NC statistics versus total routers over 50 runs replicate per scenario using the MVO algorithm with various approaches. حسب Le Huu Binh (18340233)

الموضوعات:

Evolution of porosity (<i>ϕ</i>) as a function of rescaled CBL (relative age; ) measured in seal skulls. حسب Jonathan E Kings (20818367)

Compare algorithm parameter settings. حسب Yuqi Xiong (12343771)

"…Experimental validation shows that on 23 benchmark functions and the CEC2022 test suite, MESBOA significantly outperforms the original Secretary Bird Optimization Algorithm (SBOA) and other comparative algorithms (such as GWO, WOA, PSO, etc.) in terms of convergence speed, solution accuracy, and stability. …"

Statistical results of various algorithms. حسب ZeSheng Lin (20501356)

"…Secondly, the WOA’s position update formula was modified by incorporating inertia weight <i>ω</i> and enhancing convergence factor <i>α</i>, thus improving its capability for local search. …"

Sensitivity analysis of ASMC <i>σ</i> parameter. حسب Haoran Cui (7235234)

"…And its output was smoother, the jitter amplitude was significantly reduced by 1/3, and the output torque was more stable. In addition, when the parameter <i>H</i>₀ changed to 2<i>H</i>₀, the overall speed curve did not change much, with only a slight overshoot. …"

The algorithm is designed to identify patterns in the treatment dataset. حسب Belén Otero-Carrasco (21262360)

"…<p>This process involves two key functions. First, from lines 1 to 8, the algorithm identifies amino acids that meet the occurrence threshold, starting with patterns of length 1. …"

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. حسب Gursimran Singh (575288)

الموضوعات:

Classical benchmark functions [39]. حسب Junhao Wei (6816803)

"…<div><p>Whale Optimization Algorithm (WOA) is a biologically inspired metaheuristic algorithm with a simple structure and ease of implementation. …"

Effects of prey’s speed change probability (<i>K</i>_<i>s</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoff... حسب Hiroyuki Ichijo (13891153)

Definition of the pool of candidates for an AN target user <i>u</i>. حسب Diana Ramírez-Cifuentes (20700054)

Functional enrichment analysis. حسب Junjie Su (368807)

الموضوعات:

Payoff spaces under <i>D</i>_<i>i</i> combinations where behavioral switching occurred and under all other <i>D</i>_<i>i</i> combinations of non-behavioral swit... حسب Hiroyuki Ichijo (13891153)

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 حسب Robert Zomer (12796235)

Swarm-averaged relative link counts <i>U</i>_<i>r</i> as a function of simulation tick-count <i>t</i> for the different transmission tactics. حسب Paul Kinsler (3975620)

Effects of prey’s detection distance (<i>D</i>_<i>s</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoffs (<su... حسب Hiroyuki Ichijo (13891153)

Effects of predators’ speed change probability (<i>K</i>_<i>w</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium pa... حسب Hiroyuki Ichijo (13891153)

Effects of predators’ detection distance (<i>D</i>_<i>w</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoffs... حسب Hiroyuki Ichijo (13891153)

Average fitness and standard deviation of each algorithm across the seven engineering design problems. <i>Ave</i> indicates average fitness, <i>Std</i> indicates standard deviation... حسب Junhao Wei (6816803)

Landscapes of payoff differences (<i>P</i>_<i>diff</i>) and Nash equilibrium speed changes (_<i>i</i>) with the sensory–motor algorithm in the absence of the be... حسب Hiroyuki Ichijo (13891153)

Probability density functions (PDFs) of the simulated surveillance noise. حسب Sandor Beregi (22177739)