SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers
SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super sw...
Main Authors: | , , |
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Format: | Article |
Language: | English |
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Elsevier
2023-12-01
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Series: | SoftwareX |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023002327 |
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author | Kang Liang Zhang Xiukai Oleg Krakhmalev |
author_facet | Kang Liang Zhang Xiukai Oleg Krakhmalev |
author_sort | Kang Liang |
collection | DOAJ |
description | SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super swarm, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance in finding better optimal than QPSO and several other variants thus providing a competitive solution to optimization problems. |
first_indexed | 2024-03-08T22:56:41Z |
format | Article |
id | doaj.art-92c047f0284d450d88a20748386410a9 |
institution | Directory Open Access Journal |
issn | 2352-7110 |
language | English |
last_indexed | 2024-03-08T22:56:41Z |
publishDate | 2023-12-01 |
publisher | Elsevier |
record_format | Article |
series | SoftwareX |
spelling | doaj.art-92c047f0284d450d88a20748386410a92023-12-16T06:08:04ZengElsevierSoftwareX2352-71102023-12-0124101536SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layersKang Liang0Zhang Xiukai1Oleg Krakhmalev2Engineering Training and Innovation Education Center, Shanghai Polytechnic University, Shanghai 201209, China; Corresponding author.School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaDepartment of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, Moscow, Russian Federation, 4th Veshnyakovsky Passage, 4, Moscow 109456, RussiaSLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super swarm, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance in finding better optimal than QPSO and several other variants thus providing a competitive solution to optimization problems.http://www.sciencedirect.com/science/article/pii/S2352711023002327OptimizationParticle swarm optimizationQuantum-behaved particle swarm optimization |
spellingShingle | Kang Liang Zhang Xiukai Oleg Krakhmalev SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers SoftwareX Optimization Particle swarm optimization Quantum-behaved particle swarm optimization |
title | SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers |
title_full | SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers |
title_fullStr | SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers |
title_full_unstemmed | SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers |
title_short | SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers |
title_sort | slsl qpso quantum behaved particle swarm optimization with short lived swarm layers |
topic | Optimization Particle swarm optimization Quantum-behaved particle swarm optimization |
url | http://www.sciencedirect.com/science/article/pii/S2352711023002327 |
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