Improving the performance of genetic algorithms for land-use allocation problems

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.

KEYWORDS:

• Multi-objective optimization
• land-use allocation
• genetic algorithm

Acknowledgements

Funding for this work was provided by the Swiss National Science Foundation (SNSF). It was part of a Doc.Mobility grant (P1EZP2_162222) and the project SUMSOR [Grant Number 406840_143057], which is part of the National Research Programme ‘NRP 68 – Sustainable use of soil as a resource’. This material is also based in part upon work supported by the U. S. National Science Foundation under Cooperative Agreement No. [Grant Number DBI-0939454]. We thank Silvia Dingwall and Uta Fink for language revision. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundations.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Funding for this work was provided by the Swiss National Science Foundation (SNSF). It was part of a Doc.Mobility grant [P1EZP2_162222] and the project SUMSOR [Grant Number 406840_143057], which is part of the National Research Programme ‘NRP 68 – Sustainable use of soil as a resource’. This material is also based in part upon work supported by the U. S. National Science Foundation under Cooperative Agreement No. [Grant Number DBI-0939454].