At present, most computing systems have multiple cores accessing to a shared memory space. In the case of computing systems devoted to research, it is common to cluster several shared memory architectures together in order to go beyond the limits of a single shared memory architecture. These clusters can represent a significant resource for those researchers who accomplish to exploit them to the fullest. One of the most applied techniques to achieve this purpose is the mixed mode parallel programming which combines both OpenMP and MPI paradigms. In this paper, we present a parallel implementation of a swarm-based evolutionary algorithm designed for solving a complex biological optimization problem. In order to obtain the maximum possible performance, we have combined MPI and OpenMP. Furthermore, in order to solve the addressed problem in a realistic way, we have applied multiobjective optimization. For assessing the performance achieved by our proposal we have conducted experiments under different systems on six biological instances with different sizes. Results point out the relevance of combining mixed mode parallel programming, a swarm-based evolutionary algorithm, and multiobjective optimization from both parallelism and biology points of view. (C) 2018 Elsevier B.V. All rights reserved.

Since the late 1970s, when the first DNA-based genome was sequenced, the field of biology is experiencing a significant growth in the amount of data that needs to be processed. Long ago it became impractical to analyze all this information manually, resulting in a great need for new techniques, algorithms and strategies to facilitate this work. Within the vast world of bioinformatics, we will focus on proteomics, more specifically, on the discovery of small repeated common patterns on sets of protein sequences that may represent some biological functionality. When we analyze a large number of sequences, the problem shows non-deterministic polynomial times, it implies that we could benefit from the combination of high-performance computing and computational intelligence techniques. In this paper, we address the discovery of repeated common patterns as a multiobjective optimization problem by means of a hybrid MPI/OpenMP approach which parallelizes a well-known multiobjective metaheuristic, the fast non-dominated sorting genetic algorithm (NSGA-II). Our main objective is to combine the benefits of shared-memory and distributed-memory programming paradigms to discover patterns in an accurate and efficient manner. Experiments conducted on six different datasets, comparisons with other well-known biological tools, and the obtained speed-ups and efficiencies show that our approach is able to achieve a significant performance in terms of parallel and biological results.

Over the last 25 years, the multiple sequence alignment (MSA) problem has attracted the attention of biologists because it is one of the major techniques used in several areas of computational biology, such as homology searches, genomic annotation, protein structure prediction, gene regulation networks, or functional genomics. This problem implicates the alignment of more than two biological sequences, and is considered as a nondeterministic polynomial time optimization problem. In this paper, we find a number of different approaches for dealing with this biological sequence alignment problem. Basically, we distinguish six main groups: 1) exact methods; 2) progressive methods; 3) consistency-based methods; 4) iterative methods; 5) evolutionary algorithms; and 6) structure-based methods. In this paper, we propose the use of evolutionary computation and multiobjective optimization for solving this bioinformatics problem. A multiobjective version of a memetic metaheuristic is presented: hybrid multiobjective metaheuristics for MSA. In order to prove the effectiveness of the new proposal, we use three structure-based benchmarks created by using empirical data as input. The results obtained by our method are compared with well-known methods published in this paper, concluding that the new approach presents remarkable accuracy when dealing with sets of sequences with a low sequence similarity, the most frequent ones in real world.

The motif discovery problem (MDP) is an important biological optimization problem that has been addressed in numerous ways. However, it is important to note that when we address real complex optimization problems, we should adequately formulate them in order to provide real applicability to the developed techniques. In the particular case of MDP, as we do not know the size of the motifs and the number of repetitions that can be found in the sequences, we must not make any length or pattern-repetition assumptions. In addition, if we consider that it is practically impossible to adequately formulate an optimization problem with a single-objective function formulation, multiobjective optimization can be a good methodology to be considered. In this paper, we propose a novel hybrid multiobjective algorithm for tackling the MDP. Our main objective is to study the results achieved by our algorithm, analysing its performance when different motif occurrence models are considered. As we will see, experimental results on different sets of real instances will point out the advantages and disadvantages of each model, also checking how a more realistic definition of the optimized problem provides better quality biological results.

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BA1iBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature. (c) 2015 Elsevier B.V. All rights reserved.

