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Damping vibration-based IGMM optimization algorithm: fast and significant

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Abstract

The ideal gas molecular movement (IGMM) algorithm has been introduced by the authors recently. Detailed studies on its behaviors uncover the fact that the gas molecules also experience a local self-vibration as they move. A comprehensive study was carried out here to introduce the molecular vibration thoroughly as a damping phenomenon supporting convergence rationally with a hindering behavior as molecules travel toward the global best. Thus, a new algorithm containing a molecular operand on vibrational effect (MOVE) was introduced and three different functions were employed to simulate molecular vibrations and pursue investigation. They include simple harmonic, driven harmonic and damped harmonic motions. A number of optimization problems were attempted including a set of unconstrained problems, 23 benchmark functions consisting of unimodal, multimodal and multimodal functions with fix dimensions, and also three well-known constrained engineering problems. Moreover, in a statistically significant way, Wilcoxon’s rank-sum nonparametric statistical test was carried out at the 5% significance level. Overall, the damped harmonic motion function as a molecular vibration simulator supported the optimization procedure best among the other two vibrational functions and other algorithms involved in the research. It showed a relatively better act, causing a faster escalation in the convergence throughout the optimization process. The results intensely show that the MOVE operand significantly boosts the performance of the IGMM and one could certify the significance of VIGMM, proposed in the present study, over some other metaheuristic optimization algorithms.

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References

  • Ahmadi-Nedushan B (2012) An optimized instance based learning algorithm for estimation of compressive strength of concrete. Eng Appl Artif Intell 25:1073–1081. doi:10.1016/j.engappai.2012.01.012

    Article  Google Scholar 

  • Ahmadi-Nedushan B, Varaee H (2009) Optimal design of reinforced concrete retaining walls using a swarm intelligence technique. In: The first international conference on soft computing technology in civil. structural and environmental engineering. UK, pp 1–12

  • Akhtar S, Tai K, Ray T (2002) A socio-behavioural simulation model for engineering design optimization. Eng Optim 34:341–354. doi:10.1080/03052150212723

    Article  Google Scholar 

  • Al-Betar MA, Awadallah MA, Khader AT, Abdalkareem ZA (2015) Island-based harmony search for optimization problems. Expert Syst Appl 42:2026–2035. doi:10.1016/j.eswa.2014.10.008

    Article  Google Scholar 

  • Aljarah I, Faris H, Mirjalili S (2016) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. doi:10.1007/s00500-016-2442-1

    Google Scholar 

  • Borgnakke C, Sonntag RE (2009) Fundamentals of thermodynamics, 7th edn. Wiley, Newyork. ISBN:978-0-470-17157-8

  • Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236

    Article  Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18. doi:10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  • Dizangian B, Ghasemi MR (2015) Ranked-based sensitivity analysis for size optimization of structures. J Mech Des 137:121402. doi:10.1115/1.4031295

    Article  Google Scholar 

  • Ebrahimi A, Khamehchi E (2016) Sperm whale algorithm: an effective metaheuristic algorithm for production optimization problems. J Nat Gas Sci Eng 29:211–222. doi:10.1016/j.jngse.2016.01.001

    Article  Google Scholar 

  • Gandomi AH, Yang X (2011) Benchmark problems in structural optimization. Comput Optim Methods Algorithms Stud Comput Intell 356:259–281. doi:10.1007/978-3-642-20859-1_12

    MathSciNet  MATH  Google Scholar 

  • Georgiev A, Spassova E, Assa J, Danev G (2010) Preparation of polyimide thin films by vapour deposition and solid state reactions. In: Hashim AA (ed) Polymer thin films. InTech, Rijeka, Croatia. ISBN:978-953-307-059-9

  • Ghasemi MR, Ghiasi R, Varaee H (2017a) Probability-based damage detection of structures using surrogate model and enhanced ideal gas molecular movement algorithm. In: Proceedings of 12th world congress on structural and multidisciplinary optimisation (WCSMO12). Braunschweig, Germany,

  • Ghasemi MR, Ghiasi R, Varaee H (2017b) Probability-based damage detection of structures using model updating with enhanced ideal gas molecular movement algorithm. In: ICCBE 2017: 19th International conference on civil and building engineering, Copenhagen, Denmark, United Kingdom

  • Ghasemi MR, Varaee H (2016) A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm. Eng Comput. doi:10.1007/s00366-016-0485-7

    Google Scholar 

  • Ghasemi MR, Varaee H (2017a) Enhanced IGMM optimization algorithm based on vibration for numerical and engineering problems. Eng Comput. doi:10.1007/s00366-017-0523-0

    Google Scholar 

  • Ghasemi MR, Varaee H (2017b) Modified ideal gas molecular movement algorithm based on quantum behavior. In: 12th world congress on structural and multidisciplinary optimisation (WCSMO12). Braunschweig, Germany

  • Halliday D, Resnick R, Walker J et al (2011) Principles of physics. Wiley, Hoboken

