Work

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

Year: 2025

Type: article

Abstract: 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.

Source: BMC Bioinformatics

DOI: 10.1186/s12859-025-06327-6

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

Institutions: Qassim University, Bangladesh University of Engineering and Technology

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,990

Funder: Qassim University