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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Dataset on a Benchmark for Equality Constrained Multi-objective Optimization.

Oliver Cuate1, Lourdes Uribe2, Adriana Lara2

  • 1Department of Computer Science, CINVESTAV-IPN, Mexico City, Mexico.

Data in Brief
|February 6, 2020
PubMed
Summary
This summary is machine-generated.

This study provides source code for equality constrained multi-objective optimization problems and several multi-objective evolutionary algorithms. These resources facilitate testing and comparison of optimization algorithms on benchmark functions.

Keywords:
BenchmarkingEquality constraintsEvolutionary computationMulti-objective optimization

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Area of Science:

  • * Computational Science and Engineering
  • * Optimization and Operations Research

Background:

  • * Multi-objective optimization problems (MOPs) are prevalent in various scientific and engineering disciplines.
  • * Equality constrained MOPs (ECMOPs) present unique challenges in algorithm development and testing.
  • * Existing benchmarks and algorithm implementations are crucial for advancing research in this field.

Purpose of the Study:

  • * To release the source code for novel equality constrained multi-objective optimization benchmark problems: EqDTLZ 1-4 and EqIDTLZ 1-2.
  • * To provide implementations of several state-of-the-art multi-objective evolutionary algorithms (MOEAs).
  • * To enable reproducible research and facilitate the comparative analysis of MOEAs on ECMOPs.

Main Methods:

  • * Development of benchmark functions EqDTLZ 1-4 and EqIDTLZ 1-2 in Matlab.
  • * Implementation of MOEAs including NSGA-II, NSGA-III, aNSGA-III, GDE3, MOEA/D/D, and PPS.
  • * Utilization of the PlatEMO v2.0 framework for algorithm and benchmark integration.

Main Results:

  • * Availability of comprehensive source code for ECMOP benchmarks and selected MOEAs.
  • * Numerical approximations of the algorithms' performance on the provided test functions.
  • * A unified platform for testing and comparing diverse multi-objective optimization strategies.

Conclusions:

  • * The released code provides a valuable resource for the multi-objective optimization community.
  • * Facilitates standardized testing and benchmarking of evolutionary algorithms for ECMOPs.
  • * Encourages further development and application of optimization techniques in complex problem domains.