Record Details

Orthogonal simulated annealing for multiobjective optimization

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title Orthogonal simulated annealing for multiobjective optimization
 
Creator SUMAN, B
HODA, N
JHA, S
 
Subject genetic algorithm
global optimization
system reliability
multicriteria optimization
evolutionary algorithms
continuous-variables
design
performance
search
multiobjective optimization
fractional factorial analysis
orthogonal table
oed
osa
mosa
cmosa
sa
 
Description The paper proposes a new simulated annealing (SA) based multiobjective optimization algorithm, called orthogonal simulated annealing (OSA) algorithm in this work. The OSA algorithm incorporates an orthogonal experiment design (OED) with a simulated annealing based multiobjective algorithm aiming to provide an efficient multiobjective algorithm. OED involves several experiments based on an orthogonal table and a fractional factorial analysis to extract intelligently the best combination of decision vectors making the classical SA to explore search space effectively, to enhance convergence, and to improve quality of solutions in the Pareto set. These benefits have been tested by comparing the performance of OSA with one state-of-the-art multiobjective evolutionary algorithm (NSGA2) and one classical simulated annealing based multiobjective algorithm (CMOSA) considering multiobjective problems of varying degrees of complexity. The obtained Pareto sets by these three algorithms have been tested using standard methods like measure C, hypervolume comparison, etc. Simulation results show that the performance of and CPU time required by these algorithms are problem dependent, and with some problems, the OSA algorithm outperforms the other two algorithms. In particular, the comparison between OSA and CMOSA suggests that around 70% times OSA outperforms CMOSA and obtains a well diversified set of solutions. In addition, with some problems, OSA captures the Pareto fronts where CMOSA fails. Therefore, the development of OSA is noteworthy, and it provides an additional tool to solve multiobjective optimization problems. (C) 2009
 
Publisher PERGAMON-ELSEVIER SCIENCE LTD
 
Date 2011-08-25T21:02:13Z
2011-12-26T12:57:13Z
2011-12-27T05:47:11Z
2011-08-25T21:02:13Z
2011-12-26T12:57:13Z
2011-12-27T05:47:11Z
2010
 
Type Article
 
Identifier COMPUTERS & CHEMICAL ENGINEERING, 34(10), 1618-1631
0098-1354
http://dx.doi.org/10.1016/j.compchemeng.2009.11.015
http://dspace.library.iitb.ac.in/xmlui/handle/10054/11147
http://hdl.handle.net/10054/11147
 
Language en