Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
- Ioannis T. Christou: Popt4jlib: A Parallel/Distributed Optimization Library for Java (2019) arXiv
- Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization (2017) arXiv
- Ewald, Roland; Uhrmacher, Adelinde M.: SESSL: a domain-specific language for simulation experiments (2014)
- Alba, Enrique; Luque, Gabriel; Nesmachnow, Sergio: Parallel metaheuristics: recent advances and new trends (2013)
- Humeau, J.; Liefooghe, A.; Talbi, E.-G.; Verel, S.: ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms (2013)
- Sakti, Abdelilah; Guéhéneuc, Yann-Gaël; Pesant, Gilles: Constraint-based fitness function for search-based software testing (2013)
- Parejo, José Antonio; Ruiz-Cortés, Antonio; Lozano, Sebastián; Fernandez, Pablo: Metaheuristic optimization frameworks: a survey and benchmarking (2012) ioport