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Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization

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Abstract

Nowadays, many optimization issues around us cannot be solved by precise methods or that cannot be solved in a reasonable time. One way to solve such problems is to use metaheuristic algorithms. Metaheuristic algorithms try to find the best solution out of all possible solutions in the shortest time possible. Speed in convergence, accuracy, and problem-solving ability at high dimensions are characteristics of a good metaheuristic algorithm. This paper presents a new population-based metaheuristic algorithm inspired by a new source of inspiration. This algorithm is called Giza Pyramids Construction (GPC) inspired by the ancient past has the characteristics of a good metaheuristic algorithm to deal with many issues. The ancient-inspired is to observe and reflect on the legacy of the ancient past to understand the optimal methods, technologies, and strategies of that era. The proposed algorithm is controlled by the movements of the workers and pushing the stone blocks on the ramp. This algorithm is compared with five standard and popular metaheuristic algorithms. For this purpose, thirty different and diverse benchmark test functions are utilized. The proposed algorithm is also tested on high-dimensional benchmark test functions and is used as an application in image segmentation. The results show that the proposed algorithm is better than other metaheuristic algorithms and it is successful in solving high-dimensional problems, especially image segmentation.

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Notes

  1. Source codes of GPC are publicly available at www.harifi.com.

References

  1. Rao SS (2019) Engineering optimization: theory and practice. Wiley, Hoboken

    Book  Google Scholar 

  2. Hussain K, Salleh MNM, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233

    Article  Google Scholar 

  3. Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. In: Sangaiah AK, Sheng M, Zhang Z (eds) Computational intelligence for multimedia big data on the cloud with engineering applications. Academic Press, Cambridge, pp 185–231

    Chapter  Google Scholar 

  4. Karkalos NE, Markopoulos AP, Davim JP (2019) Evolutionary-based methods. In: Karkalos NE, Markopoulos AP, Davim JP (eds) Computational methods for application in industry 4.0. Springer, Cham, pp 11–31

    Chapter  Google Scholar 

  5. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Article  Google Scholar 

  6. Neri F, Cotta C, Moscato P (2011) Handbook of memetic algorithms, vol 379. Springer, Berlin

    Google Scholar 

  7. Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  8. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  9. De Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, vol 2000, pp 36–39

  10. Glover F, Laguna M (1998) Tabu search. In: Du DZ, Pardalos PM (eds) Handbook of combinatorial optimization. Springer, Boston, pp 2093–2229

    Chapter  Google Scholar 

  11. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  12. Lourenço HR, Martin OC, Stützle T (2003) Iterated local search. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. International Series in Operations Research & Management Science, vol 57. Springer, Boston, pp 320–353

  13. Voudouris C, Tsang EP, Alsheddy A (2010) Guided local search. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, pp 321–361

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

    Article  MathSciNet  MATH  Google Scholar 

  15. Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Eur J Oper Res 130(3):449–467

    Article  MathSciNet  MATH  Google Scholar 

  16. Dhal KG, Ray S, Das A, Das S (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Arch Comput Methods Eng 26(5):1607–1638

    Article  MathSciNet  Google Scholar 

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

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

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  21. Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol Intell 12(2):211–226

    Article  Google Scholar 

  22. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  23. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Jain M, Maurya S, Rani A, Singh V (2018) Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J Intell Fuzzy Syst 34(3):1573–1582

    Article  Google Scholar 

  26. Abualigah L, Shehab M, Alshinwan M, Mirjalili S, Abd Elaziz M (2020) Ant lion optimizer: a comprehensive survey of its variants and applications. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09420-6

    Article  Google Scholar 

  27. Lam AY, Li VO (2009) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  29. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04839-1

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation. IEEE, pp 4661–4667

  32. Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the third annual conference on evolutionary programming. World Scientific, River Edge, pp 131–139

  33. 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

    Article  Google Scholar 

  34. Askari Q, Younas I, Saeed M (2020) Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl Based Syst 195:105709

    Article  Google Scholar 

  35. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366

    Article  Google Scholar 

  36. Ma L, Zhu Y, Liu Y, Tian L, Chen H (2015) A novel bionic algorithm inspired by plant root foraging behaviors. Appl Soft Comput 37:95–113

    Article  Google Scholar 

  37. Rezaei N, Ebrahimnejad S, Moosavi A, Nikfarjam A (2019) A green vehicle routing problem with time windows considering the heterogeneous fleet of vehicles: two metaheuristic algorithms. Eur J Ind Eng 13(4):507–535

