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Neutrosophic goal programming technique with bio inspired algorithms for crop land allocation problem.

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This study introduces Neutrosophic Goal Programming (NGP) with bio-inspired algorithms to optimize agricultural land distribution, maximizing profit and output under uncertainty. The new method outperforms existing techniques for better farm management.

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Area of Science:

  • Agricultural Economics
  • Operations Research
  • Computational Intelligence

Background:

  • Land distribution and crop planning are crucial in agriculture, facing uncertainties in yield, prices, and indeterminate factors.
  • Existing fuzzy and intuitionistic fuzzy optimization methods lack indeterminacy membership functions.
  • Neutrosophic optimization uniquely incorporates truth, falsity, and indeterminacy membership functions for improved decision-making.

Purpose of the Study:

  • To enhance agricultural land distribution and crop planning using Neutrosophic Goal Programming (NGP).
  • To incorporate hexagonal intuitionistic parameters and advanced membership functions (hyperbolic, exponential, linear) into NGP.
  • To optimize expenditure, production, and profit by minimizing deviations in truth, indeterminacy, and falsity.

Main Methods:

  • Developed an NGP model with hexagonal intuitionistic parameters and novel membership functions.
  • Integrated bio-inspired algorithms—Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO)—to solve the NGP achievement function.
  • Collected data from medium-sized farmers in Ariyalur District, Tamil Nadu, India.

Main Results:

  • The proposed NGP method with bio-inspired algorithms achieved superior optimal solutions compared to Zimmermann, Angelov, and Torabi techniques.
  • Demonstrated the effectiveness of incorporating truth, indeterminacy, and falsity membership functions for agricultural optimization.
  • Bio-inspired algorithms successfully navigated complex solution spaces to find global optima for the NGP achievement function.

Conclusions:

  • The novel Neutrosophic Goal Programming approach integrated with bio-inspired algorithms offers a significant advancement in optimizing agricultural land distribution.
  • This method provides a more robust framework for managing uncertainties and achieving optimal economic outcomes in farming.
  • The study highlights the potential of neutrosophic optimization and bio-inspired computing for practical applications in agricultural management and decision-making.