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Enhancing sine cosine algorithm based on social learning and elite opposition-based learning

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Abstract

In recent years, Sine Cosine Algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has good competitiveness among the existing optimization algorithms. However, the single mechanism of SCA leads to its insufficient utilization of the information of the whole population, insufficient ability to jump out of local optima and poor performance at solving complex objective function. Therefore, this paper introduces social learning strategy (SL) and elite opposition-based learning (EOBL) strategy to improve SCA, and proposes novel algorithm: enhancing Sine Cosine Algorithm based on elite opposition-based learning and social learning (ESLSCA). Social learning strategy takes full advantage of information from the entire population. The elite opposition-based learning strategy provides a possibility for the algorithm to jump out of local optima and increases the diversity of the population. To demonstrate the performance of ESLSCA, this paper uses 22 well-known benchmark test functions and CEC2019 test function set to evaluate ESLSCA. The comparisons show that the proposed ESLSCA has better performance than the standard SCA and it is very competitive among other excellent optimization algorithms.

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References

  1. Azari V, Vazquez O, Mackay E, Sorbie K, Jordan M (2022) Gradient descent algorithm to optimize the offshore scale squeeze treatments. J Petrol Sci Eng 208:109469. https://doi.org/10.1016/j.petrol.2021.109469

    Article  CAS  Google Scholar 

  2. Wang W, Cheng X, Liang X (2013) Optimization modeling of district heating networks and calculation by the newton method. Appl Therm Eng 61(2):163–170. https://doi.org/10.1016/j.applthermaleng.2013.07.025

    Article  Google Scholar 

  3. Memeti S, Pllana S, Binotto A, Kołodziej J, Brandic I (2019) Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing 101:893–936. https://doi.org/10.1007/s00607-018-0614-9

    Article  MathSciNet  Google Scholar 

  4. Ss VC, Hs A (2022) Nature inspired meta heuristic algorithms for optimization problems. Computing 104(2):251–269. https://doi.org/10.1007/s00607-021-00955-5

    Article  MathSciNet  Google Scholar 

  5. Aghaee Z, Ghasemi MM, Beni HA, Bouyer A, Fatemi A (2021) A survey on meta-heuristic algorithms for the influence maximization problem in the social networks. Computing 103:2437–2477. https://doi.org/10.1007/s00607-021-00945-7

    Article  MathSciNet  Google Scholar 

  6. Kirkpatrick S, Gelatt CD, Vecchi MP (1987) In: Fischler MA, Firschein O (eds) Optimization by simulated annealing. Morgan Kaufmann, San Francisco, pp 606–615. https://doi.org/10.1016/B978-0-08-051581-6.50059-3

  7. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

  8. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  9. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, pp 39–43. https://doi.org/10.1109/MHS.1995.494215

  10. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  11. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  12. Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  13. Zhang J, Xiao M, Gao L, Pan Q (2018) Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Appl Math Model 63:464–490. https://doi.org/10.1016/j.apm.2018.06.036

    Article  MathSciNet  Google Scholar 

  14. Zhao W, Wang L, Zhang Z (2019) A novel atom search optimization for dispersion coefficient estimation in groundwater. Futur Gener Comput Syst 91:601–610. https://doi.org/10.1016/j.future.2018.05.037

    Article  Google Scholar 

  15. Mirjalili S (2016) SCA: A sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  16. Dasgupta K, Roy PK, Mukherjee V (2020) Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm. Electr Power Syst Res. https://doi.org/10.1016/j.epsr.2019.106018

    Article  Google Scholar 

  17. Hafez AI, Zawbaa HM, Emary E, Hassanien AE (2016) Sine cosine optimization algorithm for feature selection. In: 2016 international symposium on innovations in intelligent systems and applications (INISTA), pp 1–5. https://doi.org/10.1109/INISTA.2016.7571853

