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Quantum Henry gas solubility optimization algorithm for global optimization

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Abstract

This paper proposes an improvement on the recently introduced Henry Gas Solubility Optimization (HGSO) metaheuristic algorithm that simulates Henry’s gas law (i.e., the concentration of a gas sample in a liquid solvent is proportional to the concentration of the sample in the gas phase). As an improvement, we apply quantum theory instead of the standard procedure used in the HSGO algorithm for updating solutions. The proposed algorithm is named as Quantum HGSO (QHGSO) algorithm in this paper. The suggested changes enhance the ability of HGSO to create a counterbalance between exploitation and exploration for a better investigation of the solution space. For evaluating the capability of finding the optimal solution of our proposed algorithm, a collection of forty-seven global optimization functions is solved. Moreover, three well-known engineering problems are studied to show the performance of the QHGSO algorithm in constrained optimization problems. Comparative results with other well-known metaheuristic algorithms have shown that the QHGSO algorithm outperforms others with higher computational performance.

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References

  1. Lee C, Wang Y-F, Yang T (1997) Global optimization for mapping parallel image processing tasks on distributed memory machines. J Parallel Distrib Comput 45(1):29–45

    Article  MATH  Google Scholar 

  2. Candelieri A, Archetti F (2019) Global optimization in machine learning: the design of a predictive analytics application. Soft Comput 23(9):2969–2977

    Article  Google Scholar 

  3. Srinidhi NN, Dilip Kumar SM, Venugopal KR (2019) Network optimizations in the internet of things: a review. Eng Sci Technol Int J 22(1):1–21

    Google Scholar 

  4. Boix M, Montastruc L, Azzaro-Pantel C, Domenech S (2015) Optimization methods applied to the design of eco-industrial parks: a literature review. J Clean Prod 87:303–317

    Article  Google Scholar 

  5. Juels A, Wattenberg M (1996) Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Adv Neural Inf Process Syst, 430–436

  6. Feo TA, Resende MG (1995) Greedy randomized adaptive search procedures. J Global Optim 6(2):109–133

    Article  MathSciNet  MATH  Google Scholar 

  7. Voudouris C, Tsang EP (2003) Guided local search. Handbook of metaheuristics. Springer, New York, pp 185–218

    Chapter  Google Scholar 

  8. Baba N, Shoman T, Sawaragi Y (1977) A modified convergence theorem for a random optimization method. Inf Sci 13(2):159–166

    Article  MathSciNet  MATH  Google Scholar 

  9. Burke EK, Kendall G, Soubeiga E (2003) A tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9(6):451–470

    Article  Google Scholar 

  10. H. Lourenзo, "A beginner’s introduction to iterated local search," 2001.

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

    Article  MathSciNet  MATH  Google Scholar 

  12. D. E. Goldberg and J. H. Holland, "Genetic algorithms and machine learning," 1988.

  13. Beyer H-G, Schwefel H-P (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MathSciNet  MATH  Google Scholar 

  14. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  15. Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173

    Article  MathSciNet  MATH  Google Scholar 

  16. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  17. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027

  18. Zheng Y-J (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11

    Article  MathSciNet  MATH  Google Scholar 

  19. Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79

    Article  Google Scholar 

  20. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Article  Google Scholar 

  21. Muller SD, Marchetto J, Airaghi S, Kournoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6(1):16–29

    Article  Google Scholar 

  22. 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 

  23. Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948 ((Citeseer))

    Article  Google Scholar 

  24. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  25. Moghdani R, Salimifard K (2018) Volleyball Premier League Algorithm. Appl Soft Comput 64:161–185

    Article  Google Scholar 

  26. Kaveh A, Farhoudi N (2013) A new optimization method: Dolphin echolocation. Adv Eng Softw 59:53–70

    Article  Google Scholar 

  27. Duman E, Uysal M, Alkaya AF (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217:65–77

    Article  MathSciNet  Google Scholar 

  28. Wang G-G, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 1–5

  29. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE, pp 1470–1477

  30. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  31. Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm, Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005

  32. Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627

    Article  Google Scholar 

  33. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optimiz 38(2):129–154

    Article  MathSciNet  Google Scholar 

  34. Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. Pacific Rim international conference on artificial intelligence. Springer, New York, pp 854–858

    Google Scholar 

  35. Yang X-S (2009) Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms. Springer, New York, pp 169–178

    Google Scholar 

  36. Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Article  Google Scholar 

  37. Feynman RP (1986) Quantum mechanical computers. Found Phys 16(6):507–531

    Article  MathSciNet  Google Scholar 

  38. Narayanan A, Moore M (1996) Quantum-inspired genetic algorithms. In: Proceedings of IEEE international conference on evolutionary computation. IEEE, pp 61–66

  39. Han KH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593

    Article  Google Scholar 

  40. Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Fut Gen Comput Syst 101:646–667

    Article  Google Scholar 

  41. Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2019) A modified Henry gas solubility optimization for solving motif discovery problem. Neural Comput Appl, 1–13

