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Dynamic path optimization is an important part of intelligent transportation systems (ITSs). Aiming at the shortcomings of the current dynamic path optimization method, the improved ant colony algorithm was used to optimize the dynamic path. Through the actual investigation and analysis, the influencing factors of the multiobjective planning model were determined. The ant colony algorithm was improved by using the analytic hierarchy process (AHP) to transform path length, travel time, and traffic flow into the comprehensive weight-influencing factor. Meanwhile, directional guidance and dynamic optimization were introduced to the improved ant colony algorithm. In the simulated road network, the length of the optimal path obtained by the improved ant colony algorithm in the simulation road network is 3.015, which is longer than the length of the optimal path obtained by the basic ant colony algorithm (2.902). The travel time of the optimal path obtained by the improved ant colony algorithm (376 s) is significantly shorter than that of the basic ant colony algorithm (416.3 s). The number of iterations of the improved ant colony algorithm (45) is less than that of the basic ant colony algorithm (58). In the instance network, the number of iterations of the improved ant colony algorithm (18) is less than that of the basic ant colony algorithm (26). The travel time of the optimal path obtained by the improved ant colony algorithm (377.1 s) is significantly shorter than that of the basic ant colony algorithm (426 s) and the spatial shortest distance algorithm (424 s). Compared with the basic ant colony algorithm and the spatial shortest distance algorithm, the results of the optimal path obtained by the improved ant colony algorithm were more accurate, and the effectiveness of the improved ant colony algorithm was verified.

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to a “numerical” population of solutions to optimize a fitness function. GA implementations must find a compromise between the breath of the search (to avoid being trapped into local minima) and its depth (to prevent a rough approximation of the optimal solution). Most algorithms use “elitism”, which allows preserving some of the current best solutions in the successive generations. If the initial population is randomly selected, as in many GA packages, the elite may concentrate in a limited part of the Pareto frontier preventing its complete spanning. A full view of the frontier is possible if one, first, solves the single-objective problems that correspond to the extremes of the Pareto boundary, and then uses such solutions as elite members of the initial population. The paper compares this approach with more conventional initializations by using some classical tests with a variable number of objectives and known analytical solutions. Then we show the results of the proposed algorithm in the optimization of a real-world system, contrasting its performances with those of standard packages.

Flood disasters are a hot topic in the field of disaster prevention and control. To reduce the high‐density economic losses caused by floods, it is necessary to effectively and reasonably distribute emergency supplies to disaster sites during the emergency cycle. This article describes setting up a comprehensive multirescue site, multidisaster site, and multiobjective programming model to measure the total amount of time taken to transport supplies and economic losses endured in the response and recovery. Moreover, we use the evolutionary algorithm based on the Pareto concept and simulate the calculation using computer simulation. Finally, a case study is carried out based on various supply data of the historical flood disaster and the flood control supply reserves in the Jiangsu Province. The optimization results are discussed using the sorting method that approaches the ideal solution, and three feasible emergency plans are given, which can provide a reference for emergency supply transportation for urban flood control.

In order to improve the effect of rural tourism development, this study combines multiobjective planning and intelligent optimization algorithms to analyze the development mode of rural tourism and further deals with the problem of multisensor target tracking under unknown input interference conditions by designing a tourism network consensus algorithm. The algorithm adopts a distributed multisensor fusion structure combined with consensus estimation, and each sensor first performs two-level information filtering estimation and unknown input parameter estimation locally. The experimental research results show that the rural tourism development model based on multiobjective planning and intelligent optimization algorithm proposed in this study can play an important role in the development of rural tourism.

