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Updated: Mar 9, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential

Jayesh M Dhodiya1, Anita Ravi Tailor1

  • 1Department of Applied Mathematics and Humanities, S. V. National Institute of Technology, Surat, 395007 India.

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|December 21, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid genetic algorithm to solve fuzzy multi-objective assignment problems (FMOAP). The approach effectively optimizes various scenarios for decision-makers, providing efficient solutions.

Keywords:
Fuzzy membership functionMulti-objective assignment problemTriangular fuzzy numberα-Level sets

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Area of Science:

  • Operations Research
  • Computational Intelligence
  • Decision Sciences

Background:

  • Assignment problems are common in resource allocation.
  • Fuzzy logic and multi-objective optimization are crucial for complex decision-making.
  • Existing methods may not efficiently handle uncertainty and multiple objectives simultaneously.

Purpose of the Study:

  • To develop a hybrid genetic algorithm for fuzzy multi-objective assignment problems (FMOAP).
  • To incorporate an exponential membership function and triangular possibility distribution for objective coefficients.
  • To enable decision-makers to optimize optimistic, most likely, and pessimistic scenarios using alpha-level sets.

Main Methods:

  • A hybrid approach combining a genetic algorithm with fuzzy set theory.
  • Utilizing an exponential membership function for fuzzy objectives.
  • Employing alpha-level sets to manage fuzzy judgment and optimize multiple scenarios.

Main Results:

  • The proposed hybrid approach demonstrates efficient and effective management of FMOAP.
  • A numerical example from a realistic situation validates the method's performance.
  • The approach provides effective outputs for decision-making.

Conclusions:

  • The developed hybrid algorithm is a robust tool for solving FMOAP.
  • The method successfully handles uncertainty and multiple objectives in assignment problems.
  • It empowers decision-makers with comprehensive optimization capabilities for diverse scenarios.