Staffordshire University logo
STORE - Staffordshire Online Repository

An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis

BENKHELIFA, Elhadj and Farnsworth, Michael and tiwari, Ashutosh and Zhu, Meiling and MONIRI, Mansour (2011) An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis. Memetic Computing, 3 (3). pp. 175-197. ISSN 1865-9284

Full text not available from this repository. (Request a copy)

Abstract or description

The application of multi objective evolutionary algorithms (MOEA) in the design optimisation of microelectromechanical systems (MEMS) is of particular interest in this research. MOEA is a class of soft computing techniques of biologically inspired stochastic algorithms, which have proved to outperform their conventional counterparts in many design optimisation tasks. MEMS designers can utilise a variety of multi-disciplinary design tools that explore a complex design search space, however, still follow the traditional trial and error approaches. The paper proposes a novel framework, which couples both modelling and analysis tools to the most referenced MOEAs (NSGA-II and MOGA-II). The framework is validated and evaluated through a number of case studies of increasing complexity. The research presented in this paper unprecedentedly attempts to compare the performances of the mentioned algorithms in the application domain. The comparative study shows significant insights into the behaviour of both of the algorithms in the design optimisation of MEMS. The paper provides extended discussions and analysis of the results showing, overall, that MOGA-II outperforms NSGA-II, for the selected case studies.

Item Type: Article
Subjects: G100 Mathematics
G400 Computer Science
G700 Artificial Intelligence
Faculty: Faculty of Computing, Engineering and Sciences > Computing
Depositing User: Elhadj BENKHELIFA
Date Deposited: 17 Jun 2013 07:58
Last Modified: 23 Sep 2013 14:29
URI: http://eprints.staffs.ac.uk/id/eprint/1259

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000