Skip to main content
SpringerLink
Log in

Adaptive heterogeneous comprehensive learning particle swarm optimization with history information and dimensional mutation

  • 1222: Intelligent Multimedia Data Analytics and Computing
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In many multimedia systems, optimization problems tend to be multimodal, complex and high-dimensional. Although particle swarm optimization (PSO) algorithm has excellent performance in solving optimization problems, how to avoid premature convergence in complex and multimodal situations is the problem that need to be solved urgently. To overcome this problem, an adaptive heterogeneous comprehensive learning particle swarm optimization with history information and dimensional mutation (AHPSO) is proposed in this paper. In order to keep the population diversity, the whole population is divided into two subpopulations and particles’ information and knowledge are mined to provide adaptive strategy in both subpopulations. In exploitation subpopulation, an adaptive inertia weight (AIW) method is proposed according to the particles’ historical information. In exploration subpopulation, adaptive dimension mutation strategy (ADM) is introduced to improve the ability of the method to solve multimodal and complex problems in multimedia systems. Meanwhile, in order to increase particle diversity, dynamic-opposite learning (DOL) is used in exploration subpopulation. The exploration subpopulation does not learn from any particles in the exploitation subpopulation, so the information passing between subpopulations is one-way. The diversity in the exploration subpopulation can be maintained even if the exploitation subpopulation converges prematurely. In CEC 2013 test suite, in terms of Friedman test result, compared with traditional two swarm method, the solution accuracy of the proposed AHPSO in this paper is improved by 22.4 percentage points. The performance of AHPSO is compared with 8 peer variants and 8 other evolutionary algorithms on CEC2013 and CEC2017 test suites. Experimental results verify that AHPSO has a remarkable performance in complex and multimodal conditions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Not applicable.

Code availability

The code in this paper is private.

References

  1. Agrawal S, Singh RK, Singh UP, Jain S (2019) Biogeography particle swarm optimization based counter propagation network for sketch based-face recognition. Multimed Tools Appl 78(8):9801–9825. https://doi.org/10.1007/s11042-018-6542-z

    Article  Google Scholar 

  2. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  3. Awad N, Ali M, Liang J, Qu B, Suganthan (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Tech Rep

  4. Bo W, Xia XW, Yu F (2020) Multiple adaptive strategies-based particle swarm optimization algorithm. Swarm Evol Comput 57:100731. https://doi.org/10.1016/j.swevo.2020.100731

    Article  Google Scholar 

  5. Carrasco J, Garcia S, Rueda MM, Das S (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100665. https://doi.org/10.1016/j.swevo.2020.100665

    Article  Google Scholar 

  6. Chen YG, Li LX, Peng HP, Xiao JH (2017) Particle swarm optimizer with two differential mutations. Appl Soft Comput 61:314–330. https://doi.org/10.1016/j.asoc.2017.07.020

    Article  Google Scholar 

  7. Chen YG, Li LX, Xiao JH, Yang YX (2018) Particle swarm optimizer with crossover operation. Eng Appl Artif Intell 70:159–169. https://doi.org/10.1016/j.engappai.2018.01.009

    Article  Google Scholar 

  8. Chen K, Zhou FY, Lei Y, Wang SQ (2018) A hybrid particle swarm optimizer with sine cosine acceleration coefficients. Inf Sci 422:218–241. https://doi.org/10.1016/j.ins.2017.09.015

    Article  MathSciNet  Google Scholar 

  9. Chen K, Zhou FY, Liu A (2018) Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl-Based Syst 139:23–40. https://doi.org/10.1016/j.knosys.2017.10.011

    Article  Google Scholar 

  10. Chen K, Zhou FY, Yuan XF (2019) Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection. Expert Syst Appl 128:140–156. https://doi.org/10.1016/j.eswa.2019.03.039

    Article  Google Scholar 

  11. Dhanachandra N, Chanu YJ (2020) An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Multimed Tools Appl 79:25–26. https://doi.org/10.1007/s11042-020-08699-8

