Improving the performance of genetic algorithms for land-use allocation problems. Issue 5 (4th May 2018)

• Record Type: Journal Article
• Authors: Schwaab, Jonas; Deb, Kalyanmoy; Goodman, Erik; Lautenbach, Sven; van Strien, Maarten J.; Grêt-Regamey, Adrienne
• Is Part Of: International journal of geographical information science. Volume 32:Issue 5(2018)
• Page Start: 907
• Page End: 930
• Publication Date: 2018-05-04
• Subjects: Multi-objective optimization -- land-use allocation -- genetic algorithm; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1419249
• Languages: English
• ISSNs: 1365-8816

Abstract

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.