Four vector intelligent metaheuristic FVIM

Four vector intelligent metaheuristic FVIM Optimizer

Four vector intelligent metaheuristic FVIM Optimizer

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-of in the search space, avoiding local minima, and mitigating low convergence issues. The efcacy 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 fxed-dimensional and multi-modal functions, while the other dataset comprises the CEC2017 functions. Additionally, the Wilcoxon test was computed to validate the result’s signifcance. The results illustrate FVIM’s efectiveness in addressing diverse optimization challenges. Moreover, FVIM has been successfully applied to tackle engineering design problems, such as weld beam and truss engineering design.

https://link.springer.com/article/10.1007/s00607-024-01287-w