Growing energy demand has increased the need to manage conflicts between energy production and the environment. As an example, shale-gas extraction requires substantial surface infrastructure, which fragments habitats, erodes soils, degrades freshwater systems, and displaces rare species. Strategic planning of shale-gas infrastructure can reduce trade-offs between economic and environmental objectives, but the specific nature of these trade-offs is not known. We estimated the cost of avoiding impacts from land-use change on forests, wetlands, rare species, and streams from shale-energy development within leaseholds. We created software for optimally siting shale-gas surface infrastructure to minimize its environmental impacts at reasonable construction cost. We visually assessed sites before infrastructure optimization to test whether such inspection could be used to predict whether impacts could be avoided at the site. On average, up to 38% of aggregate environmental impacts of infrastructure could be avoided for 20% greater development costs by spatially optimizing infrastructure. However, we found trade-offs between environmental impacts and costs among sites. In visual inspections, we often distinguished between sites that could be developed to avoid impacts at relatively low cost (29%) and those that could not (20%). Reductions in a metric of aggregate environmental impact could be largely attributed to potential displacement of rare species, sedimentation, and forest fragmentation. Planners and regulators can estimate and use heterogeneous trade-offs among development sites to create industry-wide improvements in environmental performance and do so at reasonable costs by, for example, leveraging low-cost avoidance of impacts at some sites to offset others. This could require substantial effort, but the results and software we provide can facilitate the process. La creciente demanda de energía ha incrementado la necesidad de manejar los conflictos entre la producción de energía y el ambiente. Como ejemplo, la extracción de gas esquisto requiere de una infraestructura superficial sustancial, la cual fragmenta los habitats, erosiona el suelo, degrada los sistemas de agua dulce y desplaza a las especies raras. La planeación estratégica de la infraestructura de gas esquisto puede reducir las compensaciones entre los objetivos económicos y ambientales, pero la naturaleza específica de estas compensaciones no se conoce. Estimamos el costo de evitar los impactos del cambio de uso de suelo causado por el desarrollo de gas esquisto dentro de los arriendos sobre los bosques, humedales, especies raras y arroyos Creamos un software para sitiar óptimamente la infraestructura superficial de gas esquisto y minimizar su impacto ambiental a un costo de construcción razonable. Valoramos visualmente los sitios antes de la optimización de la infraestructura para probar si dicha inspección podría usarse para predecir si los impactos podrían evitarse en el sitio. En promedio, hasta el 38 % de los impactos ambientales agregados de la infraestructura podría evitarse por 20 % de costos de desarrollo mayores al optimizar espacialmente la infraestructura. Sin embargo, encontramos compensaciones entre los impactos ambientales y los costos entre los sitios. En las inspecciones visuales muchas veces distinguimos entre los sitios que podrían desarrollarse para evitar los impactos a un costo relativamente bajo (29 %) y aquellos que no podrían (20 %). Las reducciones en una medida de impacto ambiental agregado podrían atribuirse en su mayoría al desplazamiento potencial de las especies raras, la sedimentación y la fragmentación del bosque. Los planificadores y los reguladores pueden estimar y usar compensaciones heterogéneas entre los sitios de desarrollo para crear mejoras en el desempeño ambiental a lo largo de la industria y hacerlo a costos razonables al, por ejemplo, evitar los impactos en algunos sitios para compensar otros. Esto podría requerir un esfuerzo sustancial, pero los resultados y el software que proporcionamos pueden facilitar el proceso.

In order to improve the spatial layout ability of ecological buildings under the background of rural revitalization, a multiobjective planning and decision-making model for the spatial layout of ecological buildings is constructed. Based on the visual impact detection of ecological building space, a three-dimensional rendering model is established. The block matrix matching and boundary contour parameter analysis methods are used to plan and design the layout boundary feature points, and the wavelet scale decomposition method is used to analyze the mixed tone of the layout image. Based on this, a multiobjective planning decision-making model for the spatial layout of ecological buildings is established, based on which the spatial layout design scheme of ecological buildings is output to realize the spatial layout planning of ecological buildings. The simulation results show that the spatial layout of ecological buildings using this method is more reasonable, and the expression ability of ecological aesthetics is stronger, which has a good application value in rural planning and design.

