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

Swarm intelligence algorithms for multi-objective max-min-ISP حسب Francisco Muñoz (9455441)

"…<p>Algorithms implemented on article "<strong>On the max-min influence spread problem: A multi-objective optimization approach</strong>"</p> <p><br></p> <p>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--spread SPREAD] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--prunelowdeg] [--excludenodes EXCLUDENODES] [--includeindegzero] [--nodepthcriteria] [--memfile MEMFILE] file</p> <p><br></p> <p>Calculates the best Influence Spread set on a Weighted Graph using PSO</p> <p><br></p> <p>positional arguments:</p> <p>file</p> <p><br></p> <p>options:</p> <p>-h, --help: show this help message and exit</p> <p>-a: read file input contents as an Adjacency Matrix</p> <p>-r: reverse nodes order, reads (b,a,w) instead of (a,b,w)</p> <p>-d D: line separator to use on file parsing</p> <p>-t T: number of times to execute this solver</p> <p>-i I: number of metaheuristic iterations per execution</p> <p>-q Q: fixed quota, use 0 = floor(n/2)+1</p> <p>--shape SHAPE: shape functions for binarization - list of implemented shape functions: s2, s2_neg, v2, v4</p> <p>--sym: consider graph as symmetric instead of directed</p> <p>--folder FOLDER: output folder</p> <p>--seed SEED: use custom seeds for metaheuristic pseudo RNG</p> <p>--spread SPREAD: use specific influence spread model funcion - list of implemented models: LT, IC</p> <p>--sum SUM: adds an extra value to all edge's weight</p> <p>--mh MH: metaheuristic to use - list of implemented metaheuristics: 1 (Swarm3), 3 (Swarm3_W), 4 (Swarm3_L), 5 (Swarm4)</p> <p>--tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</p> <p>--notags: do not use first row as node tags, instead tags will be calculated</p> <p>--prune: nodes with (1) outdegree = 0 and indegree > 0, and (2) with outdegree = 1 and neighbor's outdegree > 0 will be excluded</p> <p>--prunelowdeg: nodes with low outdegree or degree will be excluded</p> <p>--excludenodes EXCLUDENODES: nodes to skip, must be comma separated</p> <p>--includeindegzero: forces to include nodes with indegree = 0 on all executions</p> <p>--nodepthcriteria: particles will not tie off with spread depth in case of having same fitness and spread</p> <p>--memfile MEMFILE: memory dump of other execution</p>…"

Design example using the structure of the LecB lectin <i>Pseudomonas aeruginosa</i> strain PA14 (PDB ID: 5A6Y [67]) and the osprey workflow for fries/<i>EWAK</i>*. حسب Anna U. Lowegard (8051597)

"…This design consisted of 8.102 × 10¹¹ conformations and 441 sequences. fries limited this space to 5.704 × 10¹¹ conformations and 206 sequences. fries/<i>EWAK</i>* in combination reduced the amount of time taken by about 75% compared to <i>BBK</i>*. fries alone was responsible for roughly 50% of this speed-up. (B) 10 low-energy conformations included in the thermodynamic ensemble of the design sequence with mutations Q80I and I82F. …"

Fig 1 - حسب Yuh-Chin T. Huang (17867207)

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... حسب Ross J. Burton (10941910)

Results of multimodal two-dimensional function. حسب Yu-Jun Zhang (185411)

الموضوعات:

Results of CEC2014 benchmark function. حسب Yu-Jun Zhang (185411)

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Multi-peak multidimensional function results. حسب Yu-Jun Zhang (185411)

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Unimodal function results, dimension = 30. حسب Yu-Jun Zhang (185411)

الموضوعات:

Convergence curves of 27 benchmark functions. حسب Yu-Jun Zhang (185411)

الموضوعات:

(a) Radar chart of these algorithms (CEC2021), (b) The sorting diagram of these algorithms (CEC2021). حسب Yu Liu (6938)

The optimization results of each algorithm for the CEC2019 benchmark functions. حسب Guangwei Liu (181992)

الموضوعات:

Functional pathways and evaluation of mutation between the two MRG-related risk groups. حسب Xuefen Lin (17785367)

Ranking of features selected by scatter search and fast correlation base filter algorithms. حسب Pedro Manuel Martínez-García (10005013)

The optimization result of 10 benchmark functions for different algorithms (D = 10). حسب Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 30). حسب Yu Li (183836)

IGD values of different algorithms under different standard test functions. حسب Jun Sun (48981)

The result of CEC2010 standard functions for different algorithms (D = 30). حسب Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 100). حسب Yu Li (183836)

The convergence curves of the function in different dimensions. حسب Yu Li (183836)