Multimodal benchmark functions. by Xiaofeng Qu (577153)

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Unimodal benchmark functions. by Xiaofeng Qu (577153)

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Swarm intelligence algorithms for multi-objective max-min-ISP by 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>…”

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... by Ross J. Burton (10941910)

Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

Genetic algorithm flowchart. by Dingming Yang (8505990)

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Functional effect prediction of FUS mutations by ten nsSNP prediction algorithms. by L. F. S. Bonet (11502082)

Convergence curve of reactive power transmission line loss minimization based on (a) the HFPSO algorithm and (b) the PSO algorithm. by Abdullah Khan (377292)

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

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The comparison results of different algorithms on 23 benchmark functions with D=30. by Yu Li (183836)

The comparison results of different algorithms on CEC2017 functions with D=30. by Yu Li (183836)

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …”

Results of multimodal benchmark functions. by Xiaofeng Qu (577153)

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Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)