Schematic diagram of the experimental algorithm. by Ronghua Meng (20978441)

“…On the basis of stable egg carrying, a carrying motion mathematical model is established, and the genetic algorithm (GA) is chosen to optimize the structural parameters. …”

Table_1_Vestibular assessment in children with sensorineural hearing loss: diagnostic accuracy and proposal for a diagnostic algorithm.DOCX by Max Gerdsen (13825351)

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…<p>Background and Purpose: Stroke-related functional risk scores are used to predict patients' functional outcomes following a stroke event. …”

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Test function evolution curve. by Minan Tang (20389941)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

Comparative analysis of algorithms. by Xumin Zhao (18261643)

“…This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. …”

Parameter settings for metaheuristic algorithms. by Junhao Wei (6816803)

“…The performance of the canonical WOA is improved through innovative strategies: first, an initialization process using Good Nodes Set is introduced to ensure that the search starts from a higher-quality baseline; second, a distance-based guided search strategy is employed to adjust the search direction and intensity by calculating the distance to the optimal solution, which enhances the algorithm’s ability to escape local optima; and lastly, LSWOA introduces an enhanced spiral updating strategy, while the enhanced spiral-enveloping prey strategy effectively balances exploration and exploitation by dynamically adjusting the spiral shape parameters to adapt to different stages of the search, thereby more accurately updating the positions of individuals and improving convergence speed. …”

Mean training time of different algorithms. by Wei Liu (20030)

“…We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. …”

Algorithm ranking under different dimensions. by Wei Liu (20030)

“…We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. …”

<b></b>Magnetic Kernels: A Theoretical and Algorithmic Framework for the P vs NP Problem by DURGHAM QARALLEH (21904172)

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

Data from: Structural and Functional Analysis of Multi-interface Domains by ZHAO Liang (8657694)

“…This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. …”

Running time comparison of different algorithms. by Shuang Guo (294074)

“…<div><p>To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. …”

PFC value of algorithms on two datasets. by Xing Chen (140292)

“…However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. …”

Pictorial representation of the edge-cutting algorithm. by Pratik Mullick (12851011)

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

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

High-dimensional benchmark test functions. by Ruyi Dong (9038174)

“…In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. …”