SwarmX

SwarmX is the java based natural inspired optimization library. This is intended for researches, software engineers, developers and students who would like to use natural inspired optimization to solve their optimization problems.

Implemented Algorithms

1. PSO (Particle Swarm Optimization)
2. ACO (Ant Colony Optimization)
3. MBO (Marriage in Honey Bees Optimization)
4. WSO (Wasp Swarm Optimization)
5. MS (Monkey Search Optimization)
6. CSO (Cat Swarm Optimization)
7. GSO (Glowworm Swarm Optimization)
8. CS (Cuckoo Search Algorithm)
9. FA (Firefly Algorithms)
10. ABC (Artificial Bee Colony)
11. BA (Bat-Inspired Algorithm)
12. TCO (Termite Colony Optimization)
13. GWO (Grey Wolf Optimization)
14. MFA (Moth-Flame Optimization)
15. ALO (Ant Lion Optimization)
16. ALSO (Artificial Lizard Search Optimization)
17. GEO (Golden Eagle Optimizer)
18. AVOA (African Vultures Optimization Algorith)
19. TSA (Tunicate Swarm Algorithm)
20. SSA (Squirrel Search Algorithm)
21. ZOA (Zebra Optimization Algorithm)
22. JSO (Jellyfish Search Optimizer)
23. CHOA (Chimp Optimization Algorithm)
24. GOA (Grasshopper Optimisation Algorithm)

Testing Benchmark Functions

Testing the precision. robustness and general performance of optimization algorithms [1].

Single Objective

1. Rastrigin function
2. Ackley function
3. Sphere function
4. Rosenbrock function
5. Beale function
6. Goldstein–Price function
7. Booth function
8. Bukin function
9. Matyas function
10. Lévi function
11. Himmelblau's function
12. Easom function
13. Eggholder function
14. Cormick function
15. Schaffer function N. 2
16. Schaffer function N. 4
17. Styblinski–Tang function

Display Functions

new FunctionDisplay();

Installing and Using the Library

TBD : We hope to move the library to the Maven central dependency repository.

Contributing

This is an open source project.

TBD : We will provide you the contribution guidelines soon.

References

[1] https://en.wikipedia.org/wiki/Test_functions_for_optimization

Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation, Vol. 4, No. 2, pp. 150–194 (2013). DOI: 10.1504/IJMMNO.2013.055204