Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
A Note on the Griewank Test Function
Journal of Global Optimization
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
An open software environment for hydrological model assessment and development
Environmental Modelling & Software
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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This work presents and illustrates the application of hydroPSO, a novel multi-OS and model-independent R package used for model calibration. hydroPSO allows the modeller to perform a standard modelling work flow including, sensitivity analysis, parameter calibration, and assessment of the calibration results, using a single piece of software. hydroPSO implements several state-of-the-art enhancements and fine-tuning options to the Particle Swarm Optimisation (PSO) algorithm to meet specific user needs. hydroPSO easily interfaces the calibration engine to different model codes through simple ASCII files and/or R wrapper functions for exchanging information on the calibration parameters. Then, optimises a user-defined goodness-of-fit measure until a maximum number of iterations or a convergence criterion are met. Finally, advanced plotting functionalities facilitate the interpretation and assessment of the calibration results. The current hydroPSO version allows easy parallelization and works with single-objective functions, with multi-objective functionalities being the subject of ongoing development. We compare hydroPSO against standard algorithms (SCE_UA, DE, DREAM, SPSO-2011, and GML) using a series of benchmark functions. We further illustrate the application of hydroPSO in two real-world case studies: we calibrate, first, a hydrological model for the Ega River Basin (Spain) and, second, a groundwater flow model for the Pampa del Tamarugal Aquifer (Chile). Results from the comparison exercise indicate that hydroPSO is: i) effective and efficient compared to commonly used optimisation algorithms, ii) ''scalable'', i.e. maintains a high performance for increased problem dimensionality, and iii) versatile to adapt to different response surfaces of the objective function. Case study results highlight the functionality and ease of use of hydroPSO to handle several issues that are commonly faced by the modelling community such as: working on different operating systems, single or batch model execution, transient- or steady-state modelling conditions, and the use of alternative goodness-of-fit measures to drive parameter optimisation. Although we limit the application of hydroPSO to hydrological models, flexibility of the package suggests it can be implemented in a wider range of models requiring some form of parameter optimisation.