The retailer’s profit at each level is under different unit disposal costs. حسب Minchao Zheng (20558832)

Changes in Punishment Function. حسب Huiyue Xu (12022388)

The cost function Φ(<i>x</i>, <i>w</i>) versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³.... حسب Limin Ma (556873)

Order quantity and profit of the supply chain under different unit disposal costs. حسب Minchao Zheng (20558832)

The order quantity and profit of the supply chain under different unit production costs. حسب Minchao Zheng (20558832)

System cost calculation. حسب Syed Zahurul Islam (9635163)

"…The IoT system consists of soil moisture sensor with GSM module powered by PV and an algorithm was developed to adjust irrigation schedules based on soil moisture data. …"

DMTD algorithm. حسب Yunhu Huang (21402795)

"…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …"

HRV variables, as a function of BIS^TM<60 and BIS^TM≥60 periods. حسب Anne Wojtanowski (17507992)

Convergence curve of the GL-SSL Model’s training loss function, illustrating the trend of loss reduction over 300 iterations, reflecting the model’s stability and rapid convergence... حسب Jiangjiang He (9676377)

Test function convergence curve. حسب Anqi Jiang (4583380)

"…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …"

Shortest Covering Paths, Trails, and Other Walks حسب Timothy Matisziw (9079352)

الموضوعات:

Parameter settings and performance evaluation criteria for WFG and UF test functions. حسب Zhongfeng Li (3965060)

الموضوعات:

HV comparison of QLNSGA-II and baseline algorithms on real-world multi-objective problems (RWMOP). The best value in each row is highlighted in bold. حسب Zhongfeng Li (3965060)

الموضوعات:

the functioning of BRPSO. حسب Hossein Jarrahi (22530251)

Risk and density functions for standard relapse from remission condition to disease <i>b</i> condition (similar shapes for standard relapse to disease <i>a</i>). حسب Benoîte de Saporta (20454662)

الموضوعات:

Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. حسب Mahmoudreza Moein (22127153)

CEC2022 and CEC2017 benchmark functions. حسب Chengcheng Ding (9013862)

Details of the metaheuristic algorithms. حسب Yanzhao Gu (21192659)

"…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …"

IEEE CEC 2022 benchmark function specifications. حسب Yang Gao (18005)

IEEE CEC 2017 benchmark function specifications. حسب Yang Gao (18005)