Pareto-ranking based quantum-behaved particle swarm optimization for multiobjective optimization
type: Item
instance of: scholarly article
title: Pareto-ranking based quantum-behaved particle swarm optimization for multiobjective optimization
zbMATH Open document ID: 1394.90582
DOI: 10.1155/2015/940592
author: Na Tian
author: Zhicheng Ji
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: A study on Pareto-ranking based quantum-behaved particle swarm optimization (QPSO) for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection) is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 81P68
zbMATH DE Number: 6927626
Wikidata QID: Q59120355
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/940592
OpenAlex ID: W1565353536
cites work: Routing and scheduling in a flexible job shop by tabu search
cites work: Pareto-optimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic
cites work: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems
cites work: A novel strategy of Pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO)
description: scientific article; zbMATH DE number 6927626