Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

Zambrano-Vega, Cristian; Nebro, Antonio J.; García-Nieto, José; Aldana‐Montes, José F.

doi:10.1007/s13748-017-0116-6

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…A Pareto-set can be found with multi-objective meta-heuristics, such as MOPSO [22] and NSGA-II [20]. We employ NSGA-II meta-heuristic, due to the better performance in comparison with other metaheuristics [23]. The input of the algorithm is (1) the set of sensors S, (2) the network links available between each node L, (3) the set of cloud nodes C c , (4) the map M(m, n) of the area and (5) the set of admissible coordinates for edge nodes L e .…”

Section: Stage-1: Transmission Time and Energymentioning

confidence: 99%

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Aral

Demaio

Brandić

2022

IEEE Trans. Sustain. Comput.

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Section: Stage-1: Transmission Time and Energymentioning

confidence: 99%

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Aral

Demaio

Brandić

2022

IEEE Trans. Sustain. Comput.

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“…They belong to the class of evolutionary algorithms. Several studies ( [27,21,42]) demonstrated the strength of NSGA-II for solving MSA. NSGA-II works best when the number of objectives is upto three while NSGA-III is specially designed for handling more than three objectives.…”

Section: Multi-objective Metaheuristicsmentioning

confidence: 99%

“…During the last decade, we find several studies [20,21,22,23,24,25,26,27] with multiobjective formulation for MSA have been published -proposing two to four objective functions to capture and quantify different aspects of an alignment. Among them probably the most popular is the sum-of-pairs score and its weighted variants, where pairwise score is calculated for each pair of aligned sequences using a substitution matrix.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective formulation of MSA for phylogeny estimation

Nayeem¹,

Bayzid²,

Rahman³

et al. 2018

Preprint

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Multiple sequence alignment (MSA) is a basic step in many analyses in computational biology, including predicting the structure and function of proteins, orthology prediction and estimating phylogenies. The objective of MSA is to infer the homology among the sequences of chosen species. Commonly, the MSAs are inferred by optimizing a single function or objective. The alignments estimated under one criterion may be different to the alignments generated by other criteria, inferring discordant homologies and thus leading to different evolutionary histories relating the sequences. In recent past, researchers have advocated for the multi-objective formulation of MSA, to address this issue, where multiple conflicting objective functions are being optimized simultaneously to generate a set of alignments. However, no theoretical or empirical justification with respect to a real-life application has been shown for a particular multi-objective formulation. In this study, we investigate the impact of multi-objective formulation in the context of phylogenetic tree estimation. Employing multi-objective metaheuristics, we demonstrate that trees estimated on the alignments generated by multi-objective formulation are substantially better than the trees estimated by the state-of-the-art MSA tools, including PASTA, MUSCLE, CLUSTAL, MAFFT etc. We also demonstrate that highly accurate alignments with respect to popular measures like sum-of-pair (SP) score and total-column (TC) score do not necessarily lead to highly accurate phylogenetic trees. Thus in essence we ask the question whether a phylogeny-aware metric can guide us in choosing appropriate multi-objective formulations that can result in better phylogeny estimation. And we answer the question affirmatively through carefully designed extensive empirical study. As a by-product we also suggest a methodology for primary selection of a set of objective functions for a multi-objective formulation based on the association with the resulting phylogenetic tree.

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“…Bioinspired algorithms, such as Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC), have been widely used in Multiple Sequence Alignment (MSA) research due to their remarkable ability to unearth optimal or near-optimal solutions [7,8]. These algorithms mimic natural processes or use population-based strategies, which help them find optimal solutions and avoid becoming stuck in local optima.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Ibrahim,

Yusof,

Eisa

et al. 2024

Applied Sciences

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Multiple Sequence Alignment (MSA) plays a pivotal role in bioinformatics, facilitating various critical biological analyses, including the prediction of unknown protein structures and functions. While numerous methods are available for MSA, bioinspired algorithms stand out for their efficiency. Despite the growing research interest in addressing the MSA challenge, only a handful of comprehensive reviews have been undertaken in this domain. To bridge this gap, this study conducts a thorough analysis of bioinspired-based methods for MSA through a systematic literature review (SLR). By focusing on publications from 2010 to 2024, we aim to offer the most current insights into this field. Through rigorous eligibility criteria and quality standards, we identified 45 relevant papers for review. Our analysis predominantly concentrates on bioinspired-based techniques within the context of MSA. Notably, our findings highlight Genetic Algorithm and Memetic Optimization as the most commonly utilized algorithms for MSA. Furthermore, benchmark datasets such as BAliBASE and SABmark are frequently employed in evaluating MSA solutions. Structural-based methods emerge as the preferred approach for assessing MSA solutions, as revealed by our systematic literature review. Additionally, this study explores current trends, challenges, and unresolved issues in the realm of bioinspired algorithms for MSA, offering practitioners and researchers valuable insights and comprehensive understanding of the field.

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Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

Cited by 14 publications

References 31 publications

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Multi-objective formulation of MSA for phylogeny estimation

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

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