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Article: An improved artificial immune system for seeking the Pareto front of land-use allocation problem in large areas
Title | An improved artificial immune system for seeking the Pareto front of land-use allocation problem in large areas |
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Authors | |
Keywords | artificial immune system land-use allocation problem multi-objective optimization Pareto front |
Issue Date | 8-Nov-2012 |
Publisher | Taylor and Francis Group |
Citation | International Journal of Geographical Information Science, 2012, v. 27, n. 5, p. 922-946 How to Cite? |
Abstract | The Pareto front can provide valuable information on land-use planning decision by revealing the possible trade-offs among multiple, conflicting objectives. However, seeking the Pareto front of land-use allocation is much more difficult than finding a unique optimal solution, especially when dealing with large-area regions. This article proposes an improved artificial immune system for multi-objective land-use allocation (AIS-MOLA) to tackle this challenging task. The proposed AIS is equipped with three modified operators, namely (1) a heuristic hypermutation based on compromise programming, (2) a non-dominated neighbour-based proportional cloning and (3) a novel crossover operator that preserves connected patches. To validate the proposed algorithm, it was applied in a hypothetical land-use allocation problem. Compared with the Pareto Simulated Annealing (PSA) method, AIS-MOLA can generate solutions more approximate to the Pareto front, with computation time amounting to only 5.1% of PSA. In addition, AIS-MOLA was also applied in the case study of Panyu, Guangdong, PR China, a large area with cells. Experimental results indicate that this algorithm, even dealing with large-area land-use allocation problems, is capable of generating optimal alternative solutions approximate to the true Pareto front. Moreover, the distribution of these solutions can quantitatively demonstrate the complex trade-offs between the spatial suitability and the compactness in the study area. Software and supplementary materials are available at http://www.geosimulation.cn/AIS-MOLA/. |
Persistent Identifier | http://hdl.handle.net/10722/337473 |
ISSN | 2021 Impact Factor: 5.152 2020 SCImago Journal Rankings: 1.294 |
DC Field | Value | Language |
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dc.contributor.author | Huang, K | - |
dc.contributor.author | Liu, X | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | Liang, J | - |
dc.contributor.author | He, S | - |
dc.date.accessioned | 2024-03-11T10:21:09Z | - |
dc.date.available | 2024-03-11T10:21:09Z | - |
dc.date.issued | 2012-11-08 | - |
dc.identifier.citation | International Journal of Geographical Information Science, 2012, v. 27, n. 5, p. 922-946 | - |
dc.identifier.issn | 1365-8816 | - |
dc.identifier.uri | http://hdl.handle.net/10722/337473 | - |
dc.description.abstract | <p>The Pareto front can provide valuable information on land-use planning decision by revealing the possible trade-offs among multiple, conflicting objectives. However, seeking the Pareto front of land-use allocation is much more difficult than finding a unique optimal solution, especially when dealing with large-area regions. This article proposes an improved artificial immune system for multi-objective land-use allocation (AIS-MOLA) to tackle this challenging task. The proposed AIS is equipped with three modified operators, namely (1) a heuristic hypermutation based on compromise programming, (2) a non-dominated neighbour-based proportional cloning and (3) a novel crossover operator that preserves connected patches. To validate the proposed algorithm, it was applied in a hypothetical land-use allocation problem. Compared with the Pareto Simulated Annealing (PSA) method, AIS-MOLA can generate solutions more approximate to the Pareto front, with computation time amounting to only 5.1% of PSA. In addition, AIS-MOLA was also applied in the case study of Panyu, Guangdong, PR China, a large area with <img src="https://www.tandfonline.com/cms/asset/93ac2ae5-d0b8-4dbd-9900-9cffde87eebf/tgis_a_730147_o_ilm0001.gif" alt=""> cells. Experimental results indicate that this algorithm, even dealing with large-area land-use allocation problems, is capable of generating optimal alternative solutions approximate to the true Pareto front. Moreover, the distribution of these solutions can quantitatively demonstrate the complex trade-offs between the spatial suitability and the compactness in the study area. Software and supplementary materials are available at <a href="http://www.geosimulation.cn/AIS-MOLA/">http://www.geosimulation.cn/AIS-MOLA/</a>.<br></p> | - |
dc.language | eng | - |
dc.publisher | Taylor and Francis Group | - |
dc.relation.ispartof | International Journal of Geographical Information Science | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | artificial immune system | - |
dc.subject | land-use allocation problem | - |
dc.subject | multi-objective optimization | - |
dc.subject | Pareto front | - |
dc.title | An improved artificial immune system for seeking the Pareto front of land-use allocation problem in large areas | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/13658816.2012.730147 | - |
dc.identifier.scopus | eid_2-s2.0-84878123032 | - |
dc.identifier.volume | 27 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 922 | - |
dc.identifier.epage | 946 | - |
dc.identifier.eissn | 1365-8824 | - |
dc.identifier.issnl | 1365-8816 | - |