Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 907-930 |
| Seitenumfang | 24 |
| Fachzeitschrift | International Journal of Geographical Information Science |
| Jahrgang | 32 |
| Ausgabenummer | 5 |
| Publikationsstatus | Veröffentlicht - 2018 |
| Extern publiziert | Ja |
Abstract
Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.
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- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Sozialwissenschaften (insg.)
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in: International Journal of Geographical Information Science, Jahrgang 32, Nr. 5, 2018, S. 907-930.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Improving the performance of genetic algorithms for land-use allocation problems
AU - Schwaab, J.
AU - Deb, K.
AU - Goodman, E.
AU - Lautenbach, S.
AU - van Strien, M.J.
AU - Grêt-Regamey, A.
N1 - Publisher Copyright: © 2017 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2018
Y1 - 2018
N2 - Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.
AB - Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.
KW - genetic algorithm
KW - land-use allocation
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85039050964&partnerID=8YFLogxK
U2 - 10.1080/13658816.2017.1419249
DO - 10.1080/13658816.2017.1419249
M3 - Article
VL - 32
SP - 907
EP - 930
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 5
ER -