A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling

Cluster Computing - Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud...     

Genetic algorithm for multi-objective experimental optimization

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).     

Direct queries for discovering network resource properties in a distributed environment

The development and performance of networkaware applications depends on the availability of accurate predictions of network resource properties. Obtaining this information directly from the network is a scalable solution that provides the accurate performance predictions and topology information needed for planning and adapting application behavior across a variety of networks. The performance predictions obtained directly from the network are as accurate as applicationlevel benchmarks, but the networkbased technique provides the added advantages of scalability and topology discovery. We describe how to determine network properties directly from the network using SNMP. We provide an overview of SNMP and describe the features it provides that make it possible to extract both available bandwidth and network topology information from network devices. The available bandwidth predictions based on network queries using SNMP are compared with traditional predictions based on application history to demonstrate that they are equally useful. To demonstrate the feasibility of topology discovery, we present results for a large Ethernet LAN.     

Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets.     

An effective docking strategy for virtual screening based on multi-objective optimization algorithm

Background  

Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design.     

An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cluster Computing - Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable...     

Cluster optimization algorithm based on CPU and GPU hybrid architecture

With the rapid development of network technology and parallel computing, clusters formed by connecting a large number of PCs with high-speed networks have gradually replaced the status of supercomputers in scientific research and production and high-performance computing with cost-effective advantages. The research purpose of this paper is to integrate the Kriging proxy model method and energy efficiency modeling method into a cluster optimization algorithm of CPU and GPU hybrid architecture. This paper proposes a parallel computing model for large-scale CPU/GPU heterogeneous high-performance computing systems, which can effectively describe the computing capabilities and various communication behaviors of CPU/GPU heterogeneous systems, and finally provide algorithm optimization for CPU/GPU heterogeneous clusters. According to the GPU architecture, an efficient method of constructing a Kriging proxy model and an optimized search algorithm are designed. The experimental results in this paper show that the construction of the Kriging proxy model can obtain a 220 times speedup ratio, and the search algorithm can reach an 8 times speedup ratio. It can be seen that this heterogeneous cluster optimization algorithm has high feasibility.

     

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.     

Improved hybrid optimization algorithm for 3D protein structure prediction

Diuron, a chlorine-substituted dimethyl herbicide, is widely used in agriculture. Though the degradation of diuron in water has been studied much with experiments, little is known about the detailed degradation mechanism from the molecular level. In this work, the degradation mechanisms for OH-induced reactions of diuron in water phase are investigated at the MPWB1K/6–311+G(3df,2p)//MPWB1K/6–31+G(d,p) level with polarizable continuum model (PCM) calculation. Three reaction types including H-atom abstraction, addition, and substitution are identified. For H-atom abstraction reactions, the calculation results show that the reaction abstracting H atom from the methyl group has the lowest energy barrier; the potential barrier of ortho- H (H1’) abstraction is higher than the meta- H abstraction, and the reason is possibly that part of the potential energy is to overcome the side chain torsion for the H1’ abstraction reaction. For addition pathways, the ortho- site (C (2) atom) is the most favorable site that OH may first attack; the potential barriers for OH additions to the ortho- sites (pathways R7 and R8) and the chloro-substituted para- site (R10) are lower than other sites, indicating the ortho- and para- sites are more favorable to be attacked, matching well with the -NHCO- group as an ortho-para directing group.

Dynamic optimization of fed-batch bioreactors using the ant algorithm

The ant colony algorithm, mimicking the cooperative search behavior of ants in real life, has been employed for the dynamic optimization of fed-batch bioreactors. To test the capability of this new heuristic algorithm, two well-known and extensively studied systems have been chosen. The algorithm rapidly converges to optimal feed rate profiles, which maximize the overall production of the desired product and the profits in a computationally efficient and robust manner. The optimal profiles evolved are easy to implement in plant operation. The algorithm compares favorably with the other known techniques.     

Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization

Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases.     

Density estimation using a new dimension adaptive particle swarm optimization algorithm

Current Particle Swarm Optimization (PSO) algorithms do not address problems with unknown dimensions, which arise in many applications that would benefit from the use of PSO. In this paper, we propose a new algorithm, called Dimension Adaptive Particle Swarm Optimization (DA-PSO) that can address problems with any number of dimensions. We also propose and compare three other PSO-based methods with DA-PSO. We apply our algorithms to solve the Weibull mixture model density estimation problem as an illustration. DA-PSO achieves better objective function values than other PSO-based algorithms on four simulated datasets and a real dataset. We also compare DA-PSO with the recursive Expectation-Maximization (EM) estimator, which is a non-PSO-based method, obtaining again very good results.     

