Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

IBEA-SVM: An Indicator-based Evolutionary Algorithm Based on Pre-selection with Classification Guided by SVM

  • Published:
Applied Mathematics-A Journal of Chinese Universities Aims and scope Submit manuscript

Abstract

Multi-objective optimization has many important applications and becomes a challenging issue in applied science. In typical multi-objective optimization algorithms, such as Indicator-based Evolutionary Algorithm (IBEA), all of parents and offspring need to be evaluated in every generation, and then the better solutions of them are selected as the next generation candidates. This leads to a large amount of calculation and slows down convergence rate for IBEA related applications. Our discovery is that the evaluation of evolutionary algorithm is a binary classification in nature and a meaningful preselection method will accelerate the convergence rate. Therefore this paper presents a novel preselection approach to improve the performance of the IBEA, in which a SVM (Support Vector Machine) classifier is adopted to sort the promising solutions from unpromising solutions and then the newly generated solutions are conversely added as train sample to increase the accuracy of the classifier. Firstly, we proposed an online and asynchronous training method for SVM model with empirical kernel. The initial population is randomly generated among population size, which is used as initial training. In the process of training, SVM classifier is modified and perfected to adapt to the evolutionary algorithm sample. Secondly, the classifier divides all the new generated solutions from the whole solution spaces into promising solutions and unpromising ones. And only the promising ones are forwarded for evaluation. In this way, the evaluation time can be greatly reduced and the solution quality can be obviously improved. Thirdly, the promising and unpromising solutions are labeled as new train samples in next generation to refine classifier model. A number of experiments on benchmark functions validates the proposed approach. The results show that IBEA-SVM can significantly outperform previous works.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bader J, Zitzler E. HypE: An algorithm for fast hypervolume–based many–objective optimization, Evolutionary Computation, 2011, 19(1): 45–76.

    Article  Google Scholar 

  2. Bandaru S, Ng A, Deb K. On the performance of classification algorithms for learning pareto–dominance relations, Evolutionary Computation (CEC), 2014, pp 1139–1146, IEEE.

    Google Scholar 

  3. Barnhart C, Johnson E, Jin Y. Branch–and–Price: Column Generation for Solving Huge Integer Programs, Operations Research, 1998, 46(3): 316–329.

    Article  MathSciNet  MATH  Google Scholar 

  4. Brockhoff D, Zitzler E. Improving hypervolume–based multiobjective evolutionary algorithms by using objective reduction methods, Evolutionary Computation (CEC), 2007, pp 2086–2093, IEEE.

    Google Scholar 

  5. Chang C, Lin C. LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

  6. Chen Y, He F, Wu Y, et al. A local start search algorithm to compute exac. Hausdorff Distance for arbitrary point sets, Pattern Recognition, 2017, 67: 139–148.

    Article  Google Scholar 

  7. Chen X, He F, Yu H. A Matting Method Based on Full Feature Coverage, Multimedia Tools and Applications, DOI:10.1007/s11042–018–6690–1.

  8. Coello C, Lamont G, Veldhuizen D. Evolutionary algorithms for solving multi–objective problems, 2007, New York: Springer.

    MATH  Google Scholar 

  9. Coello C. Twenty Years of Evolutionary Multi–Objective Optimization: A Historical View of the Field, IEEE Computational Intelligence Magazine, 2006, 1(1): 28–36.

    Article  MathSciNet  Google Scholar 

  10. Corne D W, Jerram N R, Knowles J D, et al. PESA–II: region–based selection in evolution–ary multiobjective optimization, Conference on Genetic and Evolutionary Computation, Morgan Kaufmann Publishers Inc, 2001: 283–290.

    Google Scholar 

  11. Das S, Suganthan P. Differential evolution: A survey of the state–of–the–art, IEEE transactions on evolutionary computation, 2011, 15(1), 4–31.

    Google Scholar 

  12. Deb K, Thiele L, Laumanns M, et al. Scalable multi–objective optimization test problems, Evolu–tionary Computation, 2002, CEC'02, Proceedings of the 2002 Congress on (Vol 1, pp 825–830), IEEE.

    Google Scholar 

  13. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA–II, IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197.

    Article  Google Scholar 

  14. Fan Z, Hu K, Li F. Multi–objective evolutionary algorithms embedded with machine learning A survey, Evolutionary Computation (CEC), 2016, pp 1262–1266, IEEE.

    Google Scholar 

  15. Gui W, Zhang H. Asymptotic properties and expectation–maximization algorithm for maximum likelihood estimates of the parameters from Weibull–Logarithmic model, Applied Mathematics–A Journal of Chinese Universities, 2016, 31(4): 425–438.

