A Coral Reefs Optimization Algorithm with Substrate Layers and Local Search for Large Scale Global Optimization

This paper presents a new version of the Coral Reefs Optimization (CRO) algorithm to improve its performance in large scale global optimization problems. Specifically, we propose to extend the original CRO with different substrate layers, where several exploration operators are defined. This definition allows establishing a competitive co-evolution process within the CRO, which improves the search for optimal solution in large scale optimization problems. The new CRO with substrate layers (CRO-SL) is used in combination with a local search, and the final memetic algorithm obtained has been tested by using a test suite for large scale continuous optimization, showing a robust behavior.