    MATH  Google Scholar 

  • Hossein A, Yang GX, Gandomi AH et al (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35. doi:10.1007/s00366-011-0241-y

    Article  Google Scholar 

  • Ilich P-P (2010) Selected problems in physical chemistry: strategies and interpretations. Springer, Berlin

    Book  Google Scholar 

  • Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Franklin Inst 346:328–348. doi:10.1016/j.jfranklin.2008.11.003

    Article  MathSciNet  MATH  Google Scholar 

  • Kaveh A, Mahdavi VRR (2014) Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv Eng Softw 70:1–12. doi:10.1016/j.advengsoft.2014.01.002

    Article  Google Scholar 

  • Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85. doi:10.1016/j.compstruc.2016.01.008

    Article  Google Scholar 

  • Ku KJ, Rao SS, Chen L (1998) Taguchi-aided search method for design optimization of engineering systems. Eng Optim 30:1–23. doi:10.1080/03052159808941235

    Article  Google Scholar 

  • Laurendeau NM (2005) Statistical thermodynamics fundamentals and applications. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Li Z, Wang W, Yan Y, Li Z (2015) PS-ABC: a hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Elsevier, Amsterdam

    Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN, Chen Q (2015) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. In: Technical report 201411A. Computational Intelligence Laboratory, Singapore

  • Mezura-Montes E, Coello CAC, Landa-Becerra R (2003) Engineering optimization using simple evolutionary algorithm. In: 2003 proceedings 15th IEEE international conference on tools with artificial intelligence, pp 149–156

  • Mirjalili S, Zaiton S, Hashim M, et al (2010) A new hybrid PSOGSA algorithm for function optimization. In: Proceedings ICCIA 2010 - 2010 international conference on computer and applications. pp 374–377. doi:10.1109/ICCIA.2010.6141614

  • Mousavirad SJ, Ebrahimpour-komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell. doi:10.1007/s10489-017-0903-6

  • Rao SS (2009) Engineering optimization: theory and practice. Wiley, Hoboken

    Book  Google Scholar 

  • Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248. doi:10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  • Ravindran A, Reklaitis GV, Ragsdell KM, Reklaitis GV (2006) Engineering optimization: methods and applications. Wiley, Hoboken

    Book  Google Scholar 

  • Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33:735–748. doi:10.1080/03052150108940941

    Article  Google Scholar 

  • Sharma TK, Pant M (2016) Shuffled artificial bee colony algorithm. Soft Comput. doi:10.1007/s00500-016-2166-2

    Google Scholar 

  • Sonntag RE, Borgnakke C, Van Wylen GJ, Van Wyk S (1998) Fundamentals of thermodynamics. Wiley, New York

    Google Scholar 

  • Varaee H, Ahmadi-Nedushan B (2011) Minimum cost design of concrete slabs using particle swarm optimization with time varying acceleration coefficients. World Appl Sci J 13:2484–2494

    Google Scholar 

  • Varaee H, Ghasemi MR (2017) Engineering optimization based on ideal gas molecular movement algorithm. Eng Comput 33:71–93. doi:10.1007/s00366-016-0457-y

    Article  Google Scholar 

  • Wang G-G, Deb S, Coelho LSDS (2015a) Earthworm optimization algorithm: a bioinspired metaheuristic algorithm for global optimization problems. Int J Bio-Inspired Comput. doi:10.1504/IJBIC.2015.10004283

  • Wang G-G, Deb S, Cui Z (2015b) Monarch butterfly optimization. Neural Comput Appl. doi:10.1007/s00521-015-1923-y

    Google Scholar 

  • Wang G-G, Gandomi AH, Zhao X, Chu HCE (2014) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20:273–285. doi:10.1007/s00500-014-1502-7

    Article  Google Scholar 

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1:80–83. doi:10.2307/3001968

    Article  Google Scholar 

  • Wilson EB, Decius JC, Cross PC (2012) Molecular vibrations: the theory of infrared and Raman vibrational spectra. Courier Corporation, North Chelmsford

    Google Scholar 

  • Wolpert DH, Macready WG, Nna D et al (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. doi:10.1109/4235.585893

    Article  Google Scholar 

  • Xu X, Rong H, Trovati M, et al (2016) CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems. Soft Comput 1–13. doi:10.1007/s00500-016-2383-8

  • Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio Inspir Comput 2(2):78–84. doi:10.1504/IJBIC.2010.032124

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Civil Engineering, University of Sistan and Baluchestan, P.O. Box 9816745563-161, Zahedan, Iran

    Mohammad Reza Ghasemi

  2. Ale-Taha Institute of Higher Education, Tehran, Iran

    Hesam Varaee

Authors
  1. Mohammad Reza Ghasemi
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  2. Hesam Varaee
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Correspondence to Mohammad Reza Ghasemi.

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Communicated by V. Loia.

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Ghasemi, M.R., Varaee, H. Damping vibration-based IGMM optimization algorithm: fast and significant. Soft Comput 23, 451–481 (2019). https://doi.org/10.1007/s00500-017-2804-3

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