Sustainable land use optimization using Boundary-based Fast Genetic Algorithm

Cao, Kai; Huang, Bo; Wang, Shaowen; Lin, Hui

doi:10.1016/j.compenvurbsys.2011.08.001

Cited by 221 publications

(179 citation statements)

References 25 publications

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“…The objectives considered were to minimize conversion costs, maximize accessibility, and maximize compatibilities between land uses (Cao et al 2011). Cao et al (2012) extended their previous work to investigate the problem of how to plan and manage a rapid developing area in the future. The proposed approach used a boundary-based evolutionary algorithm, based on a reference point method, and the case study was the same as above.…”

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

“…The proposed approach used a boundary-based evolutionary algorithm, based on a reference point method, and the case study was the same as above. Many criteria, such as economic, environmental and ecological benefits, social equity including gross domestic product, conversion cost, geological suitability, ecological suitability, accessibility, Not In My Back Yard influence, compactness, and compatibility, were taken into account (Cao et al 2012). Similarly, Porta et al (2013) used parallel genetic algorithms to design a spatial decision support system for the development of municipal land use plans in Galicia, northwest Spain.…”

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

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Metaheuristics for agricultural land use optimization. A review

Memmah

Lescourret

Yao

et al. 2015

Agron. Sustain. Dev.

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Agricultural landscapes presently cover about 46 % of earth terrestrial surface. This cultivated area is decreasing, whereas the global food demand is projected to increase up to 70 % in 2050. The intensification of agriculture is not a solution to this food issue because intensive agriculture has often resulted in pollution and loss of biodiversity. On the other hand, mechanistic models with optimization algorithms can be used to design alternative land uses for sustainable agriculture. Here, we present a review of metaheuristics for land use optimization reported in 50 articles including 38 case studies carried out in 16 countries. Our main conclusions are: 1) the success of metaheuristics is problem-dependent. In general, metaheuristics enable search to escape from local optima and find a good global approximation solution.2) The choice of a given metaheuristic for solving a given problem seems to be driven by its historical use in a research team and by its popularity outside the metaheuristics research community, rather than by the characteristics of the problems to be solved and by the latest results from the metaheuristics research community. 3) Stakeholders of land use are increasingly involved at different levels of the land use optimization procedure and multi-actors decision-making methods are necessary to find trade-offs between their competing interests. 4) A future challenge is the use of parallelization techniques along with the hybridization of different metaheuristics or of metaheuristics with other optimization methods.

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Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

Metaheuristics for agricultural land use optimization. A review

Memmah

Lescourret

Yao

et al. 2015

Agron. Sustain. Dev.

View full text Add to dashboard Cite

show abstract

“…There are various methods for this purpose (e.g. weighted sum method: (Porta et al, 2013;Yang et al, 2015), goal programming: (Cao et al, 2012;Stewart et al, 2004) and fuzzy goal programming: (Chang & Ko, 2014). In this paper, the goal programming method represented in Eq.…”

Section: Formulation Of Multi-objective Land Use Optimization Problemmentioning

confidence: 99%

“…In the literature of land use optimization, context-based and suitability-related objectives were broadly indicated (Balling et al, 1999;Cao et al, 2012;Chandramouli et al, 2009;Duh & Brown, 2007;Karakostas & Economou, 2014;Liu, Lao, et al, 2012;Liu et al, 2013;Masoomi et al, 2013;Santé-Riveira et al, 2008;Stewart et al, 2004;Wang et al, 2004;Xiao et al, 2002). These functions were often slope, elevation, land price and distance-related factors (e.g.…”

Section: Formulation Of Multi-objective Land Use Optimization Problemmentioning

confidence: 99%

“…For the above reasons, solving land use optimization problem relies on the application of meta-heuristics and various researches had been assigned to adapt these algorithms with the framework of the land use optimization problem (Simulated Annealing: (Aerts & Heuvelink, 2002;Duh & Brown, 2007;Santé-Riveira et al, 2008); Tabu-Search: (Qi et al, 2008); Genetic Algorithm (GA): (Cao et al, 2011;Cao et al, 2012;Holzkämper & Seppelt, 2007;Janssen et al, 2008;Karakostas & Economou, 2014;Matthews, 2001;Stewart et al, 2004;Xiao et al, 2002;Zhang et al, 2010); Particle Swarm: (Liu, Lao, et al, 2012;Masoomi et al, 2013); Ant Colony: ; and Bee Colony (Yang et al, 2015)). Reviewing these researches show that although various algorithms adapted and examined by scholars for land use allocation, a slight part of the literature was dedicated to develop particle swarm based algorithms (PSO), and parallelizing particle swarm algorithm (PPSO) has not yet been considered.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Land Use Optimization Through Parallel Particle Swarm Algorithm: Case Study Baboldasht District of Isfahan, Iran

Sahebgharani¹

2016

JUEE

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Abstract:Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m 2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

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A two‐step spatially explicit optimization approach of integrating ecosystem services (ES) into land use planning (LUP) to generate the optimally sustainable schemes

et al. 2023

Land Degrad Dev

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Xuzhou, as an industrial centre and typical resource‐based city in China, is facing serious pressure on development transformation. The embedding of ecosystem services (ES) into land use planning (LUP) is of great significance to realize the coupling of ecology and society. This paper proposed a two‐step spatially explicit optimization approach of integrating ES into LUP, with consideration of macro‐requirements, spatial heterogeneity, and the spatially explicit basis. The first step was to construct a linear optimization model to obtain the land quantity structure corresponding to the maximized ES value. The second step was to spatially allocate land use structure to maximize the suitability of spatial units providing ES. The results showed that the land use structure corresponding to the maximization ES value of Xuzhou was obtained to satisfy the welfare of habitant and to create the ecological competitiveness. The optimal spatial layout of Xuzhou with maximum spatial suitability of providing ES was acquired through spatial optimization of this approach. ES was matched to the units with the high spatial suitability, and the spatial potential of ES was released. The conflicts among supporting services, provisioning services, regulating services, and cultural services were well managed with the equipment of multi‐objective trade‐off technology. The proposed ES embedding approach has good performance in the optimal allocation of land resources for ES maximization and in managing trade‐offs during multi‐objectives programming. Therefore, it is expected to be widely used for ES‐oriented LUP formulation.

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Sustainable land use optimization using Boundary-based Fast Genetic Algorithm

Cited by 221 publications

References 25 publications

Metaheuristics for agricultural land use optimization. A review

Metaheuristics for agricultural land use optimization. A review

Multi-Objective Land Use Optimization Through Parallel Particle Swarm Algorithm: Case Study Baboldasht District of Isfahan, Iran

A two‐step spatially explicit optimization approach of integrating ecosystem services (ES) into land use planning (LUP) to generate the optimally sustainable schemes

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