    Article  MathSciNet  MATH  Google Scholar 

  16. Kim W, Xiong S, Liang Z. Effect of Loading Symbol of Online Video on Perception of Waiting Time, International Journal of Human–Computer Interaction, 2017(5).

    Google Scholar 

  17. Konak A, Coit W. Multi–objective optimization using genetic algorithms: A tutorial, Reliability Engineering System Safety, 2006, 91(9): 992–1007.

    Article  Google Scholar 

  18. Laumanns M. SPEA2. Improving the Strength Pareto Evolutionary Algorithm, Technical Report Gloriastrasse, 2001.

    Google Scholar 

  19. Li K, He F, Yu H, et al. A parallel and robust object tracking approach synthesizing adap–tive bayesian learning and improved incremental subspace learning, Frontiers Comput Sci, DOI: 10.1007/s11704–018–6442–4.

  20. Li K, He F Z, Yu H P. Robust Visual Tracking based on convolutional features with illumination and occlusion handing, Journal of Computer Science and Technology, 2018, 33(1): 223–236.

    Article  Google Scholar 

  21. Li K, He F, Yu H, et al. A correlative classifiers approach based on particle filter and sample set for tracking occluded target, Applied Mathematics–A Journal of Chinese Universities, 2017, 32(3): 294–312.

    Article  MathSciNet  Google Scholar 

  22. Li W, Mcmahon C. A Simulated Annealing–based Optimization Approach for Integrated Process Planning and Scheduling, International Journal of Computer Integrated Manufacturing, 2007, 20(1): 80–95.

    Article  Google Scholar 

  23. Lin X, Zhang Q, Kwongs S. A decomposition based multiobjective evolutionary algorithm with classification, IEEE Congress on Evolutionary Computation, 2016, pp 3292–3299, IEEE.

    Google Scholar 

  24. Lv X, He F, Cai W, et al. A string–wise CRDT algorithm for smart and large–scale collaborative editing systems, Advanced Engineering Informatics, 2017, 33: 397–409.

    Google Scholar 

  25. Lv X, He F, Cai W, et al. Supporting selective undo of string–wise operations for collaborative editing systems, Future Generation Computer Systems, 2018, 82: 41–62.

    Article  Google Scholar 

  26. Lv X, He F, Cheng Y et al. A novel CRDT–based synchronization method for real–time collabo–rative CA. Systems, Advanced Engineering Informatics, 2018, 38: 381–391.

    Article  Google Scholar 

  27. Ni B, He F, Pan Y, et al. Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer–aided therapy, Applied Mathematics–A Journal of Chinese Universities, 2016, 31(1): 37–52.

    Article  MathSciNet  MATH  Google Scholar 

  28. Sun J, He F, Chen Y, Chen X et al. A multiple template approach for robust tracking of fast motion target, Applied Mathematics–A Journal of Chinese Universities, 2016, 31(2): 177–197.

    Article  MathSciNet  MATH  Google Scholar 

  29. Tao Q, Zhang M. Mathematical theory of signal analysis vs complex analysis method of harmonic analysis, Applied Mathematics–A Journal of Chinese Universities, 2013, 28(4): 505–530.

    Article  MATH  Google Scholar 

  30. Trivedi A, Srinivasan D, Sanyal K, Ghosh A. A Survey of Multiobjective Evolutionary Algorithms based on Decomposition, IEEE Transactions on Evolutionary Computation, 2017, 21(3): 440–462.

    Google Scholar 

  31. Van L, Melab N, Talbi E. Gpu computing for parallel local search metaheuristic algorithms, Journal of Supercomputing, 2016, 72(6): 2394–2416.

    Article  MATH  Google Scholar 

  32. Wang S, Lu X, Li X, Li W. A systematic approach of process planning and scheduling optimization for sustainable machining, Journal of Cleaner Production, 2015, 87(1): 914–929.

    Article  Google Scholar 

  33. Wu Y, He F, Zhang D, et al. Service–oriented feature–based data exchange for cloud–based design and manufacturing, IEEE Transactions on Services Computing, 2018,11: 341–353.

    Article  Google Scholar 

  34. Xiong S, Zhao J, et al. A computer–aided design system for foot–feature–based shoe last customiza–tion, International Journal of Advanced Manufacturing Technology, 2010, 46(1–4): 11–19.

    Article  Google Scholar 

  35. Yan X, He F, Chen Y. A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization, Journal of Computer Science and Technology, 2017, 32(2): 340–355.

    Article  MathSciNet  Google Scholar 

  36. Yan X, He F, Hou N, et al. An effcient particle swarm optimization for large–scale hard–ware/software co–design system, International Journal of Cooperative Information Systems, 2018, 27(1): 1741001.