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.     

Pareto-based multi-objective optimization for classification in data mining

This paper explores the possibility of classification based on Pareto multi-objective optimization. The efforts on solving optimization problems using the Pareto-based MOO methodology have gained increasing impetus on comparison of selected constraints. Moreover we have different types of classification problem based on optimization model like single objective optimization, MOO, Pareto optimization and convex optimization. All above techniques fail to generate distinguished class/subclass from existing class based on sensitive data. However, in this regard Pareto-based MOO approach is more powerful and effective in addressing various data mining tasks such as clustering, feature selection, classification, and knowledge extraction. The primary contribution of this paper is to solve such noble classification problem. Our work provides an overview of the existing research on MOO and contribution of Pareto based MOO focusing on classification. Particularly, the entire work deals with association of sub-features for noble classification. Moreover potentially interesting sub-features in MOO for classification are used to strengthen the concept of Pareto based MOO. Experiment has been carried out to validate the theory with different real world data sets which are more sensitive in nature. Finally, experimental results provide effectiveness of the proposed method using sensitive data.     

ViroBLAST: a stand-alone BLAST web server for flexible queries of multiple databases and user's datasets

ViroBLAST is a stand-alone BLAST web interface for nucleotide and amino acid sequence similarity searches. It extends the utility of BLAST to query against multiple sequence databases and user sequence datasets, and provides a friendly output to easily parse and navigate BLAST results. ViroBLAST is readily useful for all research areas that require BLAST functions and is available online and as a downloadable archive for independent installation. Availability: http://indra.mullins.microbiol.washington.edu/blast/viroblast.php.     

Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant

Cyanobacteria have potential to produce drop-in bio-fuels such as ethanol via photoautotrophic metabolism. Although model cyanobacterial strains have been engineered to produce such products, systematic metabolic engineering studies to identify optimal strains for the same have not been performed. In this work, we identify optimal ethanol producing mutants corresponding to appropriate gene deletions that result in a suitable redirection in the carbon flux. In particular, we systematically simulate exhaustive single and double gene deletions considering a genome scale metabolic model of a mutant strain of the unicellular cyanobacterium Synechocystis species strain PCC 6803. Various optimization based metabolic modeling techniques, such as flux balance analysis (FBA), method of minimization of metabolic adjustment (MOMA) and regulatory on/off minimization (ROOM) were used for this analysis. For single gene deletion MOMA simulations, the Pareto front with biomass and ethanol fluxes as the two objectives to be maximized was obtained and analyzed. Points on the Pareto front represent maximal utilization of resources constrained by substrate uptake thereby representing an optimal trade-off between the two fluxes. Pareto analysis was also performed for double gene deletion MOMA and single and double gene deletion ROOM simulations. Based on these analyses, two mutants, with combined gene deletions in ethanol and purine metabolism pathways, were identified as promising candidates for ethanol production. The relevant genes were adk, pta and ackA. An ethanol productivity of approximately 0.15 mmol/(gDW h) was predicted for these mutants which appears to be reasonable based on experimentally reported values in literature for other strains.     

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Cluster Computing - Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services...     

PATMAT: a searching and extraction program for sequence, pattern and block queries and databases

A program has been developed that provides molecular biologistswith multiple tools for searching databases, yet uses a verysimple interface. PATMATcan use protein or (translated) DNAsequences, patterns or blocks of aligned proteins as queriesof databases consisting of amino acid or nucleotide sequences,patterns or blocks. The ability to search databases of blocksby ‘on-the-fly’ conversion to scoring matrices providesa new tool for detection and evaluation of distant relationships.PATMAT uses a pull-down, menu-driven interface to carry outits multiple searching, extraction and viewing functions. Eachquery or database type is recognized, reported, and the appropriatesearch carried out, with matches and alignments reported inwindows as they occur. Any of the high scoring matches can beexported to a file, viewed and recalled as a query using onlya few keystrokes or mouse selections. Searches of multiple databasefiles are carried out by user selection within a window. PATMATruns under DOS; the searching engine also runs under UNIX.