Multiple Sequence Alignment (MSA) is the process of aligning three or more nucleotides/amino-acids sequences at the same time. It is an NP-complete optimization problem where the time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In the multiobjective version of the MSA problem, we simultaneously optimize the alignment accuracy and conservation. In this work, we present a parallel scheme for a multiobjective version of a memetic metaheuristic: Hybrid Multiobjective Memetic Metaheuristics for Multiple Sequence Alignment (H4MSA). In order to evaluate the parallel performance of H4MSA, we use several datasets with different number of sequences (up to 1000 sequences) and compare its parallel performance against other well-known parallel approaches published in the literature, such as MSAProbs, T-Coffee, Clustal O and MAFFT. On the other hand, the results reveals that parallel H4MSA is around 25 times faster than the sequential version with 32 cores.

In this paper, we propose a parallel multiobjective approach based on the honey bees' behaviour for grooming low-speed traffic requests onto high-capacity optical channels. This problem of grooming low-speed traffic requests is known in the literature as the traffic grooming problem. This problem in mesh optical networks is an NP-hard problem, in which the solution time raises exponentially when the network size increases; therefore, the use of metaheuristics and parallelism jointly is a great option in order to reduce the prohibitive runtime. The parallel approach is implemented for shared-memory architectures by using OpenMP. In our experiments, we have measured the speedup and efficiency of the method with 1, 2, 4, 8 and 16 cores when dealing with three optical networks: small (6-node network, 6 nodes), medium (National Science Foundation, 14 nodes) and large (Nippon Telegraph and Telephone, 55 nodes). Furthermore, we present a comparative study with traditional methods and other multiobjective metaheuristics published in the literature; in which we show the advantages of our multiobjective proposal.

Optimization techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have been used to address an important biological problem known as the motif discovery problem. As it is defined as a multiobjective optimization problem, we have adapted the proposed algorithms to this optimization context. We evaluate the proposed metaheuristics on 54 sequence datasets that belong to four organisms with different numbers of sequences and sizes. The results have been analysed in order to discover which algorithm performs best in each case. The algorithms implemented and the results achieved can assist biological researchers in the complicated task of finding DNA patterns with an important biological relevance.

The rising complexity of memory hierarchies and interconnections in parallel shared memory architectures leads to differences in the communication performance. These differences can be exploited to perform a communication-aware mapping of parallel applications to the hardware topology, improving their performance and energy efficiency. To perform the mapping, it is necessary to determine the communication behavior of the processes and threads of the application. Previous methods rely on static communication traces to detect communication, require hardware changes or support only a subset of parallelization models.We propose CDSM, Communication Detection in Shared Memory, a mechanism that detects communication in from page faults and uses this information to perform the mapping. CDSM works on the operating system level during the execution of the parallel application and supports all parallelization models that use shared memory for communication. It does not require modifications to the applications, previous knowledge about their behavior, or changes to the hardware and runtime libraries. Experiments with the MPI, MPI+OpenMP and OpenMP implementations of the NAS parallel benchmarks, the HPCC benchmark and the PARSEC benchmark suite on a shared memory machine show that CDSM has a high detection accuracy with a negligible overhead. Execution time and processor energy consumption were reduced by up to 35.9% and 18.9%, respectively (10.2% and 7.3%, on average). Experiments on a cluster system, where CDSM optimizes the communication within each node, showed an average execution time reduction of 10.4%.

The future of optical networks is focused on Wavelength Division Multiplexing (WDM) technology. WDM allows simultaneous transmissions of traffic on many non-overlapping channels (wavelengths). Since nowadays the majority of traffic requests only require a bandwidth of Mbps, there exists a waste of bandwidth in these non-overlapping channels because they support traffic in Gbps range. For exploiting the optical network resources effectively, several low-speed traffic requests can be groomed onto a wavelength channel, which is not a simple task. In fact, it is known as the Traffic Grooming problem, and is considered an optimization problem (NP-hard problem). In this work, we suggest the use of multiobjective evolutionary computation and swarm intelligence jointly for solving the Traffic Grooming problem. We have proposed the following swarm algorithms: Artificial Bee Colony, Gravitational Search Algorithm, and Firefly Algorithm; but adapted to multiobjective field: MO-ABC, MO-GSA, and MO-FA respectively. Furthermore, we have adapted the well-known Strength Pareto Evolutionary Algorithm 2, Fast Nondominated Sorting Genetic Algorithm, and Multiobjective Selection Based On Dominated Hypervolume to the Traffic Grooming problem with the aim of evaluating the quality of our swarm proposals. Finally, we present several comparisons with other heuristics and metaheuristics published in the literature by other authors. After comparing with them, we conclude that our approaches overcome the results obtained by other approaches published by other authors.

