Development, application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: Tabu search, genetic, GRASP, and simulated annealing algorithms

Mohammadi, Mahmoud; Nastaran, Mahin; Sahebgharani, Alireza

doi:10.1016/j.compenvurbsys.2016.07.009

Cited by 52 publications

(25 citation statements)

References 42 publications

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“…The previous compatibility definition is slightly different from the definition given in (Mohammadi et al, 2016). In the previous definition, the geographic properties of the regions, such as lengths of lines that two regions share each other, are also considered.…”

Section: Problem Definitionmentioning

confidence: 96%

“…Our input defined in the previous paragraph are a particular case of the input of the LUOP in (Mohammadi et al, 2016). In that paper, instead of defining if there is a compatibility between two areas, they give a compatibility value between all pairs of areas.…”

Section: Problem Definitionmentioning

confidence: 99%

“…There are many methods proposed to solve the problem. Most of them are based on combinatorial optimization techniques such as simulated annealing (Santé-Riveira et al, 2008), tabu search (Qi et al, 2008), genetic algorithms (Karakostas and Economou, 2014), and a hybrid of those methods (Mohammadi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interactive Land-Use Optimization Using Laguerre Voronoi Diagram With Dynamic Generating Point Allocation

Chaidee¹,

Pakawanwong²,

Suppakitpaisarn³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, we devise an efficient method for the land-use optimization problem based on Laguerre Voronoi diagram. Previous Voronoi diagram-based methods are more efficient and more suitable for interactive design than discrete optimization-based method, but, in many cases, their outputs do not satisfy area constraints. To cope with the problem, we propose a force-directed graph drawing algorithm, which automatically allocates generating points of Voronoi diagram to appropriate positions. Then, we construct a Laguerre Voronoi diagram based on these generating points, use linear programs to adjust each cell, and reconstruct the diagram based on the adjustment. We adopt the proposed method to the practical case study of Chiang Mai University's allocated land for a mixed-use complex. For this case study, compared to other Voronoi diagram-based method, we decrease the land allocation error by 62.557%. Although our computation time is larger than the previous Voronoi-diagram-based method, it is still suitable for interactive design.

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Section: Problem Definitionmentioning

confidence: 96%

Section: Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Land-Use Optimization Using Laguerre Voronoi Diagram With Dynamic Generating Point Allocation

Chaidee¹,

Pakawanwong²,

Suppakitpaisarn³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Land conversion in these plans occurs only to improve compactness. Compactness is one of the most important objectives considered in most related studies [17,26,27,[31][32][33][34][35][36][37], but landslide risk also needs to be reduced to some extent, because land use conversion is very costly. We have obtained 100 non-dominated plans of various weight combinations, but the range of plans that can actually be selected is narrow.…”

Section: Discussionmentioning

confidence: 99%

Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea

et al. 2017

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Abstract:Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslide occurrences may increase under climate change, due to the increasing variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damage; however, planning in real life is a complex and nonlinear problem. For such multi-objective problems, genetic algorithms may be the most appropriate optimization tools. Therefore, in this study, we suggest a comprehensive land-use allocation plan using the Non-dominated Sorting Genetic Algorithm II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. Our study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in urban sprawl into the hazard zone where a large-scale landslide occurred in 2006. We obtain 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and second objectives. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.

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“…Urban growth modeling is an important method of urban land‐use optimization, which is considered complicated because the decisions on land‐use allocation must be made with respect to not only what activities to select, but also how much land to allocate to each and where to allocate the land (Cao et al, ; Cao, ). Thus far, many optimization algorithms have been employed to deal with land‐use optimization problems, for example: the Tabu search algorithm (Qi, Altinakar, Vieira, & Alidaee, ), simulated annealing algorithm (Santé‐Riveira, Boullón‐Magán, Crecente‐Maseda, & Miranda‐Barrós, ), genetic algorithm (Cao et al, ; Cao & Ye, ; Li & Parrott, ), particle swarm optimization algorithm (Masoomi, Mesgari, & Hamrah, ), ant colony optimization algorithm (Liu, Li, Shi, Huang, & Liu, ), bee colony algorithm (Yang, Sun, Peng, Shao, & Chi, ), and hybridization of these algorithms (Mohammadi, Nastaran, & Sahebgharani, ). Spatial optimization methods in urban growth modeling are commonly based on the cellular automata (CA) model, which coincides with the complex theory in which macroscale and global patterns can be generated from the microscale by local interactions of individual cells (Almeida, Gleriani, Castejon, & Soares‐Filho, ; Cao, Huang, Li, & Li, ; Li, Liu, & Yu, ; Lin, Huang, Chen, & Huang, ; Liu et al, ; Li, Chen, Liu, Liang, & Wang, ; Liu, Liang, Li, Xu, & Ou, ).…”

Section: Introductionmentioning

confidence: 99%

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

Cao

Huang

et al. 2019

Transactions in GIS

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This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolfinspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWOoptimized urban growth rules for CA describe the relationship between the spatial variables and the urban land-use status for each cell in the formation of "if-then." The GWO algorithm and CA model are then integrated as the GWO-CA model for urban growth simulation and optimization.By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO-CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management. | 673 CAO et al.

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Development, application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: Tabu search, genetic, GRASP, and simulated annealing algorithms

Cited by 52 publications

References 42 publications

Interactive Land-Use Optimization Using Laguerre Voronoi Diagram With Dynamic Generating Point Allocation

Interactive Land-Use Optimization Using Laguerre Voronoi Diagram With Dynamic Generating Point Allocation

Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

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