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Chaotic Stochastic Paint Optimizer (CSPO)

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

Abstract

Optimization of engineering problems requires addressing several common difficulties in the optimization problem, including but not limited to a large number of decision variables, multiple often conflicting objectives, constraints, locally optimal solutions, and expensive objective functions. It is pretty common that an algorithm performs very well on test functions but struggles when applying to real-world problems. This paper proposes a chaotic version of the recently proposed algorithm called chaotic stochastic paint optimizer (CSPO). A comparative study with other meta-heuristics demonstrates the merits of this algorithm and the change applied in this work.

Keywords

  • Stochastic paint optimizer
  • Optimization
  • Engineering problems
  • Chaotic stochastic paint optimizer

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  • DOI: 10.1007/978-981-19-2948-9_19
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References

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

    Google Scholar 

  2. 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 

  3. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    CrossRef  Google Scholar 

  4. Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv Prepr. arXiv1609.04747

    Google Scholar 

  5. Khodadadi N, Mirjalili S (2022) Truss optimization with natural frequency constraints using generalized normal distribution optimization. Appl. Intell, 1–14

    Google Scholar 

  6. 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 

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

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  10. 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 

  11. Kaveh A, Khodadadi N, Azar BF, Talatahari S (2020) Optimal design of large-scale frames with an advanced charged system search algorithm using box-shaped sections. Eng Comput, pp 1–21

    Google Scholar 

  12. Sadollah A, Sayyaadi H, Yadav A (2018) A dynamic metaheuristic optimization model inspired by biological nervous systems: neural network algorithm. Appl Soft Comput 71:747–782

    CrossRef  Google Scholar 

  13. 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

    CrossRef  Google Scholar 

  14. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    CrossRef  Google Scholar 

  15. Khodadadi N, Vaclav S, Mirjalili S (2022) Dynamic arithmetic optimization algorithm for truss optimization under natural frequency constraints. IEEE Access, 1. https://doi.org/10.1109/ACCESS.2022.3146374

  16. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    CrossRef  Google Scholar 

  17. 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 

  18. 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

    Google Scholar 

  19. 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 

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

    CrossRef  Google Scholar 

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

    Google Scholar 

  22. Sheikholeslami R, Kaveh A (2013) A survey of chaos embedded meta-heuristic algorithms. Int J Optim Civ. Eng 3(4):617–633

    Google Scholar 

  23. He D, He C, Jiang L-G, Zhu H, Hu G (2001) Chaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE Trans Circ Syst I Fundam Theor Appl 48(7):900–906

    MathSciNet  CrossRef  Google Scholar 

  24. Devaney RL (1989) An introduction to chaotic dynamical systems. Chapman and Hall/CRC

    Google Scholar 

  25. Bucolo M, Caponetto R, Fortuna L, Frasca M, Rizzo A (2002) Does chaos work better than noise? IEEE Circ Syst Mag 2(3):4–19

    CrossRef  Google Scholar 

  26. Ott E (2002) Chaos in dynamical systems. Cambridge University Press

    Google Scholar 

  27. Peitgen H-O, Jürgens H, Saupe D, Feigenbaum MJ (2004) Chaos and fractals: new frontiers of science, vol 106. Springer

    Google Scholar 

  28. Kaveh A, Bakhshpoori T (2016) A new metaheuristic for continuous structural optimization: water evaporation optimization. Struct Multidiscip Optim 54(1):23–43

    CrossRef  Google Scholar 

  29. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    CrossRef  Google Scholar 

  30. Chickermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846

    MathSciNet  CrossRef  Google Scholar 

  31. Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design

    Google Scholar 

  32. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    CrossRef  Google Scholar 

  33. Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    CrossRef  Google Scholar 

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Correspondence to Seyedali Mirjalili .

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Khodadadi, N., Mirjalili, S.M., Mirjalili, S.Z., Mirjalili, S. (2022). Chaotic Stochastic Paint Optimizer (CSPO). 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_19

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