Fans Optimizer: A human-inspired optimizer for mechanical design problems optimization

Wang, Xiaofei; Xu, Jiazhong; Cheng, Haifang

doi:10.1016/j.eswa.2023.120242

Cited by 8 publications

(1 citation statement)

References 42 publications

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“…Education and learning processes also inspire optimization techniques, as seen in TeachingâĂŞLearning-Based Optimization (TLBO) [69], which models the inŕuence of a teacher on a learner and how knowledge is enhanced through teaching strategies. Fans Optimization (FO) [70] uses the dynamic and enthusiastic behavior of sports or celebrity fans as a metaphor for exploring and exploiting the search space.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Preprint

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Metaheuristic algorithms play a pivotal role in addressing complex and nonlinear optimization challenges. However, traditional optimizers often struggle to locate the global optimum in intricate problem spaces, necessitating the development of hybrid methodologies. This paper introduces FVIMDE, a cutting-edge hybrid optimization algorithm that amalgamates the innovative Four Vector Intelligent Metaheuristic (FVIM) with the proven robustness of Differential Evolution (DE). Designed to adeptly maneuver through the complex terrains of various optimization and engineering design problems, FVIMDE is tested and evaluated over three well-known benchmark suites—CEC2017, CEC2022, and a specially set of 50 benchmark functions. statistacel tests has been calculated including mean, standard deviation and the wilcoxon sum rank test. Further FVIMDE has been compared with state-of-art optimizers. Subsequent applications of FVIMDE to five intricate structural engineering challenges further validate its effectiveness. Comparative analyses against several leading optimization algorithms highlight the superior adaptability and robustness of FVIMDE, showcasing its exceptional performance and significant improvements over traditional methods in diverse optimization environments.

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Section: Literature Reviewmentioning

confidence: 99%