Abstract
This paper examines the effect of initial solutions on the performance of an iterated local search (ILS) algorithm for the permutation flowshop problem with the objective of minimizing total flowtime. An ILS algorithm is applied to a set of test problems, and in each separate trial the algorithm is started from an initial solution generated by one of six different methods. Experimental results indicate that initial solutions generated by a neural network are more effective in promoting the performance of the ILS algorithm towards better solutions. A modified version of the ILS algorithm, in which an initially restricted neighborhood search is gradually expanded with each iteration, is also proposed and tested. The results from this modified ILS compare very favorably with published results from a traditional ILS approach.
| Original language | English |
|---|---|
| Title of host publication | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: Jun 10 2012 → Jun 15 2012 |
Other
| Other | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
|---|---|
| Country | Australia |
| City | Brisbane, QLD |
| Period | 6/10/12 → 6/15/12 |
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Keywords
- iterated local search
- neural networks
- permutation flowshop
- total flowtime
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop. / El-Bouri, Ahmed.
2012 IEEE Congress on Evolutionary Computation, CEC 2012. 2012. 6256550.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop
AU - El-Bouri, Ahmed
PY - 2012
Y1 - 2012
N2 - This paper examines the effect of initial solutions on the performance of an iterated local search (ILS) algorithm for the permutation flowshop problem with the objective of minimizing total flowtime. An ILS algorithm is applied to a set of test problems, and in each separate trial the algorithm is started from an initial solution generated by one of six different methods. Experimental results indicate that initial solutions generated by a neural network are more effective in promoting the performance of the ILS algorithm towards better solutions. A modified version of the ILS algorithm, in which an initially restricted neighborhood search is gradually expanded with each iteration, is also proposed and tested. The results from this modified ILS compare very favorably with published results from a traditional ILS approach.
AB - This paper examines the effect of initial solutions on the performance of an iterated local search (ILS) algorithm for the permutation flowshop problem with the objective of minimizing total flowtime. An ILS algorithm is applied to a set of test problems, and in each separate trial the algorithm is started from an initial solution generated by one of six different methods. Experimental results indicate that initial solutions generated by a neural network are more effective in promoting the performance of the ILS algorithm towards better solutions. A modified version of the ILS algorithm, in which an initially restricted neighborhood search is gradually expanded with each iteration, is also proposed and tested. The results from this modified ILS compare very favorably with published results from a traditional ILS approach.
KW - iterated local search
KW - neural networks
KW - permutation flowshop
KW - total flowtime
UR - http://www.scopus.com/inward/record.url?scp=84866883649&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866883649&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6256550
DO - 10.1109/CEC.2012.6256550
M3 - Conference contribution
AN - SCOPUS:84866883649
SN - 9781467315098
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
ER -