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<b>Background</b> <p>The discovery of DNA motifs is essential for studying gene expression and function in many biological systems. Most existing algorithms for motif detection rely on a single optimization criterion or objective function. This study formulates motif finding as a bi-objective optimization problem and investigates whether multi-objective metaheuristics offer potential advantages over single-objective approaches.</p> <b>Results</b> <p>We developed four variants of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) incorporating simple, problem-specific genetic operators. Experiments on six benchmark datasets from three organisms demonstrate that our bi-objective approach significantly outperforms the state-of-the-art Artificial Bee Colony (ABC) metaheuristic. Remarkably, NSGA-II-PMC achieved superior performance over ABC using 6 times fewer fitness evaluations, highlighting its computational efficiency. The synergistic combination of problem-specific operators proved essential, with individual operators showing limited effectiveness compared to their joint application.</p> <b>Conclusions</b> <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Muhammad Ali Nayeem,
  • Shehab Sarar Ahmed,
  • Suliman Aladhadh,
  • M. Sohel Rahman

摘要

Background

The discovery of DNA motifs is essential for studying gene expression and function in many biological systems. Most existing algorithms for motif detection rely on a single optimization criterion or objective function. This study formulates motif finding as a bi-objective optimization problem and investigates whether multi-objective metaheuristics offer potential advantages over single-objective approaches.

Results

We developed four variants of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) incorporating simple, problem-specific genetic operators. Experiments on six benchmark datasets from three organisms demonstrate that our bi-objective approach significantly outperforms the state-of-the-art Artificial Bee Colony (ABC) metaheuristic. Remarkably, NSGA-II-PMC achieved superior performance over ABC using 6 times fewer fitness evaluations, highlighting its computational efficiency. The synergistic combination of problem-specific operators proved essential, with individual operators showing limited effectiveness compared to their joint application.

Conclusions

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.