Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin

The Spatial Agent-based Competition Model (SpAbCoM)

The paper presents a detailed documentation of the underlying concepts and methods of the Spatial Agent-based Competition Model (SpAbCoM). For instance, SpAbCoM is used to study firms' choices of spatial pricing policy (GRAUBNER et al., 2011a) or pricing and location under a framework of multi-firm spatial competition and two-dimensional markets (GRAUBNER et al., 2011b). While the simulation model is briefly introduced by means of relevant examples within the corresponding papers, the present paper serves two objectives. First, it presents a detailed discussion of the computational concepts that are used, particularly with respect to genetic algorithms (GAs). Second, it documents SpAbCoM and provides an overview of the structure of the simulation model and its dynamics. -- Das vorliegende Papier dokumentiert die zugrundeliegenden Konzepte und Methoden des Räumlichen Agenten-basierten Wettbewerbsmodells (Spatial Agent-based Competition Model) SpAbCoM. Anwendungsbeispiele dieses Simulationsmodells untersuchen die Entscheidung bezüglich der räumlichen Preisstrategie von Unternehmen (GRAUBNER et al., 2011a) oder Preissetzung und Standortwahl im Rahmen eines räumlichen Wettbewerbsmodells, welches mehr als einen Wettbewerber und zweidimensionalen Marktgebiete berücksichtigt. Während das Simulationsmodell in den jeweiligen Arbeiten kurz anhand eines Beispiels eingeführt wird, dient das vorliegende Papier zwei Zielen. Zum Einen sollen die verwendeten computergestützten Konzepte, hier speziell Genetische Algorithmen (GA), detailliert vorgestellt werden. Zum Anderen besteht die Absicht dieser Dokumentation darin, einen Überblick über die Struktur von SpAbCoM und die während einer Simulation ablaufenden Prozesse zu gegeben.Agent-based modelling,genetic algorithms,spatial pricing,location model.,Agent-basierte Modellierung,Genetische Algorithmen,räumliche Preissetzung,Standortmodell.

A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs

Predicting the cheapest sample size for the optimal stratification in multivariate survey design is a problem in cases where the population frame is large. A solution exists that iteratively searches for the minimum sample size necessary to meet accuracy constraints in partitions of atomic strata created by the Cartesian product of auxiliary variables into larger strata. The optimal stratification can be found by testing all possible partitions. However the number of possible partitions grows exponentially with the number of initial strata. There are alternative ways of modelling this problem, one of the most natural is using Genetic Algorithms (GA). These evolutionary algorithms use recombination, mutation and selection to search for optimal solutions. They often converge on optimal or near-optimal solution more quickly than exact methods. We propose a new GA approach to this problem using grouping genetic operators instead of traditional operators. The results show a significant improvement in solution quality for similar computational effort, corresponding to large monetary savings.Comment: 22 page

Genetic Land - Modeling land use change using evolutionary algorithms

Future land use configurations provide valuable knowledge for policy makers and economic agents, especially under expected environmental changes such as decreasing rainfall or increasing temperatures, or scenarios of policy guidance such as carbon sequestration enforcement. In this paper, modelling land use change is designed as an optimization problem in which landscapes (land uses) are generated through the use of genetic algorithms (GA), according to an objective function (e.g. minimization of soil erosion, or maximization of carbon sequestration), and a set of local restrictions (e.g. soil depth, water availability, or landscape structure). GAs are search and optimization procedures based on the mechanics of natural selection and genetics. The GA starts with a population of random individuals, each corresponding to a particular candidate solution to the problem. The best solutions are propagated; they are mated with each other and originate “offspring solutions” which randomly combine the characteristics of each “parent”. The repeated application of these operations leads to a dynamic system that emulates the evolutionary mechanisms that occur in nature. The fittest individuals survive and propagate their traits to future generations, while unfit individuals have a tendency to die and become extinct (Goldberg, 1989). Applications of GA to land use planning have been experimented (Brookes, 2001, Ducheyne et al, 2001). However, long-term planning with a time-span component has not yet been addressed. GeneticLand, the GA for land use generation, works on a region represented by a bi-dimensional array of cells. For each cell, there is a number of possible land uses (U1, U2, ..., Un). The task of the GA is to search for an optimal assignment of these land uses to the cells, evolving the landscape patterns that are most suitable for satisfying the objective function, for a certain time period (e.g. 50 years in the future). GeneticLand develops under a multi-objective function: (i) Minimization of soil erosion – each solution is validated by applying the USLE, with the best solution being the one that minimizes the landscape soil erosion value; (ii) Maximization of carbon sequestration – each solution is validated by applying atmospheric CO2 carbon uptake estimates, with the best solution being the one that maximizes the landscape carbon uptake; and (iii) Maximization of the landscape economic value – each solution is validated by applying an economic value (derived from expert judgment), with the best solution being the one that maximizes the landscape economic value. As an optimization problem, not all possible land use assignments are feasible. GeneticLand considers two sets of restrictions that must be met: (i) physical constraints (soil type suitability, slope, rainfall-evapotranspiration ratio, and a soil wetness index) and (ii) landscape ecology restrictions at several levels (minimum patch area, land use adjacency index and landscape contagion index). The former assures physical feasibility and the latter the spatial coherence of the landscape. The physical and landscape restrictions were derived from the analysis of past events based on a time series of Landsat images (1985-2003), in order to identify the drivers of land use change and structure. Since the problem has multiple objectives, the GA integrates multi-objective extensions allowing it to evolve a set of non-dominated solutions. An evolutive type algorithm – Evolutive strategy (1+1) – is used, due to the need to accommodate the very large solution space. Current applications have about 1000 decision variables, while the problem analysed by GeneticLand has almost 111000, generated by a landscape with 333*333 discrete pixels. GeneticLand is developed and validated for a Mediterranean type landscape located in southern Portugal. Future climate triggers, such as the increase of intense rainfall episodes, is accommodated to simulate climate change . This paper presents: (1) the formulation of land use modelling as an optimization problem; (2) the formulation of the GA for the explicit spatial domain, (3) the land use constraints derived for a Mediterranean landscape, (4) the results illustrating conflicting objectives, and (5) limitations encountered.

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California