A framework for constrained optimization problems based on a modified particle swarm optimization
type: Item
instance of: scholarly article
title: A framework for constrained optimization problems based on a modified particle swarm optimization
zbMATH Open document ID: 1400.90280
DOI: 10.1155/2016/8627083
author: Biwei Tang
author: Zhanxia Zhu
author: Jianjun Luo
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: This paper develops a particle swarm optimization (PSO) based framework for constrained optimization problems (COPs). Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011) algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6953689
Wikidata QID: Q59140963
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/8627083
OpenAlex ID: W2503621173
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description: scientific article; zbMATH DE number 6953689