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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.

|August 14, 2023

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:

CPLEX GA GAMS Lingo Mixed integer non-linear programming Multi-objective optimization NSGA-II Pulp Pymoo Python Sectorization

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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.