Skip to main content
SpringerLink
Log in

A Brief Overview of the Synergy Between Metaheuristics and Unconventional Dynamics

  • Conference paper
  • First Online:
AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application (AETA 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 554))

Abstract

This brief review paper focuses on the modern and original hybridization of the unconventional dynamics and the metaheuristic optimization algorithms. It discusses the concept of chaos-based optimization in general, i.e. the influence of chaotic sequences on the population diversity as well as at the metaheuristics performance. Further, the non-random processes used in evolutionary algorithms, and finally also the examples of the evolving complex network dynamics as the unconventional tool for the visualization and analysis of the population in popular optimization metaheuristics. This work should inspire the researchers for applying such methods and take advantage of possible performance improvements for the optimization tasks.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution–an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  2. Engelbrecht, A.P.: Heterogeneous particle swarm optimization. In: International Conference on Swarm Intelligence, pp. 191–202. Springer, Heidelberg, September 2010

    Google Scholar 

  3. Zelinka, I.: SOMA—self-organizing migrating algorithm. In: Self-Organizing Migrating Algorithm, pp. 3–49. Springer, Cham (2016)

    Google Scholar 

  4. Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)

    Article  Google Scholar 

  5. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: International Conference in Swarm Intelligence, pp. 355–364. Springer, Heidelberg, June 2010

    Google Scholar 

  6. Droste, S., Jansen, T., Wegener, I.: Perhaps not a free lunch but at least a free appetizer. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 1, pp. 833–839. Morgan Kaufmann Publishers Inc., July 1999

    Google Scholar 

  7. Piotrowski, A.P., Napiorkowski, J.J.: Step-by-step improvement of JADE and SHADE-based algorithms: success or failure? Swarm Evol. Comput. 43, 88–108 (2018)

    Google Scholar 

  8. Piotrowski, A.P., Napiorkowski, J.J.: Some metaheuristics should be simplified. Inf. Sci. 427, 32–62 (2018)

    Article  MathSciNet  Google Scholar 

  9. Senkerik, R., Zelinka, I., Pluhacek, M.: Chaos-based optimization-a review. J. Adv. Eng. Comput. 1(1), 68–79 (2017)

    Article  Google Scholar 

  10. Zelinka, I., Lampinen, J., Senkerik, R., Pluhacek, M.: Investigation on evolutionary algorithms powered by nonrandom processes. Soft. Comput. 22(6), 1791–1801 (2018)

    Article  Google Scholar 

  11. Senkerik, R., Zelinka, I., Pluhacek, M., Viktorin, A.: Study on the development of complex network for evolutionary and swarm based algorithms. In: Mexican International Conference on Artificial Intelligence, pp. 151–161. Springer, Cham, October 2016

    Google Scholar 

  12. Senkerik, R., Viktorin, A., Pluhacek, M., Kadavy, T.: Population diversity analysis for the chaotic based selection of individuals in differential evolution. In: International Conference on Bioinspired Methods and Their Applications, pp. 283–294. Springer, Cham, May 2018

    Google Scholar 

  13. Weber, M., Neri, F., Tirronen, V.: A study on scale factor in distributed differential evolution. Inf. Sci. 181(12), 2488–2511 (2011)

    Article  Google Scholar 

  14. Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf. Sci. 181(12), 2469–2487 (2011)

    Article  MathSciNet  Google Scholar 

  15. Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm Evol. Comput. 25, 72–99 (2015)

    Article  Google Scholar 

  16. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7(3), 289–304 (2003)

    Article  Google Scholar 

  17. Coelho, L.d.S, Mariani, V.C.: A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch. Chaos, Solitons Fractals 39(2), 510–518 (2009)

    Article  Google Scholar 

  18. Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Comput. Math Appl. 60(4), 1088–1104 (2010)

    Article  Google Scholar 

  19. Zhenyu, G., Bo, C., Min, Y., Binggang, C.: Self-adaptive chaos differential evolution. In: International Conference on Natural Computation, pp. 972–975. Springer, Heidelberg, September 2006

    Google Scholar 

  20. Ozer, A.B.: CIDE: chaotically initialized differential evolution. Expert Syst. Appl. 37(6), 4632–4641 (2010)

    Article  Google Scholar 

  21. Pluhacek, M., Senkerik, R., Davendra, D.: Chaos particle swarm optimization with Eensemble of chaotic systems. Swarm Evol. Comput. 25, 29–35 (2015)

    Article  Google Scholar 

  22. Pluhacek, M., Senkerik, R., Davendra, D., Oplatkova, Z.K., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput. Math Appl. 66(2), 122–134 (2013)

    Article  MathSciNet  Google Scholar 

  23. Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T.: Chaos-enhanced multiple-choice strategy for particle swarm optimisation. Int. J. Parallel Emergent Distrib. Syst. 1–14 (2018)

    Google Scholar 

  24. Metlicka, M., Davendra, D.: Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems. Swarm Evol. Comput. 25, 15–28 (2015)

