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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Optimization Problems01:26

Optimization Problems

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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

333
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Video

Updated: Jan 17, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions.

YiFan Chen1,2, Weng Howe Chan2,3, Eileen Lee Ming Su4

  • 1Jiaxing Key Laboratory of Industrial Intelligence and Digital Twin, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang, China.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

This review analyzes multi-objective optimization (MOO) techniques for smart cities, finding hybrid AI and evolutionary methods offer superior adaptability. Challenges remain in generalizability, uncertainty handling, and interpretability for urban planning.

Keywords:
Bio-inspired algorithmsComputational efficiencyCross-domain urban planningMachine learning-enhanced optimizationMulti-objective optimizationSmart citiesSustainability trade-offsSustainable urban developmentSystematic literature reviewUrban optimization

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Last Updated: Jan 17, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

Area of Science:

  • Urban Systems Engineering
  • Computational Optimization
  • Artificial Intelligence

Background:

  • Smart cities increasingly rely on complex, interdependent systems for planning and decision-making.
  • Multi-objective optimization (MOO) is crucial for enhancing smart city sustainability and real-time operations.

Purpose of the Study:

  • To systematically review and classify MOO techniques applied in smart-city contexts from 2015-2025.
  • To benchmark algorithm performance across diverse urban domains.
  • To identify research gaps and propose a roadmap for future MOO frameworks.

Main Methods:

  • Systematic literature review of 117 peer-reviewed studies.
  • Classification of MOO algorithms into four families: bio-inspired, mathematical, physics-inspired, and ML-enhanced.
  • Benchmarking based on efficiency, scalability, and suitability for six urban domains (infrastructure, energy, transport, IoT, agriculture, water).

Main Results:

  • Established MOO algorithms like NSGA-II and MOED/D are prevalent.
  • Hybrid frameworks combining deep learning and evolutionary search show enhanced adaptability in dynamic, high-dimensional smart-city environments.
  • Key challenges identified include limited cross-domain generalizability, poor uncertainty handling, and low interpretability of AI-assisted models.

Conclusions:

  • Hybrid MOO approaches offer significant potential for smart-city applications.
  • Addressing research gaps in privacy, trade-off resolution, digital twin integration, LLMs, and neuromorphic computing is essential.
  • A benchmarking toolkit and algorithm-selection matrix are provided to guide practical implementation and future research in scalable, interpretable, and resilient urban optimization.