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Four vector intelligent metaheuristic for data optimization

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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.

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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

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  1. Hussam N. Fakhouri
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  2. Feras M. Awaysheh
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  3. Sadi Alawadi
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  4. Mohannad Alkhalaileh
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  5. Faten Hamad
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Correspondence to Feras M. Awaysheh.

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Fakhouri, H.N., Awaysheh, F.M., Alawadi, S. et al. Four vector intelligent metaheuristic for data optimization. Computing 106, 2321–2359 (2024). https://doi.org/10.1007/s00607-024-01287-w

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