High performance genetic algorithm for land use planning

Porta, Juan; Parapar, Julio; Doallo, Ramón; Rivera, Francisco F.; Santé, Inés; Crecente, Rafael

doi:10.1016/j.compenvurbsys.2012.05.003

Cited by 94 publications

(72 citation statements)

References 26 publications

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“…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%

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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Section: Formulation Of Multi-objective Land Use Optimization Problemmentioning

confidence: 99%

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

Sahebgharani¹

2016

JUEE

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“…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. The approach took into account legal rules and constraints on some existing land uses in some areas, i.e., no change allowed.…”

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

“…Four non-fixed categories, namely nature, agriculture, forestry, urban, had to be allocated, optimizing two criteria and fulfilling the constraints. Criteria were related to land suitability and the shape regularity of the resulting land use patches (Porta et al 2013).…”

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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“…From the first perspective, which is more useful at the local scale, several works have been made [28,29] exhibiting good computing and spatial results, but also an enormous territorial assessment simplification, which has been restricted to a single suitability or fitness function. From the second approach, preferred in this research, where the allocation of a cell or polygon land use is dealt as a combinatory optimization problem or as an Integer Programming problem [5], there have been excellent quantitative and spatial distribution results [30] for diverse work scales (local and regional) and a more detailed territorial assessment through objective functions which lead to better land use allocations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Spatial Optimization: Sustainable Land Use Allocation at Sub-Regional Scale

García

Rosas²,

García-Ferrer³

et al. 2017

Sustainability

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Abstract:The rational use of territorial resources is a key factor in achieving sustainability. Spatial planning is an important tool that helps decision makers to achieve sustainability in the long term. This work proposes a multi-objective model for sustainable land use allocation known as MAUSS (Spanish acronym for "Modelo de Asignación de Uso Sostenible de Suelo") The model was applied to the Plains of San Juan, Puebla, Mexico, which is currently undergoing a rapid industrialization process. The main objective of the model is to generate land use allocations that lead to a territorial balance within regions in three main ways by maximizing income, minimizing negative environmental pressure on water and air through specific evaluations of water use and CO 2 emissions, and minimizing food deficit. The non-sorting genetic algorithm II (NSGA-II) is the evolutionary optimization algorithm of MAUSS. NSGA-II has been widely modified through a novel and efficient random initializing operator that enables spatial rationale from the initial solutions, a crossover operator designed to streamline the best genetic information transmission as well as diversity, and two geometric operators, geographic dispersion (GDO) and the proportion (PO), which strengthen spatial rationality. MAUSS provided a more sustainable land use allocation compared to the current land use distribution in terms of higher income, 9% lower global negative pressure on the environment and 5.2% lower food deficit simultaneously.

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High performance genetic algorithm for land use planning

Cited by 94 publications

References 26 publications

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

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

Metaheuristics for agricultural land use optimization. A review

Multi-Objective Spatial Optimization: Sustainable Land Use Allocation at Sub-Regional Scale

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