In wavelength division multiplexing optical networks, the vast majority of traffic demands present a bandwidth request in the range of Mbps which is much lower than the available bandwidth of an optical channel (Gbps). These low-speed traffic requests can be groomed or multiplexed onto one single channel with the aim of optimizing the resources and costs of the network topology. In the literature, this problem of grooming low-speed requests is known as the Traffic Grooming problem, and is considered as an optimization problem. In this work, we present a comparative study of nine multiobjective evolutionary algorithms for solving this NP-hard problem. Two of them are standard metaheuristics in the multiobjective domain: the Fast Non-Dominated Sorting Genetic Algorithm and the Strength Pareto Evolutionary Algorithm. The rest of them are multiobjective versions of well-known evolutionary algorithms: Differential Evolution, Variable Neighbourhood Search, Gravitational Search Algorithm, Artificial Bee Colony, Firefly Algorithm, Ant Colony Optimization, and Particle Swarm Optimization. In the comparative study, the approaches are compared when tackling four optical networks with a different number of nodes: 6, 11, 14, and 55 nodes. Furthermore, for each topology, we use different amounts of traffic: small, medium, and large. The results of this comparative study indicate that the multiobjective versions based on swarm intelligence obtain very good performance in almost all optical networks. Finally, we present a comparison between the best multiobjective approach and other methods published in the literature.

Nowadays, the traffic demands in optical networks are low-speed traffic requests (low bandwidth requirement of a few Mbps) that employ the huge capacity of a fiber channel (Gbps), causing a waste of bandwidth as a result. Fortunately, by using electronic grooming nodes, we can multiplex (groom) several low-speed demands onto one channel in order to optimize the available resources in an optical network. The problem of grooming low-speed traffic requests is known in the literature as the Traffic Grooming problem and is considered an NP-hard optimization problem. In this work, we use both multiobjective optimization and evolutionary computation with the aim of facing this optical networking problem. The selected evolutionary algorithm is based in the behaviour of fireflies, the Firefly Algorithm (FA). In order to optimize more than one conflicting objective function of the Traffic Grooming problem simultaneously, we have modified the standard FA to the multiobjective domain (MO-FA). After carrying out different experiments with diverse real-world optical networks, comparing the results of the MO-FA with other multiobjective approaches and different standard heuristics for this problem, we can conclude saying that the new version of the MO-FA is an effective approach for dealing with this telecommunication problem.

An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2; when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.

Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

Transcriptional regulation is the main regulation of gene expression, the process by which all prokaryotic organisms and eukaryotic cells transform the information encoded by the nucleic acids (DNA) into the proteins required for their operation and development. A crucial component in genetic regulation is the bindings between transcription factors and DNA sequences that regulate the expression of genes. These specific locations are short and share a common sequence of nucleotides. The discovery of these small DNA strings, also known as motifs, is labor intensive and therefore the use of high-performance computing can be a good way to address it. In this work, we present a parallel multiobjective evolutionary algorithm, a novel hybrid technique based on differential evolution with Pareto tournaments (H-DEPT). To study whether this algorithm is suitable to be parallelized, H-DEPT has been used to solve instances of different sizes on several multicore systems (2, 4, 8, 16, and 32 cores). As we will see, the results show that H-DEPT achieves good speedups and efficiencies. We also compare the predictions made by H-DEPT with those predicted by other biological tools demonstrating that it is also capable of performing quality predictions.

