Skip to main content
SpringerLink
Log in

Four vector intelligent metaheuristic for data optimization

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Swarm intelligence (SI) algorithms represent a class of Artificial Intelligence (AI) optimization metaheuristics used for solving complex optimization problems. However, a key challenge in solving complex problems is maintaining the balance between exploration and exploitation to find the optimal global solution and avoid local minima. This paper proposes an innovative Swarm Intelligence (SI) algorithm called the Four Vector Intelligent Metaheuristic (FVIM) to address the aforementioned problem. FVIM’s search strategy is guided by four top-performing leaders within a swarm, ensuring a balanced exploration-exploitation trade-off in the search space, avoiding local minima, and mitigating low convergence issues. The efficacy of FVIM is evaluated through extensive experiments conducted over two datasets, incorporating both qualitative and quantitative statistical measurements. One dataset contains twenty-three well-known single-objective optimization functions, such as fixed-dimensional and multi-modal functions, while the other dataset comprises the CEC2017 functions. Additionally, the Wilcoxon test was computed to validate the result’s significance. The results illustrate FVIM’s effectiveness in addressing diverse optimization challenges. Moreover, FVIM has been successfully applied to tackle engineering design problems, such as weld beam and truss engineering design.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Awaysheh FM, Alazab M, Garg S, Niyato D, Verikoukis C (2021) Big data resource management & networks: taxonomy, survey, and future directions. IEEE Commun Surv Tutor 23(4):2098–2130

    Article  Google Scholar 

  2. Kaur K, Kumar Y (2020) Swarm intelligence and its applications towards various computing: a systematic review. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp 57–62. IEEE

  3. Tang J, Liu G, Pan Q (2021) A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE/CAA J Autom Sin 8(10):1627–1643

    Article  MathSciNet  Google Scholar 

  4. Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. Nature-inspired computing and optimization: Theory and applications, 475–494

  5. Hassanien AE, Emary E (2018) Swarm intelligence: principles, advances, and applications. CRC Press, Boca Raton

    Book  Google Scholar 

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

  7. Chopard B, Tomassini M, Chopard B, Tomassini M (2018) Particle swarm optimization. An introduction to metaheuristics for optimization, 97–102

  8. Gill PE, Murray W, Wright MH (2019) Practical optimization. SIAM, New Delhi

    Book  Google Scholar 

  9. Ryalat MH, Fakhouri HN, Zraqou J, Hamad F, Alzboun MS et al (2023) Enhanced multi-verse optimizer (tmvo) and applying it in test data generation for path testing. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2023.0140277

    Article  Google Scholar 

  10. Diwekar UM (2020) Introduction to applied optimization, vol 22. Springer, Berlin

    Google Scholar 

  11. Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22:387–408

    Article  Google Scholar 

  12. Zraqou J, Al-Helali AH, Maqableh W, Fakhouri H, Alkhadour W (2023) Robust email spam filtering using a hybrid of grey wolf optimiser and Naive Bayes classifier. Cybern Inf Technol 23(4):79–90

    Google Scholar 

  13. Fakhouri HN, Hudaib A, Sleit A (2020) Multivector particle swarm optimization algorithm. Soft Comput 24:11695–11713

    Article  Google Scholar 

  14. Wolpert D (1997) No free lunch theorems for optimization. IEEE Tran Evol Comput 1(1):67–82

    Article  Google Scholar 

  15. Adam SP, Alexandropoulos S-AN, Pardalos PM, Vrahatis MN (2019) No free lunch theorem: a review. Approximation and optimization: algorithms, complexity and applications. pp 57–82

  16. Fakhouri SN, Hudaib A, Fakhouri HN (2020) Enhanced optimizer algorithm and its application to software testing. J Exp Theor Artif Intell 32(6):885–907

    Article  Google Scholar 

  17. Sun W, Tang M, Zhang L, Huo Z, Shu L (2020) A survey of using swarm intelligence algorithms in IoT. Sensors 20(5):1420

    Article  Google Scholar 

  18. Wang X, Hu H, Liang Y, Zhou L (2022) On the mathematical models and applications of swarm intelligent optimization algorithms. Arch Comput Methods Eng 29(6):3815–3842

