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AMHS: Archive-Based Multi-objective Harmony Search Algorithm

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Book cover 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))

Abstract

Meta-heuristics have been widely used in both science and industry as reliable alternatives to conventional optimization algorithms to solve challenging, real-world problems. Despite being general-purpose and having a black-box nature, they require changes to solve multi-objective optimization problems. This paper proposes a multi-objective version of harmony search based on the archive. Archive, grid, and leader selection mechanisms are applied in multi-objectives of HS. Five real-engineering problems are evaluated with the results of three indexes. Based on the results, the AMHS is capable of providing acceptable results than other alternatives.

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References

  1. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press

    Google Scholar 

  2. Khodadadi N, Azizi M, Talatahari S, Sareh P (2021) Multi-objective crystal structure algorithm (MOCryStAl): introduction and performance evaluation. IEEE Access

    Google Scholar 

  3. Mandic DP (2004) A generalized normalized gradient descent algorithm. IEEE Sig Process Lett 11(2):115–118

    Article  Google Scholar 

  4. Selman B, Gomes CP (2006) Hill-climbing search. Encycl Cogn Sci 81:82

    Google Scholar 

  5. Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci (Ny) 540:131–159

    Article  MathSciNet  Google Scholar 

  6. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85. https://doi.org/10.1007/BF00175354

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  10. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  Google Scholar 

  11. Kaveh A, Talatahari S, Khodadadi N (2019) Hybrid invasive weed optimization-shuffled frog-leaping algorithm for optimal design of truss structures. Iran J Sci Technol Trans Civ Eng 44(2):405–420

    Article  Google Scholar 

  12. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74

    Google Scholar 

  13. Kaveh A, Eslamlou AD, Khodadadi N (2020) Dynamic water strider algorithm for optimal design of skeletal structures. Period Polytech Civ Eng 64(3):904–916

    Google Scholar 

  14. Karami H, Anaraki MV, Farzin S, Mirjalili S (2021) Flow direction algorithm (FDA): a novel optimization approach for solving optimization problems. Comput Ind Eng 156:107224

    Article  Google Scholar 

  15. Kaveh A, Khodadadi N, Talatahari S (2021) A comparative study for the optimal design of steel structures using CSS and ACSS algorithms. Iran Univ Sci Technol 11(1):31–54

    Google Scholar 

  16. Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160

    Article  Google Scholar 

  17. Kaveh A, Talatahari S, Khodadadi N (2019) The hybrid invasive weed optimization-shuffled frog-leaping algorithm applied to optimal design of frame structures. Period Polytech Civ Eng 63(3):882–897

    Google Scholar 

  18. Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Cat. No.02TH8600), vol 2, pp 1051–1056. https://doi.org/10.1109/CEC.2002.1004388.

  19. Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1):79–95

    Article  Google Scholar 

  20. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  21. Sivasubramani S, Swarup KS (2011) Multi-objective harmony search algorithm for optimal power flow problem. Int J Electr Power Energy Syst 33(3):745–752

    Article  Google Scholar 

  22. Bhamidi L, Shanmugavelu S (2019) Multi-objective harmony search algorithm for dynamic optimal power flow with demand side management. Electr Power Compon Syst 47(8):692–702

    Article  Google Scholar 

  23. Pavelski LM, Almeida CP, Goncalves RA (2012) Harmony search for multi-objective optimization. In: 2012 Brazilian symposium on neural networks, pp 220–225

    Google Scholar 

  24. Sheng W, Liu K, Li Y, Liu Y, Meng X (2014) Improved multiobjective harmony search algorithm with application to placement and sizing of distributed generation. Math Probl Eng 2014

    Google Scholar 

  25. Qu B-Y, Li GS, Guo QQ, Yan L, Chai XZ, Guo ZQ (2019) A niching multi-objective harmony search algorithm for multimodal multi-objective problems. In: 2019 IEEE congress on evolutionary computation (CEC), pp 1267–1274

    Google Scholar 

  26. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  27. Zapotecas-Martinez S, Garcia-Najera A, Lopez-Jaimes A (2019) Multi-objective grey wolf optimizer based on decomposition. Expert Syst Appl 120:357–371

    Article  Google Scholar 

  28. Coello CAC, Sierra MR (2004) A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Mexican international conference on artificial intelligence, pp 688–697

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Florida International University, Miami, FL, USA

    Nima Khodadadi

  2. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Farhad Soleimanian Gharehchopogh & Benyamın Abdollahzadeh

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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

    Seyedali Mirjalili

Authors
  1. Nima Khodadadi
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  2. Farhad Soleimanian Gharehchopogh
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  3. Benyamın Abdollahzadeh
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  4. Seyedali Mirjalili
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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

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Khodadadi, N., Gharehchopogh, F.S., Abdollahzadeh, B., Mirjalili, S. (2022). AMHS: Archive-Based Multi-objective 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_25

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