jMetal stands for Metaheuristic Algorithms in Java, and it is an object-oriented Java-based framework for multi-objective optimization with metaheuristics.
The object-oriented architecture of the framework and the included features allow you to: experiment with the provided classic and state-of-the-art techniques, develop your own algorithms, solve your optimization problems, integrate jMetal in other tools, etc.
jMetal 5 is the first major revision of jMetal since its initial version. The architecture have been redesigned from scratch to provide a simpler design while keeping the same functionality. The current version is jMetal 5.6.
Now jMetal is a Maven project that is hosted at GitHub, where interested people can access to the current status of the project and are free to contribute.
The former jMetal versions are still available at SourceForge.
J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.
- Cites (Google Scholar)
- A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
- Multi-objective algoritms: NSGA-II, SPEA2, PAES, PESA-II, OMOPSO, MOCell, AbYSS, MOEA/D, GDE3, IBEA, SMPSO, SMPSOhv, SMS-EMOA, MOEA/D-STM, MOEA/D-DE, MOCHC, MOMBI, MOMBI-II, NSGA-III, WASF-GA, GWASF-GA, R-NSGA-II, CDG-MOEA, ESPEA, SMSPO/RP
- Single-objective algoritms: genetic algorithm (variants: generational, steady-state), evolution strategy (variants: elitist or mu+lambda, non-elitist or mu, lambda), DE, CMA-ES, PSO (Stantard 2007, Standard 2011), Coral reef optimization.
- Algorithms that can be executed in parallel: NSGA-II, SMPSO, GDE3, SPEA2, PESA-II
- Problem families: ZDT, DTLZ, WFG, CEC2009, LZ09, GLT, MOP, CEC2018
- Classical problems: Kursawe, Fonseca, Schaffer, Viennet2, Viennet3
- Constrained problems: Srinivas, Tanaka, Osyczka2, Constr_Ex, Golinski, Water, Viennet4
- Combinatorial problems: multi-objective TSP
- Academic problems: OneMax, OneZeroMax
- Quality indicators: hypervolume, spread, generational distance, inverted generational distance, inverted generational distance plus, additive epsilon.
- Variable representations: binary, real, integer, permutation, mixed encoding (real+binary, int+real).