The Arctic Puffin Optimization (APO) Algorithm is a recently proposed metaheuristic algorithm that has been widely applied to solve optimization problems in continuous spaces. However, it cannot be directly used to solve combinatorial optimization problems in discrete spaces. To address these limitations, a Binary Arctic Puffin Optimization (BAPO) Algorithm is proposed, focusing on developing transfer functions to convert the algorithm’s continuous solutions into discrete binary solutions. Two primary transfer function types, S-shaped and V-shaped, are commonly employed. Experimental analysis identifies optimal functions for different algorithmic stages. These are then integrated with a conversion factor to propose a hybrid transfer function for the binarization of the Puffin Optimization Algorithm. To address the issue of slow particle convergence in the later stages of the exploration phase and the tendency to overlook high-quality solutions during the exploitation phase in the binary algorithm, logarithmic inertia weight and the golden sine strategy are incorporated, respectively, for improvement. Simulation experiments were conducted to solve both single-dimensional and multidimensional 0–1 knapsack problems. Experimental data and convergence curves, including mean values and standard deviations, were analyzed. The results demonstrate that the binary Arctic puffin optimization algorithm exhibits excellent convergence, stability, and fast search speed.
Wang et al. (Wed,) studied this question.