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Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a...

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Detalles Bibliográficos
Autores principales: Guo, Sha-sha, Wang, Jie-sheng, Guo, Meng-wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298269/
https://www.ncbi.nlm.nih.gov/pubmed/32587606
http://dx.doi.org/10.1155/2020/6502807
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author Guo, Sha-sha
Wang, Jie-sheng
Guo, Meng-wei
author_facet Guo, Sha-sha
Wang, Jie-sheng
Guo, Meng-wei
author_sort Guo, Sha-sha
collection PubMed
description Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.
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spelling pubmed-72982692020-06-24 Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm Guo, Sha-sha Wang, Jie-sheng Guo, Meng-wei Comput Intell Neurosci Research Article Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm. Hindawi 2020-06-08 /pmc/articles/PMC7298269/ /pubmed/32587606 http://dx.doi.org/10.1155/2020/6502807 Text en Copyright © 2020 Sha-sha Guo et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Guo, Sha-sha
Wang, Jie-sheng
Guo, Meng-wei
Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title_full Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title_fullStr Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title_full_unstemmed Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title_short Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm
title_sort z-shaped transfer functions for binary particle swarm optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298269/
https://www.ncbi.nlm.nih.gov/pubmed/32587606
http://dx.doi.org/10.1155/2020/6502807
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