Analysis of Different Binarization Techniques within Whale Optimization Algorithm

In this study, Whale Optimization Algorithm (WOA), which is a recently proposed swarm intelligence-based algorithm, has been used to solve binary optimization problems. As the WOA algorithm has been developed for optimizing real-valued functions, binary versions of the WOA algorithm have been adopted for the binary optimization problems. The performance of modulation-, normalization-, s-shaped transfer function-, and angle modulation-based binarization approaches are compared. The proposed algorithms tested on the well-known benchmark problems, namely one-max, plateau, deceptive, and royal road. Our computational experiments show that angle modulation and normalization-based binarization approaches give the best results, and binary WOA is promising and has potential to handle difficult binary optimization problems.