A Hybrid Multiobjective Memetic Metaheuristic for Multiple Sequence Alignment

Rubio-Largo, Álvaro; Vega‐Rodríguez, Miguel A.; González-Álvarez, David L.

doi:10.1109/tevc.2015.2469546

Cited by 40 publications

(28 citation statements)

References 44 publications

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“…To reduce the computational effort, we preselect three multi-objective formulations of MSA and limit our investigation thereon. Thus we choose one of the formulations from among {Gap, SOP} [23], {SOP, TC} [20] and {wSOP, TC} [31,26] (please see Section 4 Table 2). We experiment with five randomly selected replicates (R0, R4, R9, R14, R19) and then judge based on two criteria: firstly, we used multiple linear regression analysis to examine the association between individual objective function and FN rate; secondly, we assess the alignments generated through the optimization of each set of objective functions in terms resultant ML trees.…”

Section: Selection From Among the Existing Formulationsmentioning

confidence: 99%

“…• {wSOP, TC}: Maximize the weighted sum of pairs with affine gap penalties (wSOP) and the number totally aligned columns (TC) [31,26].…”

Section: Objective Functionsmentioning

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%

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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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Section: Selection From Among the Existing Formulationsmentioning

confidence: 99%

“…• {wSOP, TC}: Maximize the weighted sum of pairs with affine gap penalties (wSOP) and the number totally aligned columns (TC) [31,26].…”

Section: Objective Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective formulation of MSA for phylogeny estimation

Nayeem¹,

Bayzid²,

Rahman³

et al. 2018

Preprint

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“…As the VMP problem can be regarded as a combinatorial optimization problem (COP) [44], [45], many EC algorithms may be applicable [28]. EC algorithms have been successfully applied to many COPs, such as protein structure prediction [46], music composition [47], multiple sequence alignment [48], distribution network restoration [49], constrained optimization [50], scheduling problems [51], and haystack problem [52], and have shown promising performance. However, among the EC algorithms, the ACO paradigm [53], [54], especially its ant colony system (ACS) variant, fits COPs better and has shown particular strengths in solving real-world COPs.…”

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confidence: 99%

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Liu

Zhan

Deng

et al. 2018

IEEE Trans. Evol. Computat.

350

212

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Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources. Index Terms-Ant colony system (ACS), cloud computing, virtual machine placement (VMP). I. INTRODUCTION C LOUD computing is a large-scale distributed computing paradigm, driven by an increasing demand for various

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“…As we can see, the output of H4MSA is a set of non-dominated solutions, that is, a set of solutions that represents a trade-off between alignment quality (f 1 ) and consistency (f 2 ). A detailed description of the H4MSA algorithms appears in [18].…”

mentioning

confidence: 99%

A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

Rubio-Largo

Vega‐Rodríguez

2017

Preprint

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The alignment among three or more nucleotides/amino-acids sequences at the same time is known as Multiple Sequence Alignment (MSA), an NP-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem where the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the Hybrid Multiobjective Memetic Metaheuristics for Multiple Sequence Alignment is proposed. In order to evaluate the parallel performance of our proposal, we have selected a pull of datasets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, T-Coffee, Clustal Ω, and MAFFT. The comparative study reveals that our parallel aligner is around 25 times faster than the sequential version with 32 cores, obtaining a parallel efficiency around 80%.

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A Hybrid Multiobjective Memetic Metaheuristic for Multiple Sequence Alignment

Cited by 40 publications

References 44 publications

Multi-objective formulation of MSA for phylogeny estimation

Multi-objective formulation of MSA for phylogeny estimation

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

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