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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A local cost simulation-based algorithm to solve distributed constraint optimization problems.

Meifeng Shi1,2, Feipeng Liang1, Yuan Chen1

  • 1Department of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Local Cost Simulation (LCS) algorithm for Distributed Constraint Optimization Problems (DCOPs). LCS enhances exploration by simulating historical agent values, outperforming existing methods.

Keywords:
DCOPsEWMAInferior solutionsLCS

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Area of Science:

  • Artificial Intelligence
  • Distributed Systems
  • Optimization

Background:

  • Local search algorithms are vital for Distributed Constraint Optimization Problems (DCOPs).
  • Existing algorithms overlook the impact of historical agent values on cost.
  • There's a need for enhanced exploration in DCOP algorithms.

Purpose of the Study:

  • To introduce a novel algorithm, Local Cost Simulation (LCS), for DCOPs.
  • To leverage historical agent values to improve local search exploration.
  • To enhance the performance of incomplete DCOP algorithms.

Main Methods:

  • Developed the Local Cost Simulation (LCS) algorithm.
  • Introduced Exponential Weighted Moving Average (EWMA) to simulate local costs and generate value selection probabilities.
  • Implemented population construction for agents to increase inferior solution selection and facilitate information exchange.

Main Results:

  • Theoretically analyzed the feasibility of EWMA and the potential for solution quality improvement.
  • Empirically demonstrated LCS's superior performance compared to state-of-the-art DCOP incomplete algorithms.
  • LCS effectively utilizes historical agent values for enhanced exploration.

Conclusions:

  • LCS offers a significant advancement in solving DCOPs.
  • The integration of historical value simulation and population-based approaches enhances algorithm exploration.
  • LCS provides a more effective and robust solution for DCOP challenges.