We present two models of perpetrators' decision-making in extracting resources from a protected area. It is assumed that the authorities conduct surveillance to counter the extraction activities, and that perpetrators choose their post-extraction paths to balance the time/hardship of travel against the expected losses from a possible detection. In our first model, the authorities are assumed to use ground patrols and the protected resources are confiscated as soon as the extractor is observed with them. The perpetrators' path-planning is modeled using the optimal control of randomly terminated process. In our second model, the authorities use aerial patrols, with the apprehension of perpetrators and confiscation of resources delayed until their exit from the protected area. In this case the path-planning is based on multiobjective dynamic programming. Our efficient numerical methods are illustrated through several examples with complicated geometry and terrain of protected areas, nonuniform distribution of protected resources, and spatially nonuniform detection rates due to aerial or ground patrols.

The Nature Conservancy (TNC) was founded by ecologists as a United States land trust to purchase parcels of habitat for the purpose of scientific study. It has evolved into a global organization working in 35 countries ‘to conserve the lands and waters on which all life depends’. TNC is now the world's largest conservation non‐governmental organization (NGO), an early adopter of advances in ecological theory and a producer of new science as a result of practising conservation. The Nature Conservancy's initial scientific innovation was the use of distributional data for rare species and ecological communities to systematically target lands for conservation. This innovation later evolved into a more rigorous approach known as ‘Conservation by Design’ that contained elements of systematic conservation planning, strategic planning and monitoring and evaluation. The next scientific transition at TNC was a move to landscape‐scale projects, motivated by ideas from landscape ecology. Because the scale at which land could be set aside in areas untouched by humans fell far short of the spatial scale demanded by conservation, TNC became involved with best management practices for forestry, grazing, agriculture, hydropower and other land uses. A third scientific innovation at TNC came with the pursuit of multiobjective planning that accounts for economic and resource needs in the same plans that seek to protect biodiversity. The Millennium Ecosystem Assessment prompted TNC to become increasingly concerned with ecosystem services and the material risk to people posed by ecosystem deterioration. Finally, because conservation depends heavily upon negotiation, TNC has recently recruited social scientists, economists and communication experts. One aspect still missing, however, is a solid scientific understanding of thresholds that should be averted. Synthesis and applications. Over its 60‐plus year history, scientific advances have informed The Nature Conservancy (TNC)'s actions and strategies, and in turn the evolving practice of conservation has altered the type of science sought by TNC in order to maximize its conservation effectiveness.

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case of swarm Unmanned Surface Vehicles (USVs). This study aims to further explore the performance of the ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in-parallel identifying the mission areas of each USV to reach the desired targets. The design of the operational areas for each USV in the swarm is performed by a comparative evaluation of three popular clustering algorithms: Mini Batch K-Means, Ward Clustering and Birch. Following the identification of the operational areas, the design of each USV path to perform the operation is performed based on the minimization of traveled distance and energy consumption, as well as the maximization of path smoothness. To solve this multiobjective path planning problem, a comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-Mamdani) and Takagi–Sugeno–Kang (ACO-TSK). The results show that depending on the needs of the application, each methodology can contribute, respectively. ACO-Mamdani generates better paths, but ACO-TSK presents higher computation efficiency.

The management of Lake Okeechobee in Florida has undergone significant changes in the last decade. Socio-political, environmental and demographic factors have driven changes in the environmental and water policy, which in turn have led to wide-ranging institutional changes and a shift toward multiobjective planning and implementation in the Lake management. This article describes the changes in the philosophy and practice of water resources management in South Florida hydrologic system, of which Lake Okeechobee is a crucial component. The impacts of the changes on management goals and decision processes are illustrated through a case study of the use of climate information in Lake management. The article concludes with a brief examination of the implications of the institutional changes, including greater public participation, for the long-term sustainability of the social-ecological system in South Florida.