抄録
This paper proposes a four corners' heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners' (FC) heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature; with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 1230-1248 |
| ページ数 | 19 |
| ジャーナル | European Journal of Operational Research |
| 巻 | 183 |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2007 12 16 |
Fingerprint
ASJC Scopus subject areas
- Information Systems and Management
- Management Science and Operations Research
- Statistics, Probability and Uncertainty
- Applied Mathematics
- Modelling and Simulation
- Transportation
これを引用
Applying self-adaptive evolutionary algorithms to two-dimensional packing problems using a four corners' heuristic. / Binkley, Kevin J.; Hagiwara, Masafumi.
：: European Journal of Operational Research, 巻 183, 番号 3, 16.12.2007, p. 1230-1248.研究成果: Article
}
TY - JOUR
T1 - Applying self-adaptive evolutionary algorithms to two-dimensional packing problems using a four corners' heuristic
AU - Binkley, Kevin J.
AU - Hagiwara, Masafumi
PY - 2007/12/16
Y1 - 2007/12/16
N2 - This paper proposes a four corners' heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners' (FC) heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature; with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted.
AB - This paper proposes a four corners' heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners' (FC) heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature; with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted.
KW - Cutting
KW - Evolutionary algorithms
KW - Genetic algorithms
KW - Packing
KW - Simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=34447092995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34447092995&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2004.12.029
DO - 10.1016/j.ejor.2004.12.029
M3 - Article
AN - SCOPUS:34447092995
VL - 183
SP - 1230
EP - 1248
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -