Sine-Cosine-Barnacles Algorithm Optimizer with disruption operator for global optimization and automatic data clustering

In this paper, an improved Barnacles Mating Optimizer (BMO) is proposed to deal with optimization problems and develop a new automatic clustering approach. BMO is a well-established optimization technique inspired by the mating behavior of barnacles in real-life. The exploratory trends of BMO are influential and can maintain the right balance among exploration and exploitation. However, this population-based method can be improved further to reduce the probability of potential drawbacks for any optimization technique. As such, we revised the core searching phased of BMO based on a sine-cosine algorithm (SCA) and disruption operators (DO). The proposed method is named BMSCD, which updates the current solution by switching between the mechanisms of the BMO and SCA based on a probability calculated using the fitness value of the current solution. The experiments results on various benchmark cases for global optimizations demonstrate the improved performance of the proposed BMSCD in terms of quality of solutions, the balance of the exploration- exploitation, and convergence rates. Besides, the proposed BMSCD is evaluated by nine measures in solving different clustering problems. The results show that the BMSCD can effectively and powerfully address the tested problems and provide excellent performance compared to the state-of-the-art methods.