Comparison of benchmark function results between improved algorithms. by Xiaoyi Zhang (577299)

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

Algorithm implemented using SQL with fulltext search feature. by Kathleen M. Akgün (3697702)

Box plot of the ADGWDTO and competing algorithms for the seven benchmark functions. by El-Sayed M. El-kenawy (14581088)

Optimization algorithms performance comparison across benchmark functions. by Sundreen Asad Kamal (20390818)

Alpha Max Plus Beta Min Algorithm and Arctangent Calculation using Simulink by Ara A. Assim (12512428)

Comparison of results of algorithms under five standard functions. by Kemal Gökhan Nalbant (18858009)

The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

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Three-dimensional function image for testing algorithm performance. by Jianpeng Zhang (528185)

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Time spent on image denoising using different algorithms. by Bingbing Li (461702)

Results of the PSO-Kmeans-ANMS algorithm for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

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Results of the PSO-Kmeans-ANMS algorithm for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

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Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

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>…”

The weight vector iterative scheme of adaptive filters by using the hyperbolic function. by Sihai Guan (11547518)

Results of searching performance of different algorithm models on the Sphere function and Griewank function. by Rui Zhang (13940)

Results of searching performance of the improved NAG algorithm on Sphere function and Griewank function. by Rui Zhang (13940)

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Main parameters of braking system. by Honglei Pang (22693724)

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