    Article  MathSciNet  Google Scholar 

  19. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P (2021) Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Diversity 25:1315–1360

    Article  Google Scholar 

  20. Abioye SO, Oyedele LO, Akanbi L, Ajayi A, Delgado JMD, Bilal M, Akinade OO, Ahmed A (2021) Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges. J Build Eng 44:103299

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Wang Y, Han Z (2021) Ant colony optimization for traveling salesman problem based on parameters optimization. Appl Soft Comput 107:107439

    Article  Google Scholar 

  23. Pham DT, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm-a novel tool for complex optimisation problems. In: Intelligent production machines and systems, pp 454–459. Elsevier

  24. Ullah A (2019) Artificial bee colony algorithm used for load balancing in cloud computing. IAES Int J Artif Intell 8(2):156

    Google Scholar 

  25. Cao L, Xu L, Goodman ED, Bao C, Zhu S (2019) Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor. IEEE Trans Evol Comput 24(2):305–319

    Article  Google Scholar 

  26. Blum C, Roli A, Dorigo M (2001) Hc–aco: the hyper-cube framework for ant colony optimization. In: Proceedings of MIC, vol. 2, pp 399–403

  27. Brambilla M, Ferrante E, Birattari M, Dorigo M (2013) Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7:1–41

    Article  Google Scholar 

  28. Navarro I, Matía F (2013) An introduction to swarm robotics. ISRN Robotics, Bristol

    Book  Google Scholar 

  29. Mirjalili S, Mirjalili S, Lewis A (2014) Grey wolf optimizer Advances in Engineering Software. 69:46–61

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

    Article  Google Scholar 

  31. Shehab M, Mashal I, Momani Z, Shambour MKY, AL-Badareen A, Al-Dabet S, Bataina N, Alsoud AR, Abualigah L (2022) Harris hawks optimization algorithm: variants and applications. Arch Comput Methods Eng 29(7):5579–5603

    Article  Google Scholar 

  32. Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174

    Article  Google Scholar 

  33. Fakhouri HN, Alawadi S, Awaysheh FM, Hamad F (2023) Novel hybrid success history intelligent optimizer with gaussian transformation: application in CNN hyperparameter tuning. Cluster Comput. pp 1–23

  34. Yang X-S, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  35. Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspir Comput 2(2):78–84

    Article  Google Scholar 

  36. Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. In: PRICAI 2006: trends in artificial intelligence: 9th pacific rim international conference on artificial intelligence Guilin, China, August 7–11, 2006 Proceedings 9, pp 854–858. Springer

  37. Ragab M, Awaysheh FM, Tommasini R (2021) Bench-ranking: a first step towards prescriptive performance analyses for big data frameworks. In: 2021 IEEE international conference on big data (Big Data), pp 241–251. IEEE

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

    Article  Google Scholar 

  39. Wang G-G, Deb S, Coelho LdS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI), pp 1–5. IEEE

  40. Xing B, Gao W-J, Xing B, Gao W-J (2014) Fruit fly optimization algorithm. Innovative computational intelligence: a rough guide to 134 clever algorithms. pp 167–170

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  43. Fakhouri HN, Hamad F, Alawamrah A (2022) Success history intelligent optimizer. J Supercomput 78:6461

    Article  Google Scholar 

  44. Fakhouri HN, Hudaib A, Sleit A (2020) Hybrid particle swarm optimization with sine cosine algorithm and nelder-mead simplex for solving engineering design problems. Arab J Sci Eng 45:3091–3109

    Article  Google Scholar 

  45. 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 

  46. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734

    Article  Google Scholar 

  47. Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) Lshade with semi-parameter adaptation hybrid with cma-es for solving cec 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC), pp 145–152. IEEE

  48. 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

  49. Braik M, Hammouri A, Atwan J, Al-Betar MA, Awadallah MA (2022) White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl-Based Syst 243:108457

    Article  Google Scholar 

  50. Zhao S, Zhang T, Ma S, Chen M (2022) Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications. Eng Appl Artif Intell 114:105075

    Article  Google Scholar 

  51. Şenel FA, Gökçe F, Yüksel AS, Yiğit T (2019) A novel hybrid PSO-GWO algorithm for optimization problems. Eng Comput 35:1359–1373

