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Ab initio protein structure prediction using GPU computing

Perspectives in Science 2016年 8卷 645-647页

作者： Sandhya Parasnath Dubey N. Gopalakrishna Kini M. Sathish Kumar S. Balaji Dept. of CSE Manipal Institute of Technology Manipal University India Dept. of ECE Manipal Institute of Technology Manipal University India Dept. of Biotechnology Manipal Institute of Technology Manipal University India

Graphics processing unit (GPU) accelerated computing has pioneered a new direction of research for various combinatorial optimization problems. One such problem which requires huge computation is protein structure prediction (PSP). PSP is NP -complete problem. Computational prediction of protein native structure from its primary amino acid sequence is termed as ab initio PSP problem. Till date, wet lab experiments conducted on PSP indicate that existing methods take lots of experimentation time and expensive. As a consequence, only 1% of the sequence's structures are known. This work presents a parallel programming approach with GPU computing for PSP using 2D triangular hydrophobic-polar (HP) lattice model. The implementation of proposed approach is tested on the set of HP benchmark sequence of a length ranging from 25 to 100. The experimental result shows that the proposed approach has significantly improved the performance of prediction with immense drop in computation time.

关键词： Protein structure prediction Hydrophobic-polar model NP -problem Parallel programming Graphics processing unit evolutionary programming

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Optimising vaccines supply chains to mitigate the COVID-19 pandemic

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2023年第1期10卷 Article: 2122757页

作者： Rahman, Humyun Fuad Chakrabortty, Ripon K. Paul, Sanjoy Kumar Elsawah, Sondoss Cardiff Metropolitan Univ Cardiff Sch Management Cardiff Wales Univ New South Wales Canberra ADFA Capabil Syst Ctr Sch Engn & Informat Technol Campbell ACT Australia Univ New South Wales Canberra ADFA Sch Engn & Informat Technol Campbell ACT Australia Univ Technol Sydney UTS Business Sch Sydney NSW Australia

A vaccination campaign is one of the most important initiatives to return life to normal in the face of the current COVID-19 epidemic. A successful immunisation programme requires optimising the strategy since uncertainty and disruptions play a role in the decision-making process. Despite the significance of this issue in practical terms, little research has been done to develop the best vaccine delivery strategy while considering uncertainties and interruptions. By developing bi-objective mathematical models that take into account both long-term (such as the allocation of vaccine types, the size of the vaccine centre, and the number of healthcare workers per vaccine centre) and short-term (such as daily order placement) decisions, we investigate the vaccine distribution problem for Canberra city in the Australian Capital Territory. The models also consider vaccination effectiveness and hesitation, disruptions in vaccine supplies, unpredictability in vaccine transportation, and loss of vaccines while handling them. Two meta-heuristic methods are created and put into use to solve these models. The effectiveness of the suggested algorithms is assessed using self-generated examples that were motivated by actual issues. The findings of this study will aid decision-makers in streamlining COVID-19 vaccine supply chains in the face of unpredictability and disruptions.

关键词： Vaccine supply chain genetic algorithm evolutionary programming multi-objective optimisation chance-constrained programming

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A Fast Region of Interest Extraction Approach Based on Stentiford Model of Visual Attention

A Fast Region of Interest Extraction Approach Based on Stent...

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2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

作者： Zhang Jing Zhuo Li Liu Zhixing Sui Lei Signal & Information Processing Lab Beijing University of Technology Beijing China

The model of visual attention proposed by Stentiford can effectively identify salient regions of an image. Since Stentiford model is produced by using evolutionary programming (EP), which usually involves a lot of computation, a fast region of interest (ROI) extraction approach is proposed. The input image is firstly bilinear down-sampled. The visual attention map is calculated with occurrence frequency of a region in the image. The candidate ROI is obtained after Tilling holes and filtering isolated points. The actual ROI is extracted by scaling the candidate ROI with bilinear interpolation. Experimental results show that the proposed approach can faster extract ROI, and have a more accurate ROI detection as well.

关键词： region of interest extraction Stentiford model visual attention evolutionary programming

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Hybrid organization of functional departments and manufacturing cells in the presence of imprecise data

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第2期47卷 343-368页

作者： Feyzioglu, O. Pierreval, H. Galatasaray Univ Dept Ind Engn TR-34357 Istanbul Turkey IFMA CNRS Equipe Rech Syst Prod IFMA LIMOSUMR 6158 F-63175 Aubiere France

Although the benefits of forming cells are widely recognized, certain industrial decision makers do not wish an entire transformation because of possible changes that may occur, for example, in the demand, mix or routing of products. Therefore, hybrid organization of functional departments and manufacturing cells in the same shop may appear quite relevant. The problem addressed here is how to allocate machines and products so as to design such systems that take advantages of both types of layout. We suggest an approach that favours cells for the parts that are regularly manufactured over time (i.e. stable parts), and functional departments for the parts that require more flexibility. We also consider the difficulties arising in the collection of precise data in reality. Hybrid shop design problem is formulated as a constrained fuzzy multi-objective optimization problem and an evolutionary programming algorithm is devised to solve it. The approach is illustrated in test examples.

关键词： Hybrid manufacturing systems Cellular manufacturing Functional layout Multi-objective optimization Fuzzy sets evolutionary programming

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Intelligent Control Design and Implementation of DC Servo Motor

Intelligent Control Design and Implementation of DC Servo Mo...