    Article  Google Scholar 

  25. Davendra, D., Bialic-Davendra, M., Senkerik, R.: Scheduling the lot-streaming flowshop scheduling problem with setup time with the chaos-induced enhanced differential evolution. In: 2013 IEEE Symposium on Differential Evolution (SDE), pp. 119–126. IEEE, April 2013

    Google Scholar 

  26. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    Article  MathSciNet  Google Scholar 

  27. Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S., Wang, H.: Chaotic krill herd algorithm. Inf. Sci. 274, 17–34 (2014)

    Article  MathSciNet  Google Scholar 

  28. Zhang, C., Cui, G., Peng, F.: A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis. Appl. Thermal Eng. 104, 707–719 (2016)

    Article  Google Scholar 

  29. Jordehi, A.R.: Chaotic bat swarm optimisation (CBSO). Appl. Soft Comput. 26, 523–530 (2015)

    Article  Google Scholar 

  30. Wang, G.G., Deb, S., Gandomi, A.H., Zhang, Z., Alavi, A.H.: Chaotic cuckoo search. Soft. Comput. 20(9), 3349–3362 (2016)

    Article  Google Scholar 

  31. Coelho, L.d.S., Ayala, H.V.H., Mariani, V.C.: A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization. Appl. Math. Comput. 234(0), 452–459 (2014)

    Google Scholar 

  32. Coelho, L.d.S., Pessôa, M.W.: A tuning strategy for multivariable PI and PID controllers using differential evolution combined with chaotic Zaslavskii map. Expert Syst. Appl. 38(11), 13694–13701 (2011)

    Google Scholar 

  33. Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, Oxford (2003)

    Google Scholar 

  34. Senkerik, R., Pluhacek, M., Zelinka, I., Davendra, D., Janostik, J.: Preliminary study on the randomization and sequencing for the chaos embedded heuristic. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, pp. 591–601. Springer, Cham (2016)

    Google Scholar 

  35. Senkerik, R., Pluhacek, M., Viktorin, A., Kadavy, T., Oplatkova, Z.K.: Randomization of individuals selection in differential evolution. In: 23rd International Conference on Soft Computing, pp. 180–191. Springer, Cham, June 2017

    Google Scholar 

  36. Senkerik, R., Pluhacek, M., Zelinka, I., Viktorin, A., Oplatkova, Z.K.: Hybridization of multi-chaotic dynamics and adaptive control parameter adjusting jDE strategy. In: International Conference on Soft Computing-MENDEL, pp. 77–87. Springer, Heidelberg, June 2016

    Google Scholar 

  37. Senkerik, R., Pluhacek, M., Oplatkova, Z.K., Davendra, D.: On the parameter settings for the chaotic dynamics embedded differential evolution. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1410–1417. IEEE, May 2015

    Google Scholar 

  38. Viktorin, A., Pluhacek, M., Senkerik, R.: Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4797–4803. IEEE, July 2016

    Google Scholar 

  39. Senkerik, R., Pluhacek, M., Oplatkova, Z.K., Davendra, D., Zelinka, I.: Investigation on the differential evolution driven by selected six chaotic systems in the task of reactor geometry optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 3087–3094. IEEE, June 2013

    Google Scholar 

  40. Senkerik, R., Zelinka, I., Pluhacek, M., Davendra, D., Oplatková Kominkova, Z.: Chaos enhanced differential evolution in the task of evolutionary control of selected set of discrete chaotic systems. Sci. World J. (2014)

    Google Scholar 

  41. Skanderova, L., Řehoř, A.: Comparison of pseudorandom numbers generators and chaotic numbers generators used in differential evolution. In: Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, pp. 111–121. Springer, Cham (2014)

    Google Scholar 

  42. Krömer, P., Zelinka, I., Snášel, V.: Can deterministic chaos improve differential evolution for the linear ordering problem? In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1443–1448. IEEE, July 2014

    Google Scholar 

  43. Hamaizia, T., Lozi, R.: Improving chaotic optimization algorithm using a new global locally averaged strategy. In: Emergent Properties in Natural and Artificial Complex Systems, pp. pp-17, September 2011

    Google Scholar 

  44. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T.: Modified progressive random walk with chaotic PRNG. Int. J. Parallel Emergent Distrib. Syst. 1–10 (2017)

    Google Scholar 

  45. Awad, N.H., Ali, M.Z., Suganthan, P.N.: Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction. Swarm Evol. Comput. 39, 141–156 (2018)

    Article  Google Scholar 

  46. Chen, G., Zelinka, I.: Evolutionary Algorithms, Swarm Dynamics and Complex Networks (2018)

    Google Scholar 

  47. Senkerik, R., Pluhacek, M., Viktorin, A., Kadavy, T., Janostik, J., Oplatková, Z.K.: A review on the simulation of social networks inside heuristic algorithms. In: ECMS, pp. 176–182 (2018)

    Google Scholar 

  48. Skanderova, L., Fabian, T., Zelinka, I.: Small-world hidden in differential evolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3354–3361. IEEE, July 2016

    Google Scholar 

  49. Zelinka, I., Davendra, D., Lampinen, J., Senkerik, R., Pluhacek, M.: Evolutionary algorithms dynamics and its hidden complex network structures. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3246–3251. IEEE, July 2014

