The Coral Reefs Optimization (CRO) algorithm is a novel global-search method based on corals' biology and coral reefs formation. The original idea is by Sancho Salcedo-Sanz, who realized that corals and reef formation may be artificially simulated to solve optimization problems. The algorithm is based on a reef of solutions to a given optimization problem (corals), that reproduce in a similar way as corals do in Nature (Broadcast spawning, brooding and budding operators are implemented). In addition, the larvae (new individuals) fight for space in the reef against other existing corals, and even weak solutions have the possibility of surviving, if there is enough room in the reef. The final algorithm has similarities to evolutionary algorithms and simulated annealing, and it has been shown to improve both approaches in different problems. Some time after the original idea, the first basic CRO algorithm was written in its final form, and it was programmed and tested over several problems. Different improvements and adaptations to deal with real applications have been carried out from this original algorithm. In this page I'll try to maintain an updated list of the publications, news and applications based on CRO.

1. S. Salcedo-Sanz, J. Del Ser, S. Gil-López, I. Landa-Torres and J. A. Portilla-Figueras, "The coral reefs optimization algorithm: an efficient meta-heuristic for solving hard optimization problems," 15th Applied Stochastic Models and Data Analysis International Conference, Mataró, Spain, June, 2013. (pdf)
2. S. Salcedo-Sanz, A. Pastor-Sánchez, D. Gallo-Marazuela and J. A. Portilla-Figueras, "A novel Coral Reefs Optimization algorithm for multi-objective problems,"  The 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'2013), Heifei, China, October, 2013. (pdf)
3. S. Salcedo-Sanz, J. del Ser, I. Landa-Torres, S. Gil-López and J. A. Portilla-Figueras, "The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems," The Scientific World Journal, in press, 2014. (Figure 1 of this paper was prepared from an original image by Dr. Mark Vermeij. I would like to thank Mark for his kindness and help.)  
   
4. S. Salcedo-Sanz, D. Gallo-Marazuela, A. Pastor-Sánchez, L. Carro-Calvo, A. Portilla-Figueras and L. Prieto, "Offshore wind farm design with the Coral Reefs Optimization algorithm," Renewable Energy, vol. 63, pp.109-115, 2014. (pdf)
5. S. Salcedo-Sanz, J. E. Sánchez-García, S. Jiménez-Fernández, J. A. Portilla-Figueras and A. M. Ahmadzadeh, "A Coral-Reef Optimization algorithm for the optimal service distribution problem in mobile radio access networks," Emerging Telecommunication Technologies,  in press, 2013. (pdf) 
6. S. Salcedo-Sanz, C. Casanova-Mateo, A. Pastor-Sánchez and M. Sánchez-Girón, "Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach", Solar Energy, vol. 105, pp. 91-98, 2014.  (pdf)
7. S. Salcedo-Sanz, A. Pastor-Sánchez, A. Blanco-Aguilera, L. Prieto and R. García-Herrera, "Feature Selection in Wind Speed Prediction Systems based on a hybrid Coral Reefs Optimization -- Extreme Learning Machine Approach", Energy Conversion and Management, in press, 2014.
8. S. Salcedo-Sanz, P. García-Díaz, J. A. Portilla-Figueras, J. Del Ser and S. Gil-López, “A Coral Reefs Optimization Algorithm for Optimal Mobile Network Deployment with Electromagnetic Pollution Control Criterion”, Applied Soft Computing, in press, 2014.