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A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm
Published 2020“…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm
Published 2021“…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm
Published 2020“…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm
Published 2020“…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm
Published 2020“…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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Swarm intelligence algorithms for multi-objective max-min-ISP
Published 2023“…<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>…”
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Easy-to-Manufacture In-Line 2D Nano Antenna for Enhanced Radiation-Cooled IR Camouflage
Published 2023“…We innovatively adopted the detectable IR radiation power as the optimization fitness function, based on the particle swarm optimization (PSO) algorithm, to execute powerful cooling and simultaneously minimize the emitting within the atmospheric window for enhanced camouflage. …”
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Easy-to-Manufacture In-Line 2D Nano Antenna for Enhanced Radiation-Cooled IR Camouflage
Published 2023“…We innovatively adopted the detectable IR radiation power as the optimization fitness function, based on the particle swarm optimization (PSO) algorithm, to execute powerful cooling and simultaneously minimize the emitting within the atmospheric window for enhanced camouflage. …”
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Easy-to-Manufacture In-Line 2D Nano Antenna for Enhanced Radiation-Cooled IR Camouflage
Published 2023“…We innovatively adopted the detectable IR radiation power as the optimization fitness function, based on the particle swarm optimization (PSO) algorithm, to execute powerful cooling and simultaneously minimize the emitting within the atmospheric window for enhanced camouflage. …”
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