    Google Scholar 

  50. Davendra, D., Zelinka, I., Metlicka, M., Senkerik, R., Pluhacek, M.: Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem. In: 2014 IEEE Symposium on Differential Evolution (SDE), pp. 1–8. IEEE, December 2014

    Google Scholar 

  51. Skanderova, L., Fabian, T.: Differential evolution dynamics analysis by complex networks. Soft. Comput. 21(7), 1817–1831 (2017)

    Article  Google Scholar 

  52. Metlicka, M., Davendra, D.: Ensemble centralities based adaptive Artificial Bee algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 3370–3376. IEEE, May 2015

    Google Scholar 

  53. Gajdos, P., Kromer, P., Zelinka, I.: Network visualization of population dynamics in the differential evolution. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1522–1528. IEEE, December 2015

    Google Scholar 

  54. Janostik, J., Pluhacek, M., Senkerik, R., Zelinka, I., Spacek, F.: Capturing inner dynamics of firefly algorithm in complex network—initial study. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, pp. 571–577. Springer, Cham (2016)

    Google Scholar 

  55. Pluhacek, M., Janostik, J., Senkerik, R., Zelinka, I., Davendra, D.: PSO as complex network—capturing the inner dynamics—initial study. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, pp. 551–559. Springer, Cham (2016)

    Google Scholar 

  56. Skanderova, L., Fabian, T., Zelinka, I.: Differential evolution dynamics modeled by longitudinal social network. J. Intell. Syst. 26(3), 523–529 (2017)

    Google Scholar 

  57. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T.: Towards better population sizing for differential evolution through active population analysis with complex network. In: Conference on Complex, Intelligent, and Software Intensive Systems, pp. 225–235. Springer, Cham, July 2017

    Google Scholar 

  58. Viktorin, A., Pluhacek, M., Senkerik, R.: Network based linear population size reduction in SHADE. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 86–93. IEEE, September 2016

    Google Scholar 

  59. Senkerik, R., Viktorin, A., Pluhacek, M., Janostik, J., Davendra, D.: On the influence of different randomization and complex network analysis for differential evolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3346–3353. IEEE, July 2016

    Google Scholar 

  60. Skanderova, L., Fabian, T., Zelinka, I.: Analysis of causality-driven changes of diffusion speed in non-Markovian temporal networks generated on the basis of differential evolution dynamics. Swarm Evol. Comput. 44, 212–227 (2018)

    Google Scholar 

  61. Janostik, J., Pluhacek, M., Senkerik, R., Zelinka, I.: Particle swarm optimizer with diversity measure based on swarm representation in complex network. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, pp. 561–569. Springer, Cham (2016)

    Google Scholar 

  62. Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T.: Uncovering communication density in PSO using complex network (2017)

    Google Scholar 

  63. Pluhacek, M., Viktorin, A., Senkerik, R., Kadavy, T., Zelinka, I.: PSO with partial population restart based on complex network analysis. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 183–192. Springer, Cham, June 2017

    Google Scholar 

  64. Pluhacek, M., Senkerik, R., Janostik, A.V.J., Davendra, D.: Complex network analysis in PSO as an fitness landscape classifier. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3332–3337. IEEE, July 2016

    Google Scholar 

  65. Kadavý, T., Pluháček, M., Viktorin, A., Šenkeřík, R.: Firework algorithm dynamics simulated and analyzed with the aid of complex network. In: Proceedings-31st European Conference on Modelling and Simulation, ECMS 2017. European Council for Modelling and Simulation (2017)

    Google Scholar 

  66. Tomaszek, L., Zelinka, I.: On performance improvement of the SOMA swarm based algorithm and its complex network duality. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4494–4500. IEEE, July 2016

    Google Scholar 

  67. Krömer, P., Gajdo, P., Zelinka, I.: Towards a network interpretation of agent interaction in ant colony optimization. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1126–1132. IEEE, December 2015

    Google Scholar 

  68. Skanderova, L., Zelinka, I., Saloun, P.: Complex network construction based on SOMA: vertices in-degree reliance on fitness value evolution. In: ISCS 2013: Interdisciplinary Symposium on Complex Systems, pp. 291–297. Springer, Heidelberg (2014)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089. This work is also based upon support by COST Action CA15140 (ImAppNIO), and COST Action IC1406 (cHiPSet).

Author information

Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Roman Senkerik

Authors
  1. Roman Senkerik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Senkerik .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering, VŠB-TUO, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

  2. Department of Electronics, Faculty of Electrical Engineering, VŠB-TUO, Ostrava, Czech Republic

    Pavel Brandstetter

  3. International Cooperation, Research and Training Institute, Ton Duc Thang University, Ho Chi Minh, Vietnam

    Tran Trong Dao

  4. Department of Automatic Control, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Vo Hoang Duy

  5. Department of Mechanical Design Engineering, Pukyong National University, Busan, Korea (Republic of)

    Sang Bong Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Senkerik, R. (2020). A Brief Overview of the Synergy Between Metaheuristics and Unconventional Dynamics. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14907-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14906-2

  • Online ISBN: 978-3-030-14907-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation