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Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems

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Abstract

Several metaheuristic methods have been applied to tackling various global and engineering optimization problems. However, this method still needs more improvement since they require a suitable balance between exploration and exploitation. Therefore, this study presents an enhancement of the arithmetic optimization algorithm (AOA) as a global optimization method. The developed method, named AOASC, depends on using the sine-cosine algorithm’s operators to enhance the exploitation ability of AOA during the searching process. This leads to improving the convergence rate of the developed method toward the optimal solution. Besides, improve the process of avoiding the attraction toward the local point. Besides these behaviors, the quality of the final solution (best one) is improved. To validate the efficiency of the developed method, a set of experiments is conducted, including various optimization problems, such as ten benchmark functions and five engineering optimization problems. Besides, the results of the developed method are compared with other well-known metaheuristic methods. The results showed the high efficiency of the developed method over other methods in terms of performance measures.

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References

  1. Abualigah L, Diabat A, Geem ZW (2020) A comprehensive survey of the harmony search algorithm in clustering applications. Appl Sci 10(11):3827

    Article  Google Scholar 

  2. dela Torre DMG, Gao J, Macinnis-Ng C (2021) Remote sensing-based estimation of rice yields using various models: a critical review. Geo-spat Inf Sci 24:1–24

    Article  Google Scholar 

  3. Barshandeh S, Piri F, Sangani SR (2020) Hmpa: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris hawks optimization algorithms for engineering problems, Eng Comput 1–45

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

  5. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  6. Noman N, Bollegala D, Iba H (2011) An adaptive differential evolution algorithm. In: IEEE congress of evolutionary computation (CEC). IEEE 2011, pp 2229–2236

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

    Article  Google Scholar 

  8. Zhou Q, Ismaeel A (2021) Integration of maximum crop response with machine learning regression model to timely estimate crop yield, Geo-spatial Inf Sci, pp 1–10

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

  10. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput

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

    MATH  Google Scholar 

  12. Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, Springer, pp 240–249

  13. 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 

  14. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  18. Shehab M, Alshawabkah H, Abualigah L, Nagham A-M (2020) Enhanced a hybrid moth-flame optimization algorithm using new selection schemes. Eng Comput, pp 1–26

  19. Alsalibi B, Abualigah L, Khader AT (2020) A novel bat algorithm with dynamic membrane structure for optimization problems. Appl Intell 1–26

  20. Alshinwan M, Abualigah L, Shehab M, Abd Elaziz M, Khasawneh AM, Alabool H, Al Hamad H (2021) Dragonfly algorithm: a comprehensive survey of its results, variants, and applications. Multim Tools Appl, pp 1–38

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Wang S, Jia H, Abualigah L, Liu Q, Zheng R (2021) An improved hybrid Aquila optimizer and Harris hawks algorithm for solving industrial engineering optimization problems. Processes 9(9):1551

    Article  Google Scholar 

  24. Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300

    Article  Google Scholar 

  25. Houssein EH, Dirar M, Abualigah L, Mohamed WM (2021) An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural Comput Appl, pp 1–36

  26. Zhao W, Wang L, Zhang Z (2019) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl 1–43

  27. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 113377

  28. Zheng R, Jia H, Abualigah L, Liu Q, Wang S (2021) Deep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimization. Processes 9(10):1774

    Article  Google Scholar 

  29. Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2021) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 116158

  30. Abualigah L, Shehab M, Diabat A, Abraham A (2020) Selection scheme sensitivity for a hybrid SALP swarm algorithm: analysis and applications. Eng Comput, pp 1–27

  31. Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artific Intell Rev, pp 1–42

  32. Abualigah L, Diabat A (2020) A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl, pp 1–24

  33. Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput Appl 1–24

  34. Huang X, Liu A, Li J (2021) Mapping and analyzing the local climate zones in china’s 32 major cities using Landsat imagery based on a novel convolutional neural network. Geo-spatial Inf Sci, pp 1–30

  35. Han X, Yue L, Dong Y, Xu Q, Xie G, Xu X (2020) Efficient hybrid algorithm based on moth search and fireworks algorithm for solving numerical and constrained engineering optimization problems. J Supercomput, pp 1–26

  36. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: International conference in swarm intelligence, Springer, pp 355–364

  37. Kalananda VK RA, Komanapalli VLN (2020) A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Appl Soft Comput 106903

  38. Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018

    Article  Google Scholar 

  39. Guedria NB (2016) Improved accelerated PSO algorithm for mechanical engineering optimization problems. Appl Soft Comput 40:455–467

