Skip to main content

Evolutionary Population Dynamic Mechanisms for the Harmony Search Algorithm

  • 45 Accesses

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 140)

Abstract

Evolutionary algorithms have been widely adopted in science and industry for optimizing challenging problems mainly due to their black box nature and high local optima avoidance. As popular soft computing techniques, they benefit from several stochastic operators, including but not limited to, selection, recombination, mutation, elitism, population diversity, and population dynamics. Among such operators, some have been extensively used and analyzed in different algorithms, while others are yet to be explored in different algorithms. This motivated our attempts to integrate Evolutionary Population Dynamics (EPD) in the Harmony Search (HS) algorithm. EPD is an evolutionary mechanism that excludes and/or replaces a set of the poor solutions in each generation and prevents them from reducing the quality of other solutions. EPD has been used in three different ways in HS to impact 10%, 30%, or 50% of the population to see its impact on the performance of this algorithm. It was observed that 10% is a reasonable portion of the population in HS to improve its performance on IEEE Congress of Evolutionary computation (CEC) test functions, which effectively mimic challenging real-world optimization problems.

Keywords

  • Evolutionary algorithm
  • Evolutionary operator
  • Evolutionary Population Dynamics
  • Harmony Search
  • Optimization
  • Algorithm

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-19-2948-9_18
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-981-19-2948-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   349.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3

References

  1. Yu X, Gen M (2010) Introduction to evolutionary algorithms. Springer Science & Business Media

    Google Scholar 

  2. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Google Scholar 

  3. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    CrossRef  Google Scholar 

  4. Rechenberg I (1989) Evolution strategy: nature’s way of optimization. In: Optimization: methods and applications, possibilities and limitations. Springer, pp 106–126

    Google Scholar 

  5. Eltaeib T, Mahmood A (2018) Differential evolution: a survey and analysis. Appl Sci 8(10):1945

    CrossRef  Google Scholar 

  6. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

    Google Scholar 

  7. Althobiani F, Khatir S, Brahim B, Ghandourah E, Mirjalili S, Wahab MA (2021) A hybrid PSO and Grey Wolf optimization algorithm for static and dynamic Crack identification. Theoretical and applied fracture mechanics, p 103213

    Google Scholar 

  8. Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408

    CrossRef  Google Scholar 

  9. Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36(10):5887–5958

    Google Scholar 

  10. Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194

    MathSciNet  CrossRef  Google Scholar 

  11. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 4661–4667

    Google Scholar 

  12. Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    CrossRef  Google Scholar 

  13. Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell Syst 2(3):173–203

    CrossRef  Google Scholar 

  14. Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm. Springer, pp 1–14

    Google Scholar 

  15. Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 59(4):381

    CrossRef  Google Scholar 

  16. Lewis A, Mostaghim S, Randall M (2008) Evolutionary population dynamics and multi-objective optimisation problems. In Multi-objective optimization in computational intelligence: theory and practice. IGI Global, pp 185–206

    Google Scholar 

  17. Boettcher S, Percus AG (1999) Extremal optimization: methods derived from co-evolution. arXiv preprint math/9904056

    Google Scholar 

  18. Lewis A, Abramson D, Peachey T (2003) An evolutionary programming algorithm for automatic engineering design. International conference on parallel processing and applied mathematics. Springer, pp 586–594

    Google Scholar 

  19. Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26(5):1257–1263

    CrossRef  Google Scholar 

  20. Liang J-J, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE Swarm intelligence symposium, SIS 2005. IEEE, pp 68–75

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Mirjalili, S.Z., Sajeev, S., Saha, R., Khodadadi, N., Mirjalili, S.M., Mirjalili, S. (2022). Evolutionary Population Dynamic Mechanisms for the Harmony Search Algorithm. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_18

Download citation