  18. Das S, Bhattacharya A, Chakraborty AK (2018) Solution of short-term hydrothermal scheduling using sine cosine algorithm. Soft Comput 22:6409–6427. https://doi.org/10.1007/s00500-017-2695-3

    Article  Google Scholar 

  19. Chandrasekaran K, Sankar S, Banumalar K (2020) Partial shading detection for pv arrays in a maximum power tracking system using the sine–cosine algorithm. Energy Sustain Dev 55:105–121. https://doi.org/10.1016/j.esd.2020.01.007

    Article  Google Scholar 

  20. Chen H, Jiao S, Heidari AA, Wang M, Chen X, Zhao X (2019) An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers Manag 195:927–942. https://doi.org/10.1016/j.enconman.2019.05.057

    Article  Google Scholar 

  21. Sarwagya K, Nayak PK, Ranjan S (2020) Optimal coordination of directional overcurrent relays in complex distribution networks using sine cosine algorithm. Electr Power Syst Res. https://doi.org/10.1016/j.epsr.2020.106435

    Article  Google Scholar 

  22. Li S, Fang H, Liu X (2018) Parameter optimization of support vector regression based on sine cosine algorithm. Expert Syst Appl 91:63–77. https://doi.org/10.1016/j.eswa.2017.08.038

    Article  CAS  Google Scholar 

  23. Tawhid MA, Savsani V (2019) Multi-objective sine–cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Comput Appl 31:915–929. https://doi.org/10.1007/s00521-017-3049-x

    Article  Google Scholar 

  24. Price KV, Awad NH, Ali MZ, Suganthan PN (2018) Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization

  25. Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500. https://doi.org/10.1016/j.eswa.2017.07.043

    Article  Google Scholar 

  26. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  27. 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. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  28. 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. https://doi.org/10.1016/j.asoc.2018.07.039

    Article  Google Scholar 

  29. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010

    Article  ADS  MathSciNet  Google Scholar 

  30. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. https://doi.org/10.1109/tevc.2008.919004

    Article  Google Scholar 

  31. Long W, Wu T, Liang X, Xu S (2019) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Syst Appl 123:108–126. https://doi.org/10.1016/j.eswa.2018.11.032

    Article  Google Scholar 

  32. Gupta S, Deep K (2019) Improved sine cosine algorithm with crossover scheme for global optimization. Knowl Based Syst 165:374–406. https://doi.org/10.1016/j.knosys.2018.12.008

    Article  Google Scholar 

  33. Meshkat M, Parhizgar M (2017) A novel weighted update position mechanism to improve the performance of sine cosine algorithm. In: 2017 5th Iranian joint congress on fuzzy and intelligent systems (CFIS), pp 166–171. https://doi.org/10.1109/CFIS.2017.8003677

  34. Gupta S, Deep K (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230. https://doi.org/10.1016/j.eswa.2018.10.050

    Article  Google Scholar 

  35. Liu Z, Zhang J, Wang L, Feng J, Ding Y, Ren C (2023) PSO-based feature extraction of unknown protocol data frame. Computing 105(1):131–149. https://doi.org/10.1007/s00607-022-01118-w

    Article  Google Scholar 

  36. Pradhan A, Bisoy SK, Das A (2022) A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment. J King Saud Univ Comput Inf Sci 34(8 Part A):4888–4901. https://doi.org/10.1016/j.jksuci.2021.01.003

    Article  Google Scholar 

  37. Yadav A, Roy SM (2023) An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict the aeration efficiency of venturi aeration system. Smart Agric Technol 4:100230. https://doi.org/10.1016/j.atech.2023.100230

    Article  Google Scholar 

  38. Jordehi AR (2015) Particle swarm optimisation (PSO) for allocation of facts devices in electric transmission systems: a review. Renew Sustain Energy Rev 52:1260–1267. https://doi.org/10.1016/j.rser.2015.08.007

