Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost make this algorithm very popular and powerful in solving wide ranges of problems. However, PSO suffers two problems of trapping in local minima and slow convergence speed. Binary version of this algorithm has been introduced for solving binary problems. Because BPSO uses the same concepts of PSO, it also undergoes the same problems. The main part of the binary version is the transfer function. There is not enough study in the literature focusing on the transfer function. In this study, eight new transfer functions dividing into two families (s-shape and v-shape) for binary particle swarm optimization are introduced and evaluated. Four benchmark optimization problems are employed in order to evaluate these transfer functions in terms of avoiding local minima, convergence speed, and accuracy of results. The results prove that the new introduced v-shape family of transfer functions could improve the performance of original binary PSO based on the above-mentioned drawbacks.