Skip to main content
SpringerLink
Log in

Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization

  • Conference paper
  • First Online:
Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications

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

  • 314 Accesses

Abstract

This paper proposes two hybrid versions of the generalized normal distribution optimization (GNDO) and sine cosine algorithm (SCA) for global optimization. The proposed hybrid methods combine the excellent characteristics of the GNDO and SCA algorithms to enhance the exploration and exploitation behaviors. Moreover, an additional weight parameter is introduced to further improve the search ability of the hybrid methods. The proposed methods are tested with 23 mathematical optimization problems. Our results reveal that the proposed hybrid method was very competitive compared to the other metaheuristic algorithms.

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
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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. Bogar E, Beyhan S (2020) Adolescent Identity Search Algorithm (AISA): a novel metaheuristic approach for solving optimization problems. Appl Soft Comput 95:106503

    Article  Google Scholar 

  2. Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Mirjalili S (2019) Genetic algorithm. Evolutionary algorithms and neural networks. Springer, pp 43–55

    Google Scholar 

  5. He Y, Zhang F, Mirjalili S, Zhang T (2022) Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems. Swarm Evol Comput 69:101022

    Article  Google Scholar 

  6. Price KV (2013) Differential evolution. Handbook of optimization. Springer, pp 187–214

    Google Scholar 

  7. 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 

  8. Shami TM, El-Saleh AA, Alswaitti M, Al-Tashi Q, Summakieh MA, Mirjalili S (2022) Particle Swarm optimization: a comprehensive survey. IEEE Access

    Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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 

  11. 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

    Article  MathSciNet  Google Scholar 

  12. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Simulated annealing: theory and applications. Springer, pp 7–15

    Google Scholar 

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

    Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Zhang Y, Jin Z, Mirjalili S (2020) Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models. Energy Convers Manage 224:113301

    Article  Google Scholar 

  16. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  17. Taghian S, Nadimi-Shahraki MH (2019) A binary metaheuristic algorithm for wrapper feature selection. Int J Comput Sci Eng (IJCSE) 8(5):168–172

    Google Scholar 

  18. 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. Theor Appl Fract Mech, 103213

    Google Scholar 

  19. Talbi E-G (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564

    Article  Google Scholar 

  20. Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135–4151

    Article  Google Scholar 

  21. Blum C, Roli A, Sampels M (2008) Hybrid metaheuristics: an emerging approach to optimization. Springer

    Google Scholar 

  22. Blum C, Raidl GR (2016) Hybrid metaheuristics: powerful tools for optimization. Springer

    Google Scholar 

  23. Kaveh A, Talatahari S, Khodadadi N (2020) Stochastic paint optimizer: theory and application in civil engineering. Eng Comput 1–32

    Google Scholar 

  24. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100, Melaka, Malaysia

    Jingwei Too

  2. Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Ali Safaa Sadiq

  3. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Hesam Akbari

  4. School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia

    Guo Ren Mong

  5. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  6. Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

    Seyedali Mirjalili

Authors
  1. Jingwei Too
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Safaa Sadiq
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hesam Akbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guo Ren Mong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seyedali Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Energy IT, Gachon University, Seongnam, Korea (Republic of)

    Zong Woo Geem

  4. School of Mechanical Engineering, University of Science and Culture, Tehran, Iran

    Ali Sadollah

  5. Department of Mathematics, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Too, J., Sadiq, A.S., Akbari, H., Mong, G.R., Mirjalili, S. (2022). Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization. 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_4

Download citation

Publish with us

Policies and ethics

Search

Navigation