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Tangent search algorithm for solving optimization problems

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Abstract

This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses a mathematical model based on the tangent function to move a given solution toward a better solution. The tangent flight function has the advantage to balance between the exploitation and the exploration search. Moreover, a novel escape procedure is used to avoid to be trapped in local minima. Besides, an adaptive variable step-size is also integrated in this algorithm to enhance the convergence capacity. The performance of TSA is assessed in three classes of tests: classical tests, CEC benchmarks, and engineering optimization problems. Moreover, several studies and metrics have been used to observe the behavior of the proposed TSA. The experimental results show that TSA algorithm is capable to provide very promising and competitive results on most benchmark functions thanks to better balance between exploration and exploitation of the search space. The main characteristics of this new optimization algorithm is its simplicity and efficiency and it requires only a small number of user-defined parameters.

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References

  1. Layeb A (2013) A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems. J Comput Appl Math 253:14–25

    Article  MathSciNet  MATH  Google Scholar 

  2. Törn A, Zilinskas A (1989) Global optimization 989

  3. Chong EKP, Zak SH (2004) An introduction to optimization. Wiley, Hoboken

    MATH  Google Scholar 

  4. Fernández FM (2009) On some approximate methods for nonlinear models. Appl Math Comput 215(1):168–174

    MathSciNet  MATH  Google Scholar 

  5. Bozorg-Haddad O (2018) Advanced optimization by nature-inspired algorithms. Springer, Singapore

    Book  Google Scholar 

  6. Ferdi I, Layeb A (2018) A GRASP algorithm based new heuristic for the capacitated location routing problem. J Exp Theor Artif Intell 30(3):369–387

    Article  Google Scholar 

  7. Avriel M (2003) Nonlinear programming: analysis and methods. Courier Corporation, North Chelmsford

    MATH  Google Scholar 

  8. Gao F, Han L (2012) Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Comput Optim Appl 51(1):259–277

    Article  MathSciNet  MATH  Google Scholar 

  9. Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7(1):1–25

    Article  MathSciNet  MATH  Google Scholar 

  10. Van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15

    Chapter  MATH  Google Scholar 

  11. Greiner R (1996) PALO: a probabilistic hill-climbing algorithm. Artif Intell 84(1–2):177–208

    Article  MathSciNet  Google Scholar 

  12. Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206

    Article  MATH  Google Scholar 

  13. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    Article  MathSciNet  MATH  Google Scholar 

  14. Sivanandam SN, Deepa SN (2008) Genetic algorithms. Introduction to genetic algorithms. Springer, Berlin, pp 15–37

    Chapter  MATH  Google Scholar 

  15. Karaboğa D, Ökdem S (2004) A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turk J Electrical Eng Comput Sci 12(1):53–60

    Google Scholar 

  16. Hu X, Eberhart RC, Shi Y (2003) Engineering optimization with particle swarm. In: Proceedings of the 2003 IEEE swarm intelligence symposium. SIS'03 (Cat. No. 03EX706). IEEE (2003)

  17. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  18. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  19. Yang XS (2009) Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms. Springer, Berlin, pp 169–178

    Google Scholar 

  20. Lam AYS, Li VOK (2012) Chemical reaction optimization: a tutorial.". Memetic Computing 4(1):3–17

    Article  Google Scholar 

  21. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  22. Fan X et al (2020) Review and classification of bio-inspired algorithms and their applications. J Bionic Eng 17:611–631

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Zhao J et al (2019) Spherical search optimizer: a simple yet efficient meta-heuristic approach. Neural Comput Appl 1–32

  25. Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  26. Abualigah L et al (2021) The arithmetic optimization algorithm. Computer Methods Appl Mech Eng 376:113609

    Article  MathSciNet  MATH  Google Scholar 

  27. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18

    Article  Google Scholar 

  28. Mudong et al (2016) Cognitive behavior optimization algorithm for solving optimization problems. Appl Soft Comput 39:199–222

    Article  Google Scholar 

  29. Gavana A (2014) Global optimization benchmarks and AMPGO

  30. http://infinity77.net/global_optimization/test_functions.html

  31. Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report

  32. Yue CT, Price KV, Suganthan PN, Liang JJ, Ali MZ, Qu BY, Awad NH, Biswas PP (2019) Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization, Tech. Rep., Zhengzhou University and Nanyang Technological University

  33. Auger A, Hansen N (2012) Tutorial CMA-ES: evolution strategies and covariance matrix adaptation. In: Proceedings of the 14th annual conference companion on genetic and evolutionary computation

  34. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. 2013 IEEE Congr Evol Comput, pp 71–78

  35. Faramarzi A et al (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Mirjalili S et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  38. Mohamed AW et al (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE

  39. Qi X, Yuan Z, Song Y (2020) A hybrid pathfinder optimizer for unconstrained and constrained optimization problems. Comput Intell Neurosci

  40. Aragón VS, Esquivel SC, Coello Coello CA (2010) A modified version of a T-cell algorithm for constrained optimization problems. Int J Numer Methods Eng 84(3):351–378

    MATH  Google Scholar 

  41. Bernardino HS et al (2008) A new hybrid AIS-GA for constrained optimization problems in mechanical engineering. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence). IEEE

  42. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Article  Google Scholar 

  43. Montes E, Ocana B (2008) Bacterial foraging for engineering design problems: preliminary results. In: 4th Mex. Congr Evol Comput, COMCEV’2008, Mexico, pp 33–38

  44. Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473

    Article  MathSciNet  MATH  Google Scholar 

  45. Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356

    MathSciNet  MATH  Google Scholar 

  46. Zhang J, Liang C, Huang Y, Wu J, Yang S (2009) An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Appl Math Comput 211:392–416

    MathSciNet  MATH  Google Scholar 

  47. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  50. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074

    Article  Google Scholar 

  51. Wang Y, Cai ZX, Zhou YR, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct Multidiscip Optim 37(4):395–413

    Article  Google Scholar 

  52. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640

    Article  Google Scholar 

  53. Dhiman G, Kaur A (2019) STOA: a bio-inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell 82:148–174

    Article  Google Scholar 

  54. Wang H, Hu Z, Sun Y, Su Q, Xia X (2018) Modified backtracking search optimization algorithm inspired by simulated annealing for constrained engineering optimization problems. Comput Intell Neurosci 2018:9167414

    Article  Google Scholar 

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Correspondence to Abdesslem Layeb.

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Appendix

Appendix

  1. (a)

    Pressure vessel design

figure a

Mathematical model

figure b
  1. (b)

    Welded beam design

figure c

Mathematical model

figure d
  1. (c)

    Tension/compression spring design

figure e

Mathematical model

figure f
  1. (d)

    Speed reducer design problem

figure g

Mathematical model

figure h

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Layeb, A. Tangent search algorithm for solving optimization problems. Neural Comput & Applic 34, 8853–8884 (2022). https://doi.org/10.1007/s00521-022-06908-z

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  • DOI: https://doi.org/10.1007/s00521-022-06908-z

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