  42. Biswas A, Mishra K, Tiwari S, Misra A (2013) Physics-inspired optimization algorithms: a survey. J Optimiz

  43. Cao J, Gao H (2012) A quantum-inspired bacterial swarming optimization algorithm for discrete optimization problems. International Conference in Swarm Intelligence. Springer, New York, pp 29–36

    Google Scholar 

  44. Jiao L, Li Y, Gong M, Zhang X (2008) Quantum-inspired immune clonal algorithm for global optimization. IEEE Trans Syst Man Cybern Part B (Cybernetics) 38(5):1234–1253

    Article  Google Scholar 

  45. Li P, Li S (2008) Quantum ant colony algorithm for continuous space optimization. Control Theory Appl 25(2):237–241

    Google Scholar 

  46. Sun J, Xu W, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. IEEE Conf Cybern Intell Syst 1:111–116

    Google Scholar 

  47. Zhang R, Gao H (2007) Improved quantum evolutionary algorithm for combinatorial optimization problem. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 6, pp 3501–3505: IEEE

  48. Wang Y et al (2007) A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing 70(4–6):633–640

    Article  Google Scholar 

  49. Zhang Y-k, Liu J-c, Cui Y-a, Hei X-h, Zhang M-h (2011) An improved quantum genetic algorithm for test suite reduction. In: 2011 IEEE International Conference on Computer Science and Automation Engineering, vol. 2, 149–153: IEEE

  50. Platel MD, Schliebs S, Kasabov N (2007) A versatile quantum-inspired evolutionary algorithm. In: 2007 IEEE Congress on Evolutionary Computation, 423–430: IEEE

  51. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289

    Article  MATH  Google Scholar 

  52. Birbil Şİ, Fang S-C (2003) An electromagnetism-like mechanism for global optimization. J Global Optim 25(3):263–282

    Article  MathSciNet  MATH  Google Scholar 

  53. Erol OK, Eksin I (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  54. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1–2):132–140

    Google Scholar 

  55. Flores JJ, López R, Barrera J (2011) Gravitational interactions optimization. International Conference on Learning and Intelligent Optimization. Springer, New York, pp 226–237

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

  57. Xie L, Tan Y, Zeng J, Cui Z (2010) Artificial physics optimisation: a brief survey. Int J Bio-Inspired Comput 2(5):291–302

    Article  Google Scholar 

  58. Formato RA (2007) Central force optimization. Prog Electromagn Res 77:425–491

    Article  Google Scholar 

  59. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    Article  MathSciNet  Google Scholar 

  60. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  61. Du H, Wu X, Zhuang J Small-World Optimization Algorithm for Function Optimization, ed.

  62. Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180

    Article  Google Scholar 

  63. Kaveh A, Kamalinejad M, Arzani H (2020) Quantum evolutionary algorithm hybridized with Enhanced colliding bodies for optimization. In: Structures. Elsevier, vol. 28, pp 1479–1501

  64. Kaveh A, Akbari H, Hosseini SM (2020) Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems. Eng Comput

  65. van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. In: van Laarhoven PJM, Aarts EHL (eds) Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15

    Chapter  MATH  Google Scholar 

  66. Abdechiri M, Meybodi MR, Bahrami H (2013) Gases brownian motion optimization: an algorithm for optimization (GBMO). Appl Soft Comput 13(5):2932–2946

    Article  Google Scholar 

  67. Brown TL (2009) Chemistry: the central science. Pearson Education

  68. Mastrolilli M, Gambardella LM (2000) Effective neighbourhood functions for the flexible job shop problem. J Sched 3(1):3–20

    Article  MathSciNet  MATH  Google Scholar 

  69. dos Santos CL (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals 37(5):1409–1418

    Article  Google Scholar 

  70. Singh MR, Mahapatra SS (2016) A quantum behaved particle swarm optimization for flexible job shop scheduling. Comput Ind Eng 93:36–44

    Article  Google Scholar 

  71. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  72. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  73. Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:1–67ss

    Article  Google Scholar 

  74. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  77. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  78. Woolson R (2007) Wilcoxon signed‐rank test. In: Wiley encyclopedia of clinical trials, 1–3

  79. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36):3902–3933

    Article  MATH  Google Scholar 

  80. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  81. Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015

    Article  Google Scholar 

  82. Kaveh A, Talatahari S (2010) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911

    Article  Google Scholar 

  83. Kaveh A, Motie Share M, Moslehi M (2013) A new meta-heuristic algorithm for optimization: magnetic charged system search. Acta Mech 224(1):85–107

    Article  MATH  Google Scholar 

  84. Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  86. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

  87. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294

    Article  Google Scholar 

  88. Kannan B, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411

    Article  Google Scholar 

  89. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Article  Google Scholar 

  90. Li L, Huang Z, Liu F, Wu Q (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85(7):340–349

    Article  Google Scholar 

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Acknowledgements

This research was supported by Tomsk Polytechnic University Competitiveness Enhancement Program.

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Correspondence to Mohamed Abd Elaziz.

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Mohammadi, D., Abd Elaziz, M., Moghdani, R. et al. Quantum Henry gas solubility optimization algorithm for global optimization. Engineering with Computers 38 (Suppl 3), 2329–2348 (2022). https://doi.org/10.1007/s00366-021-01347-1

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