Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/22426
Title: A novel particle swarm optimization approach for product design and manufacturing
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.
0000-0003-1790-6987
Yıldız, Ali Rıza
F-7426-2011
7102365439
Keywords: Particle swarm algorithm
Receptor editing
Hybrid approach
Multi-pass turning
Design optimization
Multipass turning operations
Genetic algorithm
Parameter selection
Search
Automation & control systems
Engineering
Algorithms
Design
Industrial research
Learning algorithms
Optimization
Product design
Simulated annealing
Turning
Design optimization
Hybrid approach
Multi-pass turning
Receptor editing
Particle swarm optimization (PSO)
Issue Date: Jan-2009
Publisher: Springer
Citation: Yıldız, Ali R. (2009) "A novel particle swarm optimization approach for product design and manufacturing". International Journal of Advanced Manufacturing Technology, 40(5-6), 617-628.
Abstract: This paper presents a novel optimization approach that is a new hybrid optimization approach based on the particle swarm optimization algorithm and receptor editing property of immune system. The aim of the present research is to develop a new optimization approach and then to apply it in the solution of optimization problems in both the design and manufacturing areas. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate performance of the approach. The results obtained by the proposed approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and Hooke-Jeeves pattern search.
URI: https://doi.org/10.1007/s00170-008-1453-1
https://link.springer.com/article/10.1007%2Fs00170-008-1453-1
http://hdl.handle.net/11452/22426
ISSN: 0268-3768
Appears in Collections:Scopus
Web of Science

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