Fish school search (FSS) algorithm has inspired several adaptations for multi-objective problems or binary optimization. However, there is no particular proposition to solve both problems simultaneously. The proposed multi-objective approach binary fish school search (MOBFSS) aims to solve optimization problems with two or three conflicting objective functions with binary decision input variables. MOBFSS is based on the dominance concept used in the multi-objective fish school search (MOFSS) and the threshold technique deployed in the binary fish school search (BFSS). Additionally, the authors evaluate the proposal for feature selection for classification in well-known datasets. Moreover, the authors compare the performance of the proposal with a state-of-art algorithm called BMOPSO-CDR. MOBFSS presents better results than BMOPSO-CDR, especially for datasets with higher complexity.
Sparsity-based evolutionary multi-objective feature selection for multi-label classification
