cuHjDE 2D-AB: A GPU-Based Hybrid jDE Algorithm Applied to the 2D-AB Protein Structure Prediction. Dev: Mateus Boiani.
BOIANI, M. ; DOMINICO, G. ; PARPINELLI, R. S. . A GPU-based Hybrid jDE Algorithm Applied to the 2D-AB Protein Structure Prediction Problem. In: 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019, Salvador-BH, 2019. v. 1. p. 317-322.
cujDE: A GPU-based jDE algorithm for unconstrained continuous optimization. Dev: Mateus Boiani.
BOIANI, M. ; DOMINICO, G. ; PARPINELLI, R. S. . "A GPU-based jDE Algorithm Applied to Continuous Unconstrained Optimization". In: International Conference on Intelligent Systems Design and Applications, 2018.
jDE: A sequential implementation of a self-adaptive differential evolution namely jDE in C++. Dev: Mateus Boiani.
SHADE: Success-History based parameter Adaptation for Differential Evolution proposed by Ryoji Tanabe and Alex Fukunaga (2013). Dev: Mateus Boiani.
EB-A-SHADE: A-SHADE with mutation strategies from EB-L-SHADE. Other variations can be found in this repository. EB-A-SHADE obtained best results using the CEC-2013 benchmark set (28 functions). Dev: Christopher Renkavieski.
RNGs: Several methods for random number generation (RNG) are available. The are: Uniform distribution, Gaussian distribution, Cauchy distribution, Logistic map, Circle map, Gauss map, Piecewise map, Sine map, Singer map, Sinusoidal map, Tent map, Chebyshev map, Iteractive map. Dev: Luiza Engler Stadelhofer.
PRVNS: Population-based Reduced Variable Neighbourhood Search for unconstrained continuous optimization. Some pattern search algorithms are also available, such as the Nelder-Mead and the Hooke-Jeeves. Dev: Wesklei Migliorini.
MIGLIORINI, W., PARPINELLI, R.S.. "Population-based variable neighbourhood search algorithm applied to unconstrained continuous optimisation". INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION (ONLINE), v. 11, p. 73-80, 2018.
ECO Framework: Ecologically-inspired framework for continuous optimization. Dev: Rafael S. Parpinelli.
PARPINELLI, R.S.; LOPES, H.S. "Biological Plausibility in Optimization: An Ecosystemic View". International Journal of Bio-Inspired Computation (Online), v. 4, p. 345-358, 2012.
PARPINELLI, R.S.; LOPES, H.S. "A computational ecosystem for optimization: review and perspectives for future research", Memetic ComputingVolume 7, Issue 1, 29-41, 2014.
Python
DSEA-Quota-PSPP: A Dynamic Speciation Evolutionary Algorithm with Fragment Insertion and Information-Based Adaptive Selection Strategy for Protein Structure Prediction. Dev: Nilcimar Neitzel Will.
WILL, N. N.; PARPINELLI, R. S. Comparing Best and Quota Fragment Picker Protocols Applied to Protein Structure Prediction. In: 12th World Congress on Nature and Biologically Inspired Computing (NaBIC 2020), 2020, Online.
NCjDE-2LSar: the cluster-based external archive and local search jDE applied to continuous multimodal optimization with multiple peaks. Dev: Gabriel Dominico.
DOMINICO, G.; PARPINELLI, R. S.. Multiple global optima location using differential evolution, clustering, and local search. Applied Soft Computing,
2021, 107448, ISSN 1568-4946
NCjDE-HJar: the cluster-based external archive maintenance strategy with the jDE algorithm applied to continuous multimodal optimization with multiple peaks. The DBSCAN algorithm, the Hooke-Jeeves local search algorithm and the Michalewicz mutation strategy are employed. Dev: Gabriel Dominico.
DOMINICO, G.; BOIANI, M.; PARPINELLI, R. S.. Differential Evolution with Cluster-based External Archive and Local Search for Multimodal Optimization. In: 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador-BH, 2019. v. 1. p. 335-340
OptAlgs: This repository contains some population-based optimization algorithms in Python, namely PSO, DE, SaDE, and CODE. Dev: Guilherme Plichoski.
Time Series Prediction: Three algorithms for time series prediction are available, namely: Recurrent Neural Network, ARIMA, and Support Vector Regression. Dev: Samuel Oliveira.
OLIVEIRA, S., KNIESS, J., PARPINELLI, R.S., CASTAÑEDA, W. “Predição de Séries Temporais em Internet das Coisas com Redes Neurais Recorrentes”, In: 50o Simpósio Brasileiro de Pesquisa Operacional (SBPO), 2018.
Java
MMAS-LS-ADTSPMV: Analysis of Max-Min Ant System with Local Search Applied to the Asymmetric and Dynamic Travelling Salesman Problem with Moving Vehicle. Dev: João Pedro Schmitt.
SCHMITT, P. J., BALDO, F., PARPINELLI, R.S. "Analysis of Max-Min Ant System with Local Search Applied to the Asymmetric and Dynamic Travelling Salesman Problem with Moving Vehicle". Special Event on Analysis of Experimental Algorithms (SEA^2), Kalamata, Greece, 2019.
CPS Problem: contains the implementation of all selection methods developed to approach the Cloud Provider Selection Problem. Deterministic methods such as DEA and matching, and metaheuristic methods such as GA, DDE, BDE, SA and stochastic 3-opt. Dev: Lucas Borges de Moraes.
MORAES, L. B. ; FIORESE, A. ; PARPINELLI, R. S. . An Improved Evolutionary Hybrid Method for Cloud Provider Selection. In: 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador-BH, 2019. v. 1. p. 24-29.
MORAES, L. B.; FIORESE, A.; PARPINELLI, R.S.. "An Evolutive Hybrid Approach to Cloud Computing Provider Selection". In: IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, 2018. p. 1564-1571.
MMAS-MEM: A MAX-MIN Ant System with Short-term Memory Applied to the Dynamic and Asymmetric Traveling Salesman Problem. Dev: João Pedro Schmitt.
SCHMITT, P. J., BALDO, F., PARPINELLI, R.S. "A MAX-MIN Ant System with Short-Term Memory Applied to the Dynamic and Asymmetric Traveling Salesman Problem". 7th Brazilian Conference on Intelligent Systems (BRACIS), 2018.
GUI Ant-Miner: GUI Ant-Miner is a tool for extracting classification rules from data. It is an updated version of a data mining algorithm called Ant-Miner. Dev: Fernando Meyer.
PARPINELLI, R.S., LOPES, H.S. and FREITAS, A.A. "Data Mining with an Ant Colony Optimization Algorithm". In: IEEE Transactions on Evolutionary Computation, Volume 6, Issue 4, pp. 321–332. IEEE, 2002.