TY - JOUR
T1 - Four vector intelligent metaheuristic for data optimization
AU - Fakhouri, Hussam N.
AU - Awaysheh, Feras M.
AU - Alawadi, Sadi
AU - Alkhalaileh, Mohannad
AU - Hamad, Faten
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Data optimization
KW - Exploitation
KW - Exploration
KW - Global optima
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85190817750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190817750&partnerID=8YFLogxK
U2 - 10.1007/s00607-024-01287-w
DO - 10.1007/s00607-024-01287-w
M3 - Article
AN - SCOPUS:85190817750
SN - 0010-485X
JO - Computing (Vienna/New York)
JF - Computing (Vienna/New York)
ER -