    Article  Google Scholar 

  12. Draa A, Bouzoubia S, Boukhalfa I (2015) A sinusoidal differential evolution algorithm for numerical optimisation. Appl Soft Comput 27:99–126. https://doi.org/10.1016/j.asoc.2014.11.003

    Article  Google Scholar 

  13. Du SY, Liu ZG (2020) Hybridizing particle swarm optimization with JADE for continuous optimization. Multimed Tools Appl 79:4619–4636. https://doi.org/10.1007/s11042-019-08142-7

    Article  Google Scholar 

  14. Eberhart R, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In proceedings of the 2001 ieee congress on evolutionary computation pp 94–100 https://doi.org/10.1109/CEC.2001.934376

  15. Elhoseny M, Sangaiah AK, Saemi B (2019) Extended genetic algorithm for solving open-shop scheduling problem. Soft Comput 13:5099–5116. https://doi.org/10.1007/s00500-018-3177-y

    Article  Google Scholar 

  16. Engelbrecht AP (2005) Fundamentals of computational swarm intelligence: 1. Wiley Chichester

  17. Engin O, Guclu A (2018) A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems. Appl Soft Comput 72:166–176. https://doi.org/10.1016/j.asoc.2018.08.002

    Article  Google Scholar 

  18. Gong YJ, Li JJ, Zhou Y (2017) Genetic learning particle swarm optimization. IEEE Trans Cybern 46(10):2277–2290. https://doi.org/10.1109/TCYB.2015.2475174

    Article  Google Scholar 

  19. Jain M, Maurya S, Rani A, Singh V (2018) Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J Intell Fuzzy Syst 34(3):1573–1582. https://doi.org/10.3233/JIFS-169452

    Article  Google Scholar 

  20. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  21. Katarya R, Verma OP (2017) Efficient music recommender system using context graph and particle swarm. Multimed Tools Appl 77(2):2673–2687. https://doi.org/10.1007/s11042-017-4447-x

    Article  Google Scholar 

  22. Kennedy E (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  23. Liang JJ, Qin AK, Suganthan PN (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295. https://doi.org/10.1109/TEVC.2005.857610

    Article  Google Scholar 

  24. Liang JJ, Qu BY, Suganthan (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Tech Rep. https://doi.org/10.1016/j.knosys.2017.10.011

  25. Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In proceedings of the IEEE swarm intelligence symposium, pp 124–129. https://doi.org/10.1109/CEC.2006.1688284

  26. Lin AP, Sun W, Yu HS, Wu GH (2009) Global genetic learning particle swarm optimization with diversity enhancement by ring topology. Swarm Evol Comput 44:571–583. https://doi.org/10.1016/j.swevo.2018.07.002

    Article  Google Scholar 

  27. Liu H, Zhang XW, Tu LP (2020) A modified particle swarm optimization using adaptive strategy. Expert Syst Appl 152:113353. https://doi.org/10.1007/978-981-10-8944-247

    Article  Google Scholar 

  28. Lynn N, Suganthan PN (2017) Ensemble particle swarm optimizer. Appl Soft Comput 55:533–548. https://doi.org/10.1016/j.asoc.2017.02.007

    Article  Google Scholar 

  29. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):255–262. https://doi.org/10.1109/tevc.2004.826074

    Article  Google Scholar 

  30. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  31. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  32. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  33. Molaei S, Moazen H, Ghabel SN (2020) Particle swarm optimization with an enhanced learning strategy and crossover operator. Knowl-Based Syst 215:106768. https://doi.org/10.1016/j.knosys.2021.106768

    Article  Google Scholar 

  34. Nandar L, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24. https://doi.org/10.1016/j.swevo.2015.05.002

    Article  Google Scholar 

  35. Qin AK, Huang PN, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417. https://doi.org/10.1109/TEVC.2008.927706

    Article  Google Scholar 

  36. Ratnaweera A, Halgamuge SK (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255. https://doi.org/10.1109/tevc.2004.826071

