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Updated: Jul 19, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A comparison among optimization software to solve bi-objective sectorization problem.

Aydin Teymourifar1

  • 1Universidade Católica Portuguesa, Católica Porto Business School, Centro de Estudos em Gestão e Economia Porto, Portugal.

Heliyon
|August 14, 2023
PubMed
Summary

This study evaluates optimization software for the bi-objective sectorization problem. GAMS performed best among solvers, while Pymoo metaheuristics provided feasible solutions efficiently.

Keywords:
CPLEXGAGAMSLingoMixed integer non-linear programmingMulti-objective optimizationNSGA-IIPulpPymooPythonSectorization

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Area of Science:

  • Operations Research
  • Computer Science

Background:

  • Sectorization problems are complex, often involving multiple objectives.
  • Existing optimization methods face challenges with non-linear, bi-objective models.

Purpose of the Study:

  • To compare the performance of various optimization software and metaheuristics for the bi-objective sectorization problem.
  • To introduce a novel approach transforming bi-objective models into single-objective ones using ideal and anti-ideal points.

Main Methods:

  • A novel method transforming bi-objective sectorization models into single-objective ones.
  • Implementation and comparison of optimization software: Pulp, Lingo, CPLEX, and GAMS.
  • Application of metaheuristics from the Pymoo library for single- and bi-objective versions.

Main Results:

  • Commercial solvers showed unsatisfactory performance, with GAMS yielding the best results.
  • Pymoo metaheuristics effectively found feasible solutions within reasonable timeframes.
  • Analysis of model linearization and its impact on variables and constraints.

Conclusions:

  • GAMS is recommended for solving sectorization problems among the tested solvers.
  • Metaheuristics offer a viable alternative for obtaining practical solutions efficiently.
  • Further research should explore enhanced methods for similar complex optimization tasks.