A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Anonymous authors (for blind review purpose) (6473313)

“…In particular, the first folder also contains the addition data applicable to deterministic model (indicated by the notes in the name of the file, i.e., "for deterministic model"), which directly gives the processing time and processing cost of the end-of-life products on the corresponding processing line.…”

Sensitivity analysis of the TV-BayesOpt algorithm with a forgetting (orange line) or a forgetting-periodic (blue line) covariance function for a range of ε values. by John E. Fleming (8533956)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Deterministic-optimal policy in the Sensitive-Resistant model. by MingYi Wang (2807581)

Model results for daily COVID-19 caused deaths for simulations performed during the second step of the optimization algorithm. by Martin Bicher (10852788)

Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period antic... by John E. Fleming (8533956)

Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction by E. Tejera (7470644)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Meanings of model symbols. by Yingbo Mao (22522192)

“…Against this backdrop, the vehicle-assisted UAV collaborative delivery model has emerged: through the division of labor and collaboration between ground vehicles and UAVs, it not only expands the service radius of UAVs but also overcomes the constraints of no-fly zones, achieving dual improvements in delivery efficiency and service quality.This study focuses on the optimization of vehicle-assisted UAV delivery paths under no-fly zone constraints, aiming to construct a multi-objective optimization model that balances delivery costs, carbon emissions, and customer satisfaction, and to design an efficient solution algorithm for providing scientific decision support to logistics enterprises. …”

Highly sensitive conductance parameters are well-constrained by the optimized calibration pipeline. by Janice Yang (5747246)

Optimization framework. by Nisha Ann Viswan (20371285)

“…This cost is used by the algorithm. B: Typical model decomposition into levels. …”

Optimal schedule deployed in real life. by Samuel Christensen (9922554)

Optimal Bayesian sparsity threshold selection. by Jarod Smith (11999040)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

Model convergence to steady state. by Dmitrii Smirnov (8822324)

Model selection by BO from the simulated data presented in Fig 1C and 1D at a fixed value of <i>ω</i> = 1. by László Zimányi (2649973)

The optimal distance weights found from the search procedure after 40 successive runs of Algorithm 2 on the toy model test problem (described in Section 4.1). by Jonathan U. Harrison (5447603)

|B1+| and |E| maps using the standard basis. by Maryam Arianpouya (12299222)

General framework of the models. by Tiffany Leung (666431)