Revisiting motif finding: do bi-objective metaheuristics surpass single-objective metaheuristics?

Our findings question the common belief that single-objective metaheuristics are better suited for combinatorial problems like motif finding. The bi-objective formulation helps maintain diversity and avoid premature convergence, even with partially correlated objectives, resulting in better solutions than those obtained through dedicated single-objective optimization. Simple, interpretable problem-specific adaptations can yield substantial performance gains over sophisticated alternatives. These results suggest that bi-objective approaches may provide more robust and computationally efficient solutions for DNA motif discovery, opening new research directions in bioinformatics.