Cyclooxygenases (prostaglandin-endoperoxide synthases, (EC 1.14.99.1) 1 and 2 (COX-1 and COX-2)) are key enzymes with a highly functional and pharmacological relevance. Genetic variations in the corresponding genes PTGS1 and PTGS2 are related to diverse human disorders and adverse drug reactions. Although COX-2 is highly inducible, most genetic association studies have focused on coding region gene variants. The aim of this study is to analyze the genetic variants modifying transcription factor binding sites in human PTGS genes based on the combined use of bioinformatics with 1,000 genomes data and replication by next generation sequencing. Updated information on gene sequences and variants was obtained from the 1,000 genomes website and from a replication sequencing study. Of the 570 upstream PTGS1 gene variants, 43 altered binding sites, either by disrupting existing sequences or by creating new binding sites. The most relevant are the SNP rs72769722, which creates a new binding site for NFKB, and the SNPs rs73559017 and rs76403914, both disrupting binding sites for CDX1. Of the 682 upstream PTGS2 gene variants, 31 altered binding sites, the most relevant being rs689466 and rs20417, which disrupt binding sequences for MYB and E2F, respectively; rs689462 which creates a new binding site for POU3F2; and a haplotype combining the SNPs rs34984585+rs10911904, which creates a new binding site for SRY. This study provides a detailed catalog of variant and invariant transcription factor binding sites for PTGS genes and related haplotypes. This information can be useful to identify potential genetic targets for studies related to COX enzymes.

A cis-regulatory element is a non-coding DNA sequence in or near a gene required for its expression, often containing transcription factors binding sites (TFBS). Identifying new TFBSs is fundamental to understand the regulation process of these genes. The optimization problem responsible for these findings is the Motif Discovery Problem (MDP), which is formulated as a short DNA pattern discovery problem. In addition, the MDP is an NP-hard optimization problem since it has to find short strings mixed among a large amount of biological information. In this paper, we propose and study a new hybrid multiobjective evolutionary algorithm based on Differential Evolution (DE) named Hybrid Differential Evolution with Pareto Tournaments (H-DEPT). To demonstrate the superiority of our proposed algorithm on a biological level, we compare the predictions made with those predicted by MEME, BioProspector and BioOptimizer when solving a set of four real sequence datasets. Finally, by using OpenMP, we also demonstrate that this algorithm is highly suitable to be parallelized. As we will see, we have performed several experiments with multi-core systems of 2, 4, and 8 cores; obtaining good parallel results.

The Multiobjective Artificial Bee Colony with Differential Evolution (MO-ABC/DE) is a new hybrid multiobjective evolutionary algorithm proposed for solving optimization problems. One important optimization problem in Bioinformatics is the Motif Discovery Problem (MDP), applied to the specific task of discovering DNA patterns (motifs) with biological significance, such as DNA-protein binding sites, replication origins or transcriptional DNA sequences. In this work, we apply the MO-ABC/DE algorithm for solving the MDP using as benchmark genomic data belonging to four organisms: drosophila melanogaster, homo sapiens, mus musculus, and saccharomyces cerevisiae. To demonstrate the good performance of our algorithm we have compared its results with those obtained by four multiobjective evolutionary algorithms, and their predictions with those made by thirteen well-known biological tools. As we will see, the proposed algorithm achieves good results from both computer science and biology point of views.

In recent years, a huge number of biological problems have been successfully addressed through computational techniques, among all these computational techniques we highlight metaheuristics. Also, most of these biological problems are directly related to genomic, studying the microorganisms, plants, and animals genomes. In this work, we solve a DNA sequence analysis problem called Motif Discovery Problem (MDP) by using two novel algorithms based on swarm intelligence: Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA). To guide the pattern search to solutions that have a better biological relevance, we have redefined the problem formulation and incorporated several biological constraints that should be satisfied by each solution. One of the most important characteristics of the problem definition is the application of multiobjective optimization (MOO), maximizing three conflicting objectives: motif length, support, and similarity. So, we have adapted our algorithms to the multiobjective context. This paper presents an exhaustive comparison of both multiobjective proposals on instances of different nature: real instances, generic instances, and instances generated according to a Markov chain. To analyze their operations we have used several indicators and statistics, comparing their results with those obtained by standard algorithms in multiobjective computation, and by 14 well-known biological methods.