    Article  Google Scholar 

  37. Tanweer MR, Suresh S, Sundararajan N (2015) Self-regulating particle swarm optimization algorithm. Inf Sci 294:182–202. https://doi.org/10.1016/j.ins.2014.09.053

    Article  MathSciNet  MATH  Google Scholar 

  38. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In proceedings of international conference on computational intelligence for modeling control and automation, pp 695–701. https://doi.org/10.1109/cimca.2005.1631345

  39. Vitorino LN, Ribeiro SF (2015) A mechanism based on artificial bee Colony to generate diversity in particle swarm optimization. Neurocomputing 148:39–45. https://doi.org/10.1016/j.neucom.2013.03.076

    Article  Google Scholar 

  40. Walton S, Hassan O, Morgan K (2011) Modified cuckoo search: a new gradient free optimization algorithm. Chaos, Solitons Fractals 44(9):710–718. https://doi.org/10.1016/j.chaos.2011.06.004

    Article  Google Scholar 

  41. Wang SH, Li YZ, Yang HY (2019) Self-adaptive mutation differential evolution algorithm based on particle swarm optimization. Appl Soft Comput J 81:105496. https://doi.org/10.1016/j.asoc.2019.105496

    Article  Google Scholar 

  42. Wang F, Zhang H, Li KS, Lin ZY (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci 436:162–177. https://doi.org/10.1016/j.ins.2018.01.027

    Article  MathSciNet  Google Scholar 

  43. Wei HL, Nor AMI (2014) An adaptive two-layer particle swarm optimization with elitist learning strategy. Inf Sci 273:49–72. https://doi.org/10.1016/j.ins.2014.03.031

    Article  MathSciNet  Google Scholar 

  44. Xia X, Gui L, He G (2019) An expanded particle swarm optimization based on multi-exemplar and forgetting ability. Inf Sci 508:105–120. https://doi.org/10.1016/j.ins.2019.08.065

    Article  MathSciNet  MATH  Google Scholar 

  45. Xia X, Gui L, Zhan Z (2018) A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Appl Soft Comput 67:126–140. https://doi.org/10.1016/j.asoc.2018.02.042

    Article  Google Scholar 

  46. Xia X, Xing Y, Wei B (2018) A fitness-based multi-role particle swarm optimization. Swarm Evol Comput 44:349–364. https://doi.org/10.1016/j.swevo.2018.04.006

    Article  Google Scholar 

  47. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958. https://doi.org/10.1109/TEVC.2009.2014613

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Key R&D Program of China (2019YFF0302203), the National Natural Science Foundation of China (61973067, 61903071).

Funding

This work was supported by the National Key R&D Program of China (2019YFF0302203), the National Natural Science Foundation of China (61973067), and the Open Research Fund from the State Key Laboratory of Rolling and Automation, Northeastern University(2019RALKFKT004).

Author information

Authors and Affiliations

  1. Information Science and Engineering, Northeastern University, No.11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People’s Republic of China

    Xu Yang, Hongru Li & Xia Yu

Authors
  1. Xu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongru Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xia Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xu Yang: Conceptualization, Methodology, Software, Validation, Writing original draft.

Hongru Li: Supervision, Writing-review & editing.

Xia Yu: Writing-review & editing.

Corresponding author

Correspondence to Hongru Li.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 26 kb)

Appendix A

Appendix A

Table 9 Results of peer algorithms on CEC2017 test suite (D = 30)
Full size table
Table 10 Results of other evolutionary algorithms on CEC2017 test suite (D = 30)
Full size table
Table 11 Friedman-test of other evolutionary algorithms on CEC2017 test suite
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., Li, H. & Yu, X. Adaptive heterogeneous comprehensive learning particle swarm optimization with history information and dimensional mutation. Multimed Tools Appl 82, 9785–9817 (2023). https://doi.org/10.1007/s11042-022-13044-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13044-2

Keywords

Search

Navigation