Multi-objective, multi-domain genetic optimization of a hydraulic rescue spreader

Thomas A. Sullivan, James D Van De Ven

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, general strategies are presented by which a multi-domain, multi-objective, mechanism-based optimization problem may be efficiently formulated and solved by means of a genetic algorithm. These strategies include integration of traditional precision position techniques with genetic optimization, efficient selection of design variables and search bounds, and a nested optimization structure. A case study illustrating these methods is presented in which a hydraulic rescue spreader is simultaneously optimized for four objectives relating to structural efficiency and kinematic behavior. The solution obtained is shown to be equal or superior to a comparable commercially available device with respect to all four objectives.

Original languageEnglish (US)
Pages (from-to)35-51
Number of pages17
JournalMechanism and Machine Theory
Volume80
DOIs
StatePublished - Jan 1 2014

Fingerprint

Spreaders
Hydraulics
Kinematics
Genetic algorithms

Keywords

  • Evolutionary techniques
  • Genetic algorithms
  • Mechanism optimization
  • Mechanism synthesis
  • Multi-domain optimization
  • Multi-objective optimization

Cite this

Multi-objective, multi-domain genetic optimization of a hydraulic rescue spreader. / Sullivan, Thomas A.; Van De Ven, James D.

In: Mechanism and Machine Theory, Vol. 80, 01.01.2014, p. 35-51.

Research output: Contribution to journalArticle

@article{2ddddce457504092b2e4edd330985fce,
title = "Multi-objective, multi-domain genetic optimization of a hydraulic rescue spreader",
abstract = "In this paper, general strategies are presented by which a multi-domain, multi-objective, mechanism-based optimization problem may be efficiently formulated and solved by means of a genetic algorithm. These strategies include integration of traditional precision position techniques with genetic optimization, efficient selection of design variables and search bounds, and a nested optimization structure. A case study illustrating these methods is presented in which a hydraulic rescue spreader is simultaneously optimized for four objectives relating to structural efficiency and kinematic behavior. The solution obtained is shown to be equal or superior to a comparable commercially available device with respect to all four objectives.",
keywords = "Evolutionary techniques, Genetic algorithms, Mechanism optimization, Mechanism synthesis, Multi-domain optimization, Multi-objective optimization",
author = "Sullivan, {Thomas A.} and {Van De Ven}, {James D}",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.mechmachtheory.2014.04.019",
language = "English (US)",
volume = "80",
pages = "35--51",
journal = "Mechanism and Machine Theory",
issn = "0374-1052",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Multi-objective, multi-domain genetic optimization of a hydraulic rescue spreader

AU - Sullivan, Thomas A.

AU - Van De Ven, James D

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, general strategies are presented by which a multi-domain, multi-objective, mechanism-based optimization problem may be efficiently formulated and solved by means of a genetic algorithm. These strategies include integration of traditional precision position techniques with genetic optimization, efficient selection of design variables and search bounds, and a nested optimization structure. A case study illustrating these methods is presented in which a hydraulic rescue spreader is simultaneously optimized for four objectives relating to structural efficiency and kinematic behavior. The solution obtained is shown to be equal or superior to a comparable commercially available device with respect to all four objectives.

AB - In this paper, general strategies are presented by which a multi-domain, multi-objective, mechanism-based optimization problem may be efficiently formulated and solved by means of a genetic algorithm. These strategies include integration of traditional precision position techniques with genetic optimization, efficient selection of design variables and search bounds, and a nested optimization structure. A case study illustrating these methods is presented in which a hydraulic rescue spreader is simultaneously optimized for four objectives relating to structural efficiency and kinematic behavior. The solution obtained is shown to be equal or superior to a comparable commercially available device with respect to all four objectives.

KW - Evolutionary techniques

KW - Genetic algorithms

KW - Mechanism optimization

KW - Mechanism synthesis

KW - Multi-domain optimization

KW - Multi-objective optimization

UR - http://www.scopus.com/inward/record.url?scp=84901426226&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901426226&partnerID=8YFLogxK

U2 - 10.1016/j.mechmachtheory.2014.04.019

DO - 10.1016/j.mechmachtheory.2014.04.019

M3 - Article

VL - 80

SP - 35

EP - 51

JO - Mechanism and Machine Theory

JF - Mechanism and Machine Theory

SN - 0374-1052

ER -