    Article  Google Scholar 

  52. Yang Z, Deng L, Wang Y, Liu J (2021) Aptenodytes forsteri optimization: algorithm and applications. Knowl-Based Syst 232:107483

    Article  Google Scholar 

  53. Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming

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

    Article  Google Scholar 

  55. Erfani, T., Utyuzhnikov, S.: On controlling the extent of robust solution in uncertain environment in multiobjective optimization. In: 49th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, p 887 (2011)

  56. Khodadadi N, Mirjalili S (2022) Truss optimization with natural frequency constraints using generalized normal distribution optimization. Appl Intell 52(9):10384–10397

    Article  Google Scholar 

  57. Zhang Y, Jin Z, Mirjalili S (2020) Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models. Energy Convers Manage 224:113301

    Article  Google Scholar 

  58. Gomes HM (2011) Truss optimization with dynamic constraints using a particle swarm algorithm. Expert Syst Appl 38(1):957–968

    Article  Google Scholar 

  59. Sedaghati R, Suleman A, Tabarrok B (2002) Structural optimization with frequency constraints using the finite element force method. AIAA J 40(2):382–388

    Article  Google Scholar 

  60. Konzelman CJ (1986) Dual methods and approximation concepts for structural optimization

  61. Kaveh A, Zolghadr A (2017) Truss shape and size optimization with frequency constraints using tug of war optimization. Asian J Civ Eng 18(2):311–333

    Google Scholar 

  62. Miguel LFF, Miguel LFF (2012) Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms. Expert Syst Appl 39(10):9458–9467

    Article  Google Scholar 

  63. Fakhouri HN, Alawadi S, Awaysheh FM, Hani IB, Alkhalaileh M, Hamad F (2023) A comprehensive study on the role of machine learning in 5g security: challenges, technologies, and solutions. Electronics 12(22):4604

    Article  Google Scholar 

  64. Awaysheh FM, Aladwan MN, Alazab M, Alawadi S, Cabaleiro JC, Pena TF (2021) Security by design for big data frameworks over cloud computing. IEEE Trans Eng Manage 69(6):3676–3693

    Article  Google Scholar 

  65. Awaysheh FM, Alawadi S, AlZubi S (2022) FLIoDT: a federated learning architecture from privacy by design to privacy by default over IoT. In: 2022 seventh international conference on fog and mobile edge computing, pp 1–6. IEEE

  66. Awaysheh FM (2022) From the cloud to the edge towards a distributed and light weight secure big data pipelines for IoT applications. In: Trust, security and privacy for big data, pp 50–68. CRC Press

  67. Awaysheh FM, Tommasini R, Awad A (2023) Big data analytics from the rich cloud to the frugal edge. In: 2023 IEEE international conference on edge computing and communications (EDGE), pp 319–329. IEEE

Download references

Author information

Authors and Affiliations

  1. Data Science and Artificial Intelligence Department, Faculty of Information Technology, University of Petra, Amman, Jordan

    Hussam N. Fakhouri

  2. Delta Research Center, Institute of Computer Science, Tartu University, Tartu, Estonia

    Feras M. Awaysheh

  3. Department of Computer Science (DIDA), Blekinge Institute of Technology, Karlskrona, Sweden

    Sadi Alawadi

  4. College of Education, Humanities and Social Sciences, Al Ain University, Al-Ain, UAE

    Mohannad Alkhalaileh

  5. Information Studies Department, Sultan Qaboos University, Muscat, Sultanate of Oman

    Faten Hamad

  6. School of Educational Sciences, The University of Jordan, Amman, Jordan

    Faten Hamad

Authors
  1. Hussam N. Fakhouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feras M. Awaysheh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sadi Alawadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohannad Alkhalaileh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Faten Hamad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feras M. Awaysheh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fakhouri, H.N., Awaysheh, F.M., Alawadi, S. et al. Four vector intelligent metaheuristic for data optimization. Computing (2024). https://doi.org/10.1007/s00607-024-01287-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00607-024-01287-w

Keywords

Mathematics Subject Classification

Search

Navigation