In this paper, we introduce the use of a population-based selection scheme in a particle swarm optimizer used for designing combinational logic circuits. The scheme aims to distribute the search effort in a better way within the particles of the population as to accelerate convergence while improving the robustness of the algorithm. For our study, we compare six PSO-based approaches, combining different encodings (integer and binary) with both single- and multi-objective selection schemes. The comparative study performed indicates that the use of a population-based approach combined with an integer encoding improves both the robustness and quality of results of PSO when designing combinational logic circuits.