    Article  Google Scholar 

  37. Yu H, He F, Pan Y, et al. A novel region–based active–contour model via local patch similarity measure for image segmentation, Multimedia Tools and Appliations, 2018, 77(18), 24097–24119.

    Article  Google Scholar 

  38. Yu H, He F, Pan Y. A Novel Segmentation Model for Medical Images with Intensity Inhomogeneity Based on Adaptive Perturbation, Multimedia Tools and Applications, DOI: 10.1007/s11042–018–6735–5.

  39. Zhang D J, He F Z, Han S H, et al. Quantitative optimization of interoperability during feature–based data exchange, Integrated Computer–Aided Engineering, 2016, 23(1): 31–50.

    Article  Google Scholar 

  40. Zhang D, He F, Han S, et al. An effcient approach to directly compute the exact Hausdorff distance for 3D point sets, Integrated Computer–Aided Engineering, 2017, 24(3): 261–277.

    Article  Google Scholar 

  41. Zhang J, Zhou A, Zhang G. A classification and Pareto domination based multiobjective evolu–tionary algorithm, Evolutionary Computation, 2015, pp 2883–2890, IEEE.

    Google Scholar 

  42. Zhang Q, Zhou A, Zhao S. Multiobjective optimization test instances for the CEC 2009 spe–cial session and competition, University o. Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi–objective optimization algorithms, technical report, 2008, 264.

    Google Scholar 

  43. Zhang Q, Li H. MOEA/D:A Multiobjective Evolutionary Algorithm Based on Decomposition, IEEE transactions on evolutionary computation, 2007, 11(6): 712–731.

    Google Scholar 

  44. Zhang S, He F, Ren W, Yao J. Joint learning of image detail and transmission map for single image dehazing, The Visual Computer, DOI 10.1007/s00371–018–1612–9.

  45. Zhou Y, He F, Qiu Y. Optimization of parallel iterated local search algorithms on graphics pro–cessing unit, Journal of Supercomputing, 2016, 72(6): 2394–2416.

    Article  Google Scholar 

  46. Zhou Y, He F, Qiu Y. Dynamic Strategy based Parallel Ant Colony Optimization on GPUs for TSPs, Science China Information Sciences, 2017, 60(6): 068–102.

    Article  Google Scholar 

  47. Zhou Y, He F, Qiu Y. Parallel ant colony optimization on multi–core simd cpus, Future Genera–tion Computer Systems, 2018, 79(2): 473–487.

    Article  Google Scholar 

  48. Zhu H. Avoiding Con icts by Group Role Assignment, IEEE Trans on Systems, Man, and Cy–bernetics: Systems, 2016, 46(4): 535–547.

    Google Scholar 

  49. Zhu H. Role–Based Collaboration and the E–CARGO: Revisiting the Developments of the Last Decade, IEEE Systems, Man, and Cybernetics Magazine, 2015, 1(3): 27–35.

    Article  Google Scholar 

  50. Zhu H, Zhou M. Role–Based Collaboration and its Kernel Mechanisms, IEEE Trans on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2006, 36(4): 578–589.

    Google Scholar 

  51. Zitzler E, Kunzli S. Indicator–Based Selection in Multiobjective Search, Lecture Notes in Com–puter Science, 2004, 3242: 832–842.

    Article  Google Scholar 

  52. Zitzler E, Thiele L. Multiobjective optimization using evolutionary algorithms A comparative case study, Int Conf Parallel Problem Solving from Nature (PPSN–V), 1998, 1498(3): 292–301.

    Article  Google Scholar 

  53. Zitzler E, Thiele L, Laumanns M. Performance assessment of multiobjective optimizers: An analysis and review, IEEE Transactions on Evolutionary Computation, 2003, 7(2): 117–132.

    Article  Google Scholar 

  54. Zitzler E, Knzli S. Indicator–Based Selection in Multiobjective Search, 2004 Parallel Problem Solving from Nature.

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University, Wuhan, China

    Hao-ran Li, Fa-zhi He & Xiao-hu Yan

  2. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University, Wuhan, 430074, China

    Fa-zhi He

Authors
  1. Hao-ran Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fa-zhi He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-hu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fa-zhi He.

Additional information

Supported by the National Science Foundation of China (Grant No. 61472289) and Hubei Province Science Foundation (Grant No. 2015CFB254).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Hr., He, Fz. & Yan, Xh. IBEA-SVM: An Indicator-based Evolutionary Algorithm Based on Pre-selection with Classification Guided by SVM. Appl. Math. J. Chin. Univ. 34, 1–26 (2019). https://doi.org/10.1007/s11766-019-3706-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11766-019-3706-1

Keywords

MR Subject Classification

Search

Navigation