Self-Regulated Particle Swarm Multi-Task Optimization

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For ex...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaolong Zheng, Deyun Zhou, Na Li, Tao Wu, Yu Lei, Jiao Shi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	evolutionary multitasking evolutionary multi-task optimization multi-task optimization knowledge transfer particle swarm optimization Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e85b81237d8645ea89b1801ba2cdfbb7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e85b81237d8645ea89b1801ba2cdfbb7
record_format	dspace
spelling	oai:doaj.org-article:e85b81237d8645ea89b1801ba2cdfbb72021-11-25T18:56:56ZSelf-Regulated Particle Swarm Multi-Task Optimization10.3390/s212274991424-8220https://doaj.org/article/e85b81237d8645ea89b1801ba2cdfbb72021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7499https://doaj.org/toc/1424-8220Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.Xiaolong ZhengDeyun ZhouNa LiTao WuYu LeiJiao ShiMDPI AGarticleevolutionary multitaskingevolutionary multi-task optimizationmulti-task optimizationknowledge transferparticle swarm optimizationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7499, p 7499 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	evolutionary multitasking evolutionary multi-task optimization multi-task optimization knowledge transfer particle swarm optimization Chemical technology TP1-1185
spellingShingle	evolutionary multitasking evolutionary multi-task optimization multi-task optimization knowledge transfer particle swarm optimization Chemical technology TP1-1185 Xiaolong Zheng Deyun Zhou Na Li Tao Wu Yu Lei Jiao Shi Self-Regulated Particle Swarm Multi-Task Optimization
description	Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.
format	article
author	Xiaolong Zheng Deyun Zhou Na Li Tao Wu Yu Lei Jiao Shi
author_facet	Xiaolong Zheng Deyun Zhou Na Li Tao Wu Yu Lei Jiao Shi
author_sort	Xiaolong Zheng
title	Self-Regulated Particle Swarm Multi-Task Optimization
title_short	Self-Regulated Particle Swarm Multi-Task Optimization
title_full	Self-Regulated Particle Swarm Multi-Task Optimization
title_fullStr	Self-Regulated Particle Swarm Multi-Task Optimization
title_full_unstemmed	Self-Regulated Particle Swarm Multi-Task Optimization
title_sort	self-regulated particle swarm multi-task optimization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e85b81237d8645ea89b1801ba2cdfbb7
work_keys_str_mv	AT xiaolongzheng selfregulatedparticleswarmmultitaskoptimization AT deyunzhou selfregulatedparticleswarmmultitaskoptimization AT nali selfregulatedparticleswarmmultitaskoptimization AT taowu selfregulatedparticleswarmmultitaskoptimization AT yulei selfregulatedparticleswarmmultitaskoptimization AT jiaoshi selfregulatedparticleswarmmultitaskoptimization
_version_	1718410561346076672

Self-Regulated Particle Swarm Multi-Task Optimization

Ejemplares similares