    Article  Google Scholar 

  40. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2020) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

    Article  Google Scholar 

  41. Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268

    Article  Google Scholar 

  42. Ewees AA, Abd Elaziz M (2020) Performance analysis of chaotic multi-verse Harris hawks optimization: a case study on solving engineering problems. Eng Appl Artific Intell 88:103370

    Article  Google Scholar 

  43. Omran MG, Al-Sharhan S (2019) Improved continuous ant colony optimization algorithms for real-world engineering optimization problems. Eng Appl Artif Intell 85:818–829

    Article  Google Scholar 

  44. Kar D, Ghosh M, Guha R, Sarkar R, Garcia-Hernandez L, Abraham A (2020) Fuzzy mutation embedded hybrids of gravitational search and particle swarm optimization methods for engineering design problems. Eng Appl Artific Intell 95 (2020) 103847. https://doi.org/10.1016/j.engappai.2020.103847http://www.sciencedirect.com/science/article/pii/S0952197620302098

  45. Du T-S, Ke X-T, Liao J-G, Shen Y-J (2018) Dslc-foa: improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Appl Math Model 55:314–339

    Article  MathSciNet  MATH  Google Scholar 

  46. Chen Y, Pi D (2020) An innovative flower pollination algorithm for continuous optimization problem. Appl Math Model 83:237–65

    Article  MathSciNet  MATH  Google Scholar 

  47. Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng

  48. Al-Qaness MA, Abd Elaziz M, Ewees AA (2018) Oil consumption forecasting using optimized adaptive neuro-fuzzy inference system based on sine cosine algorithm. IEEE Access 6:68394–68402

    Article  Google Scholar 

  49. Al-qaness MA, Ewees AA, Fan H, Abd Elaziz M (2020) Optimized forecasting method for weekly influenza confirmed cases. Int J Environ Res Public Health 17(10):3510

    Article  Google Scholar 

  50. Jouhari H, Lei D, Al-qaness M AA, AbdElaziz M, Ewees AA, Farouk O (2019) Sine-cosine algorithm to enhance simulated annealing for unrelated parallel machine scheduling with setup times. Mathematics 7(11):1120

    Article  Google Scholar 

  51. Naji Alwerfali HS, Al-qaness MA, Abd Elaziz M, Ewees AA, Oliva D, Lu S (2020) Multi-level image thresholding based on modified spherical search optimizer and fuzzy entropy. Entropy 22(3):328

    Article  MathSciNet  Google Scholar 

  52. Ewees AA, Abd Elaziz M, Al-Qaness MA, Khalil HA, Kim S (2020) Improved artificial bee colony using sine-cosine algorithm for multi-level thresholding image segmentation. IEEE Access 8:26304–26315

    Article  Google Scholar 

  53. Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500

    Article  Google Scholar 

  54. Abd Elaziz M, Nabil N, Ewees AA, Lu S, (2019) Automatic data clustering based on hybrid atom search optimization and sine-cosine algorithm. In: IEEE congress on evolutionary computation (CEC). IEEE 2019:2315–2322

  55. Sindhu R, Ngadiran R, Yacob YM, Zahri NAH, Hariharan M (2017) Sine-cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput Appl 28(10):2947–2958

    Article  Google Scholar 

  56. Chen H, Wang M, Zhao X (2020) A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Appl Math Comput 369:124872

    MathSciNet  MATH  Google Scholar 

  57. Abualigah L, Diabat A, Sumari P, Gandomi AH (2021) A novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of COVID-19 CT images. Processes 9(7):1155

    Article  Google Scholar 

  58. Premkumar M, Jangir P, Kumar BS, Sowmya R, Alhelou RH, Abualigah L, Yildiz AR, Mirjalili S (2021) A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validations. IEEE Access

  59. Wang S, Liu Q, Liu Y, Jia H, Abualigah L, Zheng R, Wu D (2021) A hybrid SSA and SMA with mutation opposition-based learning for constrained engineering problems. Comput Intell Neurosci

  60. Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731

    Article  Google Scholar 

  61. Abualigah L, Diabat A, Elaziz MA (2021) Improved slime mould algorithm by opposition-based learning and levy flight distribution for global optimization and advances in real-world engineering problems. J Ambient Intell Hum Comput 1–40

  62. Chegini SN, Bagheri A, Najafi F (2018) Psoscalf: a new hybrid PSO based on sine cosine algorithm and levy flight for solving optimization problems. Appl Soft Comput 73:697–726

