Java Evolutionary Multi-Objective Algorithms, this version was developed with Java 11.
- Integer
- Real (double)
- Interval
- Fuzzy Number (Trapezoidal)
- PSP
- Knapsack
- DTLZ
- ZDT
- For single-objective:
- GA: a genetic algorithm.
- GWO: Grey Wolf Optimizer
- For multi-objective optimization:
- GA: A multi-objectve evolutionary algorithm (using dominance)
- NSGA-II
- NSGA-III
- NSGA-III-P : NSGA-III with preferences incorporation, using a multi-criteria ordinal classifier
- MOGWO : Multi-Objective Grey Wolf Optimizer
- MOGWO/DE: Multi-Objective Grey Wolf Optimizer based on decomposition
- MOGWO-V : Multi-Objective Grey Wolf Optimizer with SBX crossover and Polynomial mutation
- MOGWO-P : Multi-Objective Grey Wolf Optimizer with preferences incorporation, using a multi-criteria ordinal classifier
- MOGWO-O : Multi-Objective Grey Wolf Optimizer with preferences incorporation, using a net outranking score
- iMOACO_R : Indicator-Based Multi-Objective Ant Colony Optimization Algorithm for Continuous Search Spaces
- GWO-InClass: Multi-Objective Grey Wolf Optimizer with InterClass-nC
- ACO-InClass: Indicator-Based Multi-Objective Ant Colony Optimization Algorithm for Continuous Search Spaces with InterClass-nC
- Electre Tri
- INTERCLASS-nC
- INTERCLASS-nB
- SatClassifier
To compile and run from command line the test suite it is necessary to execute the following maven goal:
$ mvn clean compile package
For run
$ java -jar jemoa-1.0.0-jar-with-dependencies.jar -a NSGAIII -m 3
It is required to have a directory -relative to the execution directory- with the instances in the following form:
DTLZ_INSTANCES/numberOfObjectives/DTLZ$N$_Instance.txt
Where N is the number of DTLZ problem. For more details use the --help
There are examples of how to execute each algorithm in the EXAMPLE package, it is only necessary to execute the class of interest, this from an IDE.
E.G.
Grey Wolf Optimizer example.
- Castellanos A, Cruz-Reyes L, Fernández E, Rivera G, Gomez-Santillan C, Rangel-Valdez N. Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation. Mathematics. 2022; 10(3):322. doi.org/10.3390/math10030322
- Castellanos-Alvarez A, Cruz-Reyes L, Fernandez E, Rangel-Valdez N, Gómez-Santillán C, Fraire H, Brambila-Hernández JA. A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification Mathematical and Computational Applications. 2021; 26(2):27. doi.org/10.3390/mca26020027