    Article  Google Scholar 

  38. Bekdaş G, Nigdeli SM, Kayabekir AE, Yang XS (2019) Optimization in civil engineering and metaheuristic algorithms: a review of state-of-the-art developments. In: Computational intelligence, optimization and inverse problems with applications in engineering. Springer, Cham, pp 111–137

  39. Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2020) Optimizing a neuro-fuzzy system based on nature inspired emperor penguins colony optimization algorithm. IEEE Trans Fuzzy Syst 28(6):1110–1124

    Article  Google Scholar 

  40. Ghosh M, Guha R, Singh PK, Bhateja V, Sarkar R (2019) A histogram based fuzzy ensemble technique for feature selection. Evol Intell 12(4):713–724

    Article  Google Scholar 

  41. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  42. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

    Google Scholar 

  43. Finley MI (1985) Ancient history, evidence and models. Chatto & Windus, London

    Google Scholar 

  44. Momigliano A (1950) Ancient history and the antiquarian. J Warbg Court Inst 13(3/4):285–315

    Article  Google Scholar 

  45. Spaulding AC (2017) Explanation in archeology. In: Binford L (ed) Archeology in cultural systems. Routledge, Abingdon, pp 33–39

    Chapter  Google Scholar 

  46. Gates C (2011) Ancient cities: the archaeology of urban life in the ancient Near East and Egypt, Greece and Rome. Taylor & Francis, Abingdon

    Book  Google Scholar 

  47. Laurence R (2004) The uneasy dialogue between ancient history and archaeology. In: Sauer E (ed) Archaeology and ancient history. Routledge, Abingdon, pp 111–125

    Google Scholar 

  48. Verboven K (2014) Attitudes to work and workers in classical Greece and Greece and Rome. Tijdschrift voor Economische en Sociale Geschiedenis 11:67–87

    Article  Google Scholar 

  49. Noorbergen R (2001) Secrets of the lost races: new discoveries of advanced technology in ancient civilizations. TEACH Services Inc., Fort Oglethorpe

    Google Scholar 

  50. Flohr M (2015) Innovation and society in the Roman World. Oxford Handbooks Online

  51. Verner M (2007) The pyramids: the mystery, culture, and science of Egypt’s great monuments. Open Road+Grove/Atlantic

  52. Magli G (2009) Akhet Khufu: archaeo-astronomical hints at a common project of the two main pyramids of Giza, Egypt. Nexus Netw J 11(1):35–50

    Article  Google Scholar 

  53. Morishima K, Kuno M, Nishio A, Kitagawa N, Manabe Y, Moto M et al (2017) Discovery of a big void in Khufu’s Pyramid by observation of cosmic-ray muons. Nature 552(7685):386–390

    Article  Google Scholar 

  54. Lehner M (1997) The complete pyramids. Thames & Hudson, London

    Google Scholar 

  55. Smith CB (1999) Program management BC. Civ Eng 69(6):34

    Google Scholar 

  56. Smith CB (2018) How the great pyramid was built. Smithsonian Institution, Washington, DC

    Google Scholar 

  57. Rigby JK (2016) Building the great pyramid at Giza: investigating ramp models

  58. Surjanovic S, Bingham D (2013) Virtual library of simulation experiments: test functions and datasets. Retrieved March 4, 2020, from http://www.sfu.ca/~ssurjano

  59. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240:152–174

    Article  Google Scholar 

  60. Pare S, Kumar A, Bajaj V, Singh GK (2016) A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Appl Soft Comput 47:76–102

    Article  Google Scholar 

  61. Jia H, Lang C, Oliva D, Song W, Peng X (2019) Hybrid grasshopper optimization algorithm and differential evolution for multilevel satellite image segmentation. Remote Sens 11(9):1134

    Article  Google Scholar 

  62. Kapoor S, Zeya I, Singhal C, Nanda SJ (2017) A grey wolf optimizer based automatic clustering algorithm for satellite image segmentation. Procedia Comput Sci 115:415–422

    Article  Google Scholar 

  63. Hrosik RC, Tuba E, Dolicanin E, Jovanovic R, Tuba M (2019) Brain image segmentation based on firefly algorithm combined with k-means clustering. Stud Inform Control 28:167–176

    Google Scholar 

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Harifi, S., Mohammadzadeh, J., Khalilian, M. et al. Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evol. Intel. 14, 1743–1761 (2021). https://doi.org/10.1007/s12065-020-00451-3

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