1. 1. M. Molga and C. Smutnicki, ―Test functions for optimization needs‖, kwietnia, 2005.
   2. Dyke Parunak and S. Brueckner, "Engineering swarming systems,"Methodologies and Software Engineering for Agent Systems, pp.341-376, 2004.
   3. Bonabeau, E., Dorigo, M. and Theraulaz, G.1999: Swarm intelligence. Oxford University Press.
   4. A. Khanna and A. Mishra "A literature based survey on swarm intelligence inspired optimization technique," International Journal of Advanced Technology in Engineering and Science Volume No 03, Special Issue No. 01, March 2015.
   5. M. Dorigo and G. Di Caro, "Ant colony optimization: a new meta-heuristic," 1999.
   6. M. Dorigo, "Optimization, learning and natural algorithms," Ph. D. Thesis, Politecnico di Milano, Italy, 1992.
   7. R. Beckers, J. L. Deneubourg, and S. Goss, "Trails and U-turns in the selection of a path by the ant Lasius niger," Journal of theoretical biology, vol. 159, pp. 397-415, 1992.
   8. Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1997, 1, 53–66.
   9. Stützle T, Hoos H H. Max-Min ant system, future generation computer systems. Future Generation Computer Systems, 2000, 16, 889–914,
   10. Iredi S, Merkle D, Middendorf M. Bi-criterion optimization with multi colony ant algorithms, evolutionary multi-criterion optimization. First International Conference EMO 2001, Zurich, Switzerland, 2001, 359–372.
   11. Manish Dixit,Nikita Upadhyay and Sanjay Silakari ―An Exhaustive Survey on Nature Inspired Optimization Algorithms,‖ International Journal of Software Engineering and Its Applications Vol. 9, No. 4 (2015), pp. 91-104
   12. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (2007) 459–471
   13. Artificial bee colony (abc), harmony search and bees algorithms on Numerical optimization D. Karaboga, b. Akay Erciyes University, the dept. Of computer engineering, 38039, melikgazi, kayseri, turkiye.
   14. Karaboga, D. Artificial Bee Colony Algorithm. Scholarpedia 2010, 5, 6915.Availableonline: http://www.scholarpedia.org/article/Artificial_bee_colony _algorithm/ (accessed on 27 May 2011).
   15. Chaotic Bee Swarm Optimization Algorithm for Path Planning of Mobile Robots Jiann-Horng Lin and Li Ren Huang Department of Information Management IShou University, Taiwan 2009.
   16. Das S, Biswas A, Dasgupta S, Abraham A. Bacterial forag¬ing optimization algorithm: theoretical foundations, analy¬sis, and applications. 2009; 203(1):23–55.
   17. Thomas. Survey of Bacterial Foraging Optimization Algo¬rithm. IJISME. 2013 Mar; 1(4):11–2. ISSN: 2319-6386
   18. Raghavendra V. Kulkarni, and Ganesh Kumar Venayagamoorthy, ―Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes‖, PART C: APPLICATIONS AND REVIEWS(to appear in issue 5, volume 40, 2010)
   19. X. S. Yang, ―Bat algorithm: literature review and applications‖, Int. J. Bio-Inspired Computation, Vol. 5, No. 3, 2013, pp. 141–149.
   20. X. S. Yang and S. Deb, ―Engineering Optimisation by Cuckoo Search‖, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 2010, pp. 330–343.
   21. X. S. Yang and S. Deb, ―Cuckoo search: recent advances and applications,‖ Neural Computing and Applications, vol. 24, no.1, 2014, pp. 169–174.
   22. Fister I, Fister IJR, Yang XS, Brest J. A comprehensive re-view of firefly algorithms swarm and evolutionary compu¬tation. Elsevier; 2013 Dec; 13(13):34–46.
   23. X. S. Yang, ―Firefly Algorithm, Stochastic Test Functions and Design Optimisation‖, Int. J. Bio-Inspired Computation, Vol. 2, No. 2, 2010, pp.78–84.
   24. Xin-She Yang, Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning, International Journal of Swarm Intelligence Research, December 2011.
   25. J. Kennedy and R. Eberhart, ―Particle swarm optimization‖, IEEE international conference on neural networks, 1995, pp. 1942–1948.