal bearing using the Pareto optimality concept. J Eng Tribology 218(4):323–336

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Technology Department, Sany Heavy Equipment Co., LTD, 110027, Shenyang, China

    Zhe Wei

  2. General Office, Hangzhou Municipal Electric Power Burear Chengxi Branch, 310024, Hangzhou, China

    Dandan Yang

  3. College of Management, Zhejiang University, 310027, Hangzhou, China

    **aoyi Wang

  4. School of Computer Engineering, Qingdao Technological University, Qingdao, 266033, China

    **long Wang

Authors
  1. Zhe Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dandan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **aoyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. **long Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhe Wei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, Z., Yang, D., Wang, X. et al. Multi-objectives optimal model of heavy equipment using improved Strength Pareto Evolutionary Algorithm. Int J Adv Manuf Technol 45, 389–396 (2009). https://doi.org/10.1007/s00170-009-1962-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-1962-6

Keywords

Search

Navigation