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type: software
title: 'hydroMOPSO: Multi-Objective Optimisation with Focus on Environmental Models'
version: 0.2-6
doi: 10.32614/CRAN.package.hydroMOPSO
abstract: State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based
  on the algorithm developed by Lin et al. (2018) <https://doi.org/10.1109/TEVC.2016.2631279>
  with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <https://doi.org/10.1109/LA-CCI48322.2021.9769844>.
  This package is inspired by and closely follows the philosophy of the single objective
  'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) <https://doi.org/10.1016/j.envsoft.2013.01.004>),
  and can be used for global optimisation of non-smooth and non-linear R functions
  and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of
  'hydroMOPSO' is optimising environmental and other real-world models that need to
  be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the
  model to be optimised through its input and output files, without requiring modifying
  its source code. Thanks to its flexible design and the availability of several fine-tuning
  options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems
  (e.g., multi-objective functions, multiple model variables, multiple periods). Finally,
  'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate
  the computational burden of complex models with long execution time.
authors:
- family-names: Marinao-Rivas
  given-names: Rodrigo
  email: ra.marinao.rivas@gmail.com
- family-names: Zambrano-Bigiarini
  given-names: Mauricio
  email: mzb.devel@gmail.com
  orcid: https://orcid.org/0000-0002-9536-643X
repository: https://rmarinao.r-universe.dev
repository-code: https://gitlab.com/rmarinao/hydroMOPSO
commit: 1610ee6585e63c004bab6015bf1973827764698c
url: https://rmarinao.gitlab.io/hydroMOPSO
date-released: '2026-05-12'
contact:
- family-names: Marinao-Rivas
  given-names: Rodrigo
  email: ra.marinao.rivas@gmail.com