    Article  Google Scholar 

  63. Alabool HM, Alarabiat D, Abualigah L, Heidari AA (2021) Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Comput Appl 1–42

  64. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190

    Article  Google Scholar 

  65. Neggaz N, Ewees AA, Abd Elaziz M, Mafarja M (2020) Boosting Salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103

    Article  Google Scholar 

  66. Abd Elaziz M, Mirjalili S (2019) A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowl-Based Syst 172:42–63

    Article  Google Scholar 

  67. Abdel-Basset M, Chang V, Mohamed R (2020) Hsma_woa: a hybrid novel slime Mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest x-ray images. Appl Soft Comput 95:106642

    Article  Google Scholar 

  68. Mani M, Bozorg-Haddad O, Chu X (2018) Ant lion optimizer (alo) algorithm. In: Advanced optimization by nature-inspired algorithms, Springer, pp. 105–116

  69. Ewees AA, Abd Elaziz M, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  70. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

  71. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338

    Article  MATH  Google Scholar 

  72. Gandomi AH, Deb K (2020) Implicit constraints handling for efficient search of feasible solutions. Comput Methods Appl Mech Eng 363:112917

    Article  MathSciNet  MATH  Google Scholar 

  73. Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y (2008) Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Methods Appl Mech Eng 197(33–40):3080–3091

    Article  MATH  Google Scholar 

  74. Gholizadeh S, Salajegheh E (2009) Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel. Comput Methods Appl Mech Eng 198(37–40):2936–2949

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  76. Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming

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

    Article  Google Scholar 

  78. 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(36–38):3902–3933

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  80. Krohling RA, dos Santos Coelho L (2006) Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern Part B (Cybern) 36(6):1407–1416

    Article  Google Scholar 

  81. Huang F-Z, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356

    MathSciNet  MATH  Google Scholar 

  82. Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500

    Article  Google Scholar 

  83. 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 

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

    Article  Google Scholar 

  85. 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 

  86. Arora JS (2004) Introduction to optimum design. Elsevier, Amsterdam

    Book  Google Scholar 

  87. 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(4):443–473

    Article  MathSciNet  MATH  Google Scholar 

  88. Sandgren E Nonlinear integer and discrete programming in mechanical design optimization

  89. 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 

  90. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186(2):1407–1422

    MathSciNet  MATH  Google Scholar 

  91. Kaveh A, Talatahari S An improved ant colony optimization for constrained engineering design problems. Eng Comput

  92. 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 

  93. Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33(6):735–748

    Article  Google Scholar 

  94. 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 

  95. 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 

  96. Tsai J-F (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim 37(4):399–409

    Article  MathSciNet  Google Scholar 

  97. Baykasoğlu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164

    Article  Google Scholar 

  98. Brancato V, Calabrese L, Palomba V, Frazzica A, Fullana-Puig M, Solé A, Cabeza LF (2018) Mgso4\(\cdot\) 7h2o filled macro cellular foams: an innovative composite sorbent for thermo-chemical energy storage applications for solar buildings. Sol Energy 173:1278–1286

    Article  Google Scholar 

  99. Czerniak JM, Zarzycki H, Ewald D (2017) Aao as a new strategy in modeling and simulation of constructional problems optimization. Simul Model Pract Theory 76:22–33

    Article  Google Scholar 

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

    Article  Google Scholar 

  101. Baykasoğlu A, Akpinar Ş (2015) Weighted superposition attraction (WSA): a swarm intelligence algorithm for optimization problems-part 2: constrained optimization. Appl Soft Comput 37:396–415

    Article  Google Scholar 

Download references

Acknowledgements

This study was financially supported via a funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project Number (TURSP-2020/300), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  2. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau, Pinang, Malaysia

    Laith Abualigah

  3. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A. Ewees

  4. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Mohammed A. A. Al-qaness

  5. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz & Rehab Ali Ibrahim

  6. Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt

    Dalia Yousri

  7. Department of Management Information System, College of Business Administration, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia

    Maryam Altalhi

  8. Faculty of Computer Science & Engineering, Galala University, Suze, 435611, Egypt

    Mohamed Abd Elaziz

  9. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates

    Mohamed Abd Elaziz

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  1. Laith Abualigah
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Abualigah, L., Ewees, A.A., Al-qaness, M.A.A. et al. Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Comput & Applic 34, 8823–8852 (2022). https://doi.org/10.1007/s00521-022-06906-1

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