Heuristic Methods

Malczewski, Jacek; Rinner, Claus

doi:10.1007/978-3-540-74757-4_6

Cited by 6 publications

(3 citation statements)

References 110 publications

(255 reference statements)

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“…The PSO with the fixed parameters was repeated for 20 runs in each scenario, and the statistics of three groups of fitness values are reported in Table . The small coefficient of variation of fitness values in each group (less than 0.02) demonstrates the stability of the PSO (Malczewski & Rinner, ). Furthermore, we performed a one‐way analysis of variance (ANOVA) on the grouped fitness values, and a statistically significant difference exists between three groups, F ( df 1 = 2, df 2 = 57) = 183.38, p < .001.…”

Section: Resultsmentioning

confidence: 99%

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Liu

Kong

Zhou

et al. 2019

Transactions in GIS

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Social connections between villages can represent farmers'interests and thus benefit participatory rural relocation planning. With rural development, however, these connections will change and may weaken the adaptation of relocation plans to future rural systems. As yet, most studies still use empirical social connections to guide relocation planning, while a few have incorporated predicted connections in the context of rural development into relocation decisions. Meanwhile, spatial optimization approaches have seldom been adopted to solve this geographical decision issue. Accordingly, our study proposes a novel spatial relocation framework that incorporates changed inter-village social connections under future rural development scenarios. Empirical inter-village connections and their policy-induced changes in central China were explored using social network analysis. An integration of particle swarm optimization and geographic information systems was adopted to identify the relocation solutions with maximum inter-village connections and maximum spatial land use compactness, and to examine how connection changes under different policy scenarios influenced relocation outcomes. The results demonstrate the significance of incorporating policy-induced social connections into relocation plans, and most importantly, show the negative relations between changed social connections | 689 LIU et aL.

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Section: Resultsmentioning

confidence: 99%

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Liu

Kong

Zhou

et al. 2019

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“…As the results of any optimization depends a lot on the selected response variables, future studies should test the robustness of our findings considering additional target indicators. The optimization algorithm NSGA‐II is most often used for solving spatial allocation problems, but is unfortunately restricted to a maximum of four objectives (Malczewski & Rinner, 2015). However, several other optimization algorithms exist that also allow handling of more than four objectives (Deb & Jain, 2014).…”

Section: Discussionmentioning

confidence: 99%

Riparian reforestation on the landscape scale: Navigating trade‐offs among agricultural production, ecosystem functioning and biodiversity

Witing

Forio

Burdon

et al. 2022

Journal of Applied Ecology

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Stream–riparian networks are subject to multiple human pressures that threaten key functions of aquatic and terrestrial ecosystems, drive habitat and diversity losses, affect riparian connectivity and cause stakeholder conflicts. Designing riparian landscapes in a way that they can simultaneously meet multiple competing demands requires a clear understanding of existing trade‐offs, and a landscape‐scale perspective on the planning of reforestation measures. This study applied a landscape optimization algorithm for allocating riparian forest management measures in the intensively used agricultural catchment of the Zwalm River (Belgium). We optimized forest allocation to improve stream ecological quality (EPT index), functional diversity (diatoms) and riparian carbon processing (cotton‐strip assay), while minimizing losses in agricultural production potential. Regression models were developed to predict the target indicators for 489 segments of the Zwalm riparian corridor, using spatial variables on three different scales. For each riparian segment, we developed spatially explicit management measures, representing different intensities of riparian reforestation. The allocation and combination of these measures in the riparian corridor were optimized to identify (a) trade‐offs among the target indicators, (b) priority regions for reforestation actions and (c) the required reforestation intensity. The results showed that all target indicators were affected by the area share of riparian forests and its landscape‐scale configuration. Reforestation of the Zwalm riparian corridor could significantly improve indicators for biodiversity and ecosystem functioning (e.g. up to +96% for EPT index), but would lead to a strong trade‐off with agricultural production. By optimizing the placement of management measures, we showed how these trade‐offs could be best balanced. The headwater regions of the Zwalm were identified as priority regions for reforestation actions. Facilitating connectivity among and further expansion of existing forest patches in the Zwalm headwaters showed to improve ecosystems with minimized trade‐offs. Synthesis and applications. This study demonstrates, for the first time, the potential of landscape optimization algorithms to support the management and design of multifunctional stream–riparian networks. We identified riparian reforestation solutions that minimized trade‐offs between specific natural values and societal needs. Our spatially explicit approach allows for an integration into spatial planning and can inform policy design and implementation.

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“…A corridor location study that utilises a genetic algorithm with raster surfaces was able to outperform traditional shortest‐path methods in terms of both computation time and solution quality (Zhang & Armstrong, 2008). Fundamentally, the algorithm follows the biological principle of natural selection (Malczewski & Rinner, 2015). It begins with a population of individuals (i.e.…”

Section: Introductionmentioning

confidence: 99%

Bridging the gap: Optimising connectivity solutions for an arboreal gliding mammal

Lee

Mendes

Liang³

et al. 2023

Journal of Applied Ecology

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1. Connectivity modelling tools are important for developing mitigation strategies to alleviate negative impacts on animal movement caused by road networks.Arboreal gliding mammals are especially vulnerable to road widening as they are unable to cross gaps beyond their gliding capacity. However, there are limitations of conventional raster-based modelling techniques when applied on this group.2. We developed and applied a new model that quantifies the changes in connectivity for an arboreal gliding species, the Sunda colugo, in Singapore using information on species-specific glide performance through a vector-based approach. We also incorporated a genetic algorithm to determine optimal locations for sets of glide poles that could be installed to improve connectivity.3. Expectedly, connectivity was heavily impacted by the road widening works, with a 93.3% decrease in the total number of feasible glide paths connecting roadside trees.4. Nine glide poles installed during the construction phase of the project were initially uninformed by the connectivity model and they provided 26 additional connections only. Comparatively, the top nine pole locations identified through the genetic algorithm provided 247 additional connections, almost 10 times more than the amount supplied by the initial nine glide pole locations determined through qualitative methods.5. The multi-objective capacity of the genetic algorithm also reduced large connectivity gaps post-development, with simulated glide poles increasing the percentage of pixels covered with connectivity from 16.1% to 27.7%.6. Synthesis and applications. Our model fills a knowledge gap in connectivity modelling for arboreal gliding mammals whose movements are affected by varying habitat alterations. We demonstrated that locations of mitigation structures greatly influence the success of mitigation efforts and the locations can be optimised by incorporating the glide performance of the species into the genetic algorithm.

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Heuristic Methods

Cited by 6 publications

References 110 publications

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Riparian reforestation on the landscape scale: Navigating trade‐offs among agricultural production, ecosystem functioning and biodiversity

Bridging the gap: Optimising connectivity solutions for an arboreal gliding mammal

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