Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy

The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pa...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nana Li, Yanmin Liu, Qijun Shi, Shihua Wang, Kangge Zou
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/dec56b0a64b84d56b024d37aef8ee53c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:dec56b0a64b84d56b024d37aef8ee53c
record_format	dspace
spelling	oai:doaj.org-article:dec56b0a64b84d56b024d37aef8ee53c2021-11-15T01:19:43ZMultiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy1687-527310.1155/2021/6440338https://doaj.org/article/dec56b0a64b84d56b024d37aef8ee53c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6440338https://doaj.org/toc/1687-5273The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Therefore, to solve multiobjective problems is a challenging task. A multiobjective particle swarm optimization, which combined cosine distance measurement mechanism and novel game strategy, has been proposed in this article. The cosine distance measurement mechanism was adopted to update Pareto optimal set in the external archive. At the same time, the candidate set was established so that Pareto optimal set deleted from the external archive could be effectively replaced, which helped to maintain the size of the external archive and improved the convergence and diversity of the swarm. In order to strengthen the selection pressure of leader, this article combined with the game update mechanism, and a global leader selection strategy that integrates the game strategy including the cosine distance mechanism was proposed. In addition, mutation was used to maintain the diversity of the swarm and prevent the swarm from prematurely converging to the true Pareto front. The performance of the proposed competitive multiobjective particle swarm optimizer was verified by benchmark comparisons with several state-of-the-art multiobjective optimizer, including seven multiobjective particle swarm optimization algorithms and seven multiobjective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of optimization quality.Nana LiYanmin LiuQijun ShiShihua WangKangge ZouHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Nana Li Yanmin Liu Qijun Shi Shihua Wang Kangge Zou Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
description	The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Therefore, to solve multiobjective problems is a challenging task. A multiobjective particle swarm optimization, which combined cosine distance measurement mechanism and novel game strategy, has been proposed in this article. The cosine distance measurement mechanism was adopted to update Pareto optimal set in the external archive. At the same time, the candidate set was established so that Pareto optimal set deleted from the external archive could be effectively replaced, which helped to maintain the size of the external archive and improved the convergence and diversity of the swarm. In order to strengthen the selection pressure of leader, this article combined with the game update mechanism, and a global leader selection strategy that integrates the game strategy including the cosine distance mechanism was proposed. In addition, mutation was used to maintain the diversity of the swarm and prevent the swarm from prematurely converging to the true Pareto front. The performance of the proposed competitive multiobjective particle swarm optimizer was verified by benchmark comparisons with several state-of-the-art multiobjective optimizer, including seven multiobjective particle swarm optimization algorithms and seven multiobjective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of optimization quality.
format	article
author	Nana Li Yanmin Liu Qijun Shi Shihua Wang Kangge Zou
author_facet	Nana Li Yanmin Liu Qijun Shi Shihua Wang Kangge Zou
author_sort	Nana Li
title	Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
title_short	Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
title_full	Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
title_fullStr	Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
title_full_unstemmed	Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy
title_sort	multiobjective particle swarm optimization based on cosine distance mechanism and game strategy
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/dec56b0a64b84d56b024d37aef8ee53c
work_keys_str_mv	AT nanali multiobjectiveparticleswarmoptimizationbasedoncosinedistancemechanismandgamestrategy AT yanminliu multiobjectiveparticleswarmoptimizationbasedoncosinedistancemechanismandgamestrategy AT qijunshi multiobjectiveparticleswarmoptimizationbasedoncosinedistancemechanismandgamestrategy AT shihuawang multiobjectiveparticleswarmoptimizationbasedoncosinedistancemechanismandgamestrategy AT kanggezou multiobjectiveparticleswarmoptimizationbasedoncosinedistancemechanismandgamestrategy
_version_	1718428953486557184

Multiobjective Particle Swarm Optimization Based on Cosine Distance Mechanism and Game Strategy

Ejemplares similares