<b>Opti2Phase</b>: Python scripts for two-stage focal reducer حسب Morgan Najera (21540776)

"…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…"

Convergence curve of real power transmission line loss minimization by using (a) the HFPSO algorithm and (b) the PSO algorithm. حسب Abdullah Khan (377292)

SP results of algorithms in the CEC 2009 test functions. حسب Vahid Goodarzimehr (21343124)

MS results of algorithms in the CEC 2009 test functions. حسب Vahid Goodarzimehr (21343124)

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz حسب Faizan Ali (21689861)

الموضوعات:

An expectation-maximization algorithm for finding noninvadable stationary states. حسب Robert Marsland (8616483)

"…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …"

<b>Algorithm Pseudocode</b> حسب Yibin Zhao (22425801)

Flowchart of MOSRS algorithm. حسب Vahid Goodarzimehr (21343124)

"…To test the performance, MOSRS is applied to the most challenging test functions set (CEC 2009) and 21 real and constrained world problems, being compared with a total of eleven metaheuristics: NSGA-II, NSGA-III, MOEA/D, MOPSO, MOGWO, ARMOEA, TiGE2, CCMO, ToP, and AnD. …"

Verification of the in-line PS function. حسب Raul Castañeda (13932971)

Boxplot of PLNMFG and other eight state-of-the-art algorithms measured by (a) ACC and (b) AMI on six real datasets. حسب Hui Yuan (402180)

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. حسب Francisco Páscoa dos Santos (16510676)

"…Inertia was calculated using the scikit learn module in Python. B) Resulting cluster distance from hierarchical clustering to averaged functional connectivity from healthy subjects, with different numbers of clusters. …"

Continuous Probability Distributions generated by the PIPE Algorithm حسب LUIS G.B. PINHO (14073372)

"…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …"

Listed python codes of our algorithm with explanations (Python 3.9 recommended). حسب Takayuki Niizato (162226)

The details of the Scelestial algorithm. حسب Mohammad-Hadi Foroughmand-Araabi (6658772)

"…<p>The inputs to the Scelestial algorithm are a) a set of sequences <i>S</i>, b) the degree of restriction of the restricted Steiner tree <i>k</i>. …"

Adaptive H-REMD simulation algorithm. حسب Danial Pourjafar-Dehkordi (7165523)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. حسب Raul Castañeda (13932971)

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. حسب 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.…"

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection حسب Samin Aref (4683934)

"…</p><p dir="ltr"><br></p><p dir="ltr">For more information about the data, one may refer to the article below:</p><p dir="ltr">Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. …"

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. حسب Panfei Li (22379086)

"…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…"

Results of real data application. حسب Yuhan Xie (9937449)

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