    Article  Google Scholar 

  39. Nenavath H, Kumar Jatoth DR, Das DS (2018) A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43:1–30. https://doi.org/10.1016/j.swevo.2018.02.011

    Article  Google Scholar 

  40. Abed-Alguni BH, Alawad NA, Al-Betar MA, Paul D (2022) Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. Appl Intell. https://doi.org/10.1007/s10489-022-04201-z

    Article  Google Scholar 

  41. Belazzoug M, Touahria M, Nouioua F, Brahimi M (2020) An improved sine cosine algorithm to select features for text categorization. J King Saud Univ Comput Inf Sci 32(4):454–464. https://doi.org/10.1016/j.jksuci.2019.07.003. (Emerging Software Systems)

    Article  Google Scholar 

  42. Nayak DR, Dash R, Majhi B, Wang S (2018) Combining extreme learning machine with modified sine cosine algorithm for detection of pathological brain. Comput Electr Eng 68:366–380. https://doi.org/10.1016/j.compeleceng.2018.04.009

    Article  Google Scholar 

  43. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  44. Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60. https://doi.org/10.1016/j.ins.2014.08.039

    Article  MathSciNet  Google Scholar 

  45. Zhang X, Wang X, Kang Q, Cheng J (2019) Differential mutation and novel social learning particle swarm optimization algorithm. Inf Sci 480:109–129. https://doi.org/10.1016/j.ins.2018.12.030

    Article  Google Scholar 

  46. Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23. https://doi.org/10.1016/j.swevo.2017.09.010

    Article  Google Scholar 

  47. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 1, pp 695–701. https://doi.org/10.1109/CIMCA.2005.1631345

  48. Abed-Alguni BH, Alawad NA, Al-Betar MA, Paul D (2022) Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. Appl Intell. https://doi.org/10.1007/s10489-022-04201-z

    Article  Google Scholar 

  49. Yu X, Xu W, Li C (2021) Opposition-based learning grey wolf optimizer for global optimization. Knowl Based Syst 226:107139. https://doi.org/10.1016/j.knosys.2021.107139

    Article  Google Scholar 

  50. Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294–310. https://doi.org/10.1016/j.neucom.2015.01.110

    Article  Google Scholar 

  51. Paiva FA, Silva CR, Leite IV, Marcone MH, Costa JA (2017) Modified bat algorithm with Cauchy mutation and elite opposition-based learning. In: 2017 IEEE Latin American conference on computational intelligence (LA-CCI). IEEE, pp 1–6. https://doi.org/10.1109/LA-CCI.2017.8285715

  52. Zhang S, Luo Q (2017) Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method. Int J Comput Intell Appl 16:1750012. https://doi.org/10.1142/S1469026817500122

    Article  Google Scholar 

  53. Abed-alguni BH, Paul D (2022) Island-based cuckoo search with elite opposition-based learning and multiple mutation methods for solving optimization problems. Soft Comput 26(7):3293–3312. https://doi.org/10.1007/s00500-021-06665-6

    Article  Google Scholar 

  54. Sihwail R, Omar K, Ariffin KAZ, Tubishat M (2020) Improved Harris Hawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access 8:121127–121145. https://doi.org/10.1109/access.2020.3006473

    Article  Google Scholar 

  55. Reihanian A, Feizi-Derakhshi M-R, Aghdasi HS (2019) NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator. Inf Sci 504:178–201. https://doi.org/10.1016/j.ins.2019.07.054

    Article  Google Scholar 

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Acknowledgements

This work was supported by the [National Natural Science Foundation of China] under Grant [No. 61401307].

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  1. School of Information Engineering, Tianjin University of Commerce, Tianjin, China

    Lei Chen, Linyun Ma & Lvjie Li

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  1. Lei Chen
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Chen, L., Ma, L. & Li, L. Enhancing sine cosine algorithm based on social learning and elite opposition-based learning. Computing (2024). https://doi.org/10.1007/s00607-024-01256-3

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