A novel human learning optimization algorithm with Bayesian inference learning

Zhang, Pinggai; Wang, Ling; Fei, Zixiang; Wei, Lisheng; Fei, Minrui; Menhas, Muhammad Ilyas

doi:10.1016/j.knosys.2023.110564

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…Bayesian optimization, a sophisticated optimization technique, contributes significantly to the realm of machine learning by efficiently navigating complex hyperparameter spaces to enhance the performance of models such as random forest. This approach maximizes the use of computational resources by making informed decisions about which hyperparameters to explore, leading to quicker convergence and improved model outcomes [15]. By strategically selecting hyperparameters for exploration, Bayesian optimization ensures that computational efforts are focused on the most promising areas, thus streamlining the optimization process, and improving the overall efficiency of model tuning.…”

Section: Bayesian Optimizationmentioning

confidence: 99%

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Rhouas,

El Attaoui,

El Hami

2024

IJECE

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In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.

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Section: Bayesian Optimizationmentioning

confidence: 99%

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Rhouas,

El Attaoui,

El Hami

2024

IJECE

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“…As the preeminent representatives of intelligent creatures, humans and their group behavior have been heavily studied and applied to human-based algorithms. The human urbanization search algorithm (HUS) 39 , the human evolutionary optimization algorithm (HEOA) 40 , the human behavioral optimization algorithm (HBBO) 41 , the focus group algorithm (FG) 42 , the human learning optimization algorithm (HLO) 43 , and the brainstorming optimization algorithm (BSO) 44 belong to this category. The main search mechanism of the NBAs is the linear combination, which is the formation of new variables by the linear combination of multiple variables, with different combinations and specific coefficients depending on the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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Kids Learning Optimizer: social evolution and cognitive learning-based optimization algorithm

Javed,

Zafar,

Younas

2024

Neural Comput & Applic

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A novel human learning optimization algorithm with Bayesian inference learning

Cited by 10 publications

References 35 publications

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Kids Learning Optimizer: social evolution and cognitive learning-based optimization algorithm

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