New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that able to combine different search procedures a single population. work, two improve analyzed. First, (PCRO-SL) presented, which substitutes substrates in population tags associated each individual. Each tag represents operator will modify individual reproduction phase. generation of algorithm, randomly assigned individuals similar probabilities, obtaining way sees more intense changes application operators given than CRO-SL. Second, (DPCRO-SL) probability assignment modified during evolution depending quality solutions generated substrate. Thus, best process be higher probabilities those showed worse performance search. The performances proposed were tested problems, including benchmark functions real wind-turbine-layout optimization, comparing results obtained existing algorithms literature.