Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II

Cao, Kai; Batty, Michael; Huang, Bo; Liu, Yan; Yu, Le; Chen, Jiongfeng

doi:10.1080/13658816.2011.570269

Cited by 199 publications

(173 citation statements)

References 20 publications

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“…Maximizes the water quality and the production benefits from agricultural exploitation Tabu search Altinakar and Qi (2008), Lehmann et al (2013), Rabotyagov et al (2012), Panagopoulos et al (2013), Yu et al (2011), Seppelt andVoinov (2002), Voinov (2003), Seppelt et al (2013), Georgiou and Papamichail (2008), Fotakis and Sidiropoulos (2012), Fotakis and Sidiropoulos (2014) Moulton et al (2009), Cao et al (2011), Morio et al (2013, Ma et al (2011), Masoomi et al (2012), Huang (2010, Liu et al (2013a), Liu et al (2013c), Yang et al (2012), Duh and Brown (2007) …”

Section: Discussionmentioning

confidence: 99%

“…The objective was to allocate five land uses, optimizing three objectives and fulfilling a set of constraints reflecting the user's and land planner's restrictions. 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.…”

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

“…Eight landscape-ecology metrics were considered as criteria to be maximized (Moulton et al 2009). The NSGA-II algorithm was also used by Cao et al (2011) for multi-objective optimization of land use. The authors applied it to the example of Tongzhou New Town, China.…”

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

“…Metaheuristics do not assume any mathematical property of the treated problem and can deal with multiple points of view of stakeholders in a reasonable amount of computation time. Metaheuristics, such as genetic algorithms (Cao et al 2011;Stewart et al 2004), simulated annealing (Duh andBrown 2007;Santé-Riveira et al 2008), particle swarm optimization (Liu et al 2013a;Masoomi et al 2012), and ant colony optimization Mousa and El_Desoky 2013), combined with multiobjective optimization techniques can generate diversified land use planning scenarios (more details below). These approaches are efficient for solving land use spatial allocation problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Discussionmentioning

confidence: 99%

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

Section: Studies Using Genetic Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metaheuristics for agricultural land use optimization. A review

Memmah

Lescourret

Yao

et al. 2015

Agron. Sustain. Dev.

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“…MOEAs are essentially heuristic methods that are often more efficient than exact approaches, they are less susceptible to the shape or continuity of the Pareto-optimal frontier (Coello 1999). They are derived from evolutionary algorithms (EAs) and have been successfully demonstrated as efficient posterior approaches in solving various multi-objective spatial optimization problems (Xiao et al 2002(Xiao et al , 2007Farhana and Murray 2008;Lee and Xiao 2009;Huang et al 2008;Cao et al 2011;Wu et al 2011;Tong and Murray 2012;Bhattacharya and Bandyopadhyay 2010;Lančinskas and _ Zilinskas 2013). The main advantage of MOEA is their ability to deal simultaneously with a set of possible solutions and allow one to find multiple Pareto-optimal solutions in a single simulation run, instead of having to perform a series of separate runs as in the case of traditional mathematical programming methods (Deb et al 2002;Georgiadou et al 2010;Srinivas and Deb 1994).…”

mentioning

confidence: 99%

Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

Zhao

Chen

2014

Nat Hazards

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Emergency rescue facility is an indispensable component of urban system and important for public safety. Due to the special characteristics and complexity of urban environmental accidents, locations of emergency rescue facilities should be optimized, and the decision criteria should be comprehensive, taking into account not only the spatial relations of these facilities but also the potential hazard of environmental accidents. This study proposes an innovative methodological framework to optimize the locations of emergency rescue facilities in the contexts of large-scale urban environmental accidents. Risk-based approach is involved to delineate the potential hazard of environmental accidents to the adjacent urban areas, and a three-objective decision optimization model is constructed. Moreover, an appropriate spatial representation and encoding strategy is designed and coupled with the non-dominated sorting genetic algorithm-II for model solving. A case study is presented to demonstrate the methodology, and it provides evidence that the involvement of risk mapping into the model construction has significant effect on the optimization result. The findings show that the strategy proposed in this study has the potential to be a useful decision support tool for urban planning with respect to public safety.

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