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2010 International Symposium on Computer,Communication, Control and Automation

作者： Meei-Ling Hung Her-Terng Yau Yuan-Hung Su Far-East University Department of Electrical Engineering

This study focuses on the position feedback control problem of DC servo motors.A suitable IAE fitness function is selected,and then the methods of GA and EP are used to find the optimal PID control gain *** on these optimal PID control gain constants,we add an FLC to the constants to make them the fine tuning gain *** result shows that a better control response can be obtained by combining the FLC with optimal PID ***,a DC servo motor produced by Quanser *** used to verify its effectiveness.

关键词： DC servo motor PID control evolutionary programming Fuzzy Logic Controller

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Optimal coordination of directional overcurrent relays in distribution systems with distributed generation based on a hybrid PSO-LP algorithm

Optimal coordination of directional overcurrent relays in di...

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MedPower 2014

作者： V.A. Papaspiliotopoulos T.A. Kurashvili G.N. Korres National Technical University of Athens Athens GR

ISBN: (纸本)9781510814097

This paper presents a new method for solving the problem of directional overcurrent relays (DOCR) coordination in distribution grids with high penetration of distributed generation (DG), using a hybrid particle swarm optimization (PSO) - linear programming (LP) algorithm. The purpose of the proposed technique is to calculate the values of pickup currents (PU) and time dial (TD) settings of each DOCR which minimize the sum of relays' operating times subject to particular constraints. Moreover, the suggested implementation is able to handle the arising protection issues due to DG infeed and network topology changes and can be integrated into new adaptive protection schemes. Finally, the performance of the developed method will be evaluated on three test systems with varying configurations.

关键词： Distributed Generation evolutionary programming Linear programming Overcurrent Relays Particle Swarm Optimization Power System Protection overcurrent relay distributed power generation evolutionary algorithm linear programming Particle swarm optimization Power system protection dispensing system Test systems Power grids

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Optimizing models of dolphin auditory sensitivity using evolutionary computation

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Bioacoustics 2001年第1期12卷 57-78页

作者： Houser, Dorian S. Helweg, David A. Moore, Patrick W. B. Chellapilla, Kumar Space and Warfare Systems Center—San Diego San Diego CA 92152-6506 Code D3501 53560 Hull Street United States Center for Information Engineering University of California at San Diego La Jolla CA 92093-4007 United States

Object classifiers that attempt to mimic dolphin echolocation require an auditory weighting function representative of dolphin peripheral auditory processing. An evolutionary program (EvPg) was used to fit the frequency-dependent output of a bank of bandpass filters to the auditory sensitivity of the bottlenose dolphin, Tursiops truncatus. Pseudo-Gaussian (PG) and rounded exponential (ROEX) functions were used to describe individual filter shapes. Variables determining the number of filters per model, overall filter shape and amplitude scaling were submitted to the EvPg for optimization. Maximum deviation (P_e) between model output and the sensitivity of the dolphin was used as a measure of similarity between the two, i.e., lower P_e indicated a greater similarity. The number of filters converged upon 37 for all ROEX models and ≤ 45 for all PG models. The P_e of the best-performing PG model was 0.08, and for all ROEX models was 0.13. Greatest deviations typically occurred below 5 kHz and above 130 kHz. Relative audiometric sensitivity of a dolphin ear model has been improved relative to previous models, thereby providing an auditory weighting function more representative of dolphin peripheral auditory processing. This model will be applied to further investigate how dolphins use echolocation to discriminate among objects. © 2001 Taylor & Francis Group, LLC.

关键词： Auditory model Dolphin Ear filter evolutionary programming Spectrum filter

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A DNR and DG Sizing Simultaneously by Using EPSO

A DNR and DG Sizing Simultaneously by Using EPSO

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International Conference on Intelligent Systems, Modelling and Simulation

作者： Mohamad Fani Sulaima Nur Hazahsha Shamsudin Hazrik Izzuan Jaafar Wardiah M. Dahalan Hazlie Mokhlis Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Department of Electrical Engineering University Malaya

ISBN: (纸本)9781479938599

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and evolutionary programming (EP) methods which is called the evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

关键词： evolutionary particle swarm optimization Particle swarm optimization evolutionary programming Distribution generation

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Structure-Based and Property-Compliant Library Design of 11β-HSD1 Adamantyl Amide Inhibitors

Structure-Based and Property-Compliant Library Design of 11...

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作者： Genevieve D. Paderes Klaus Dress Buwen Huang Jeff Elleraas Paul A. Rejto Tom Pauly

Multiproperty lead optimization that satisfies multiple biological endpoints remains a challenge in the pursuit of viable drug candidates. Optimization of a given lead compound to one having a desired set of molecular attributes often involves a lengthy iterative process that utilizes existing information, tests hypotheses, and incorporates new data. Within the context of a data-rich corporate setting, computational tools and predictive models have provided the chemists a means for facilitating and streamlining this iterative design process. This chapter discloses an actual library design scenario for following up a lead compound that inhibits 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme. The application of computational tools and predictive models in the targeted library design of adamantyl amide 11β-HSD1 inhibitors is described. Specifically, the multiproperty profiling using our proprietary PGVL (Pfizer Global Virtual Library) Hub is discussed in conjunction with the structure-based component of the library design using our in-house docking tool AGDOCK. The docking simulations were based on a piecewise linear potential energy function in combination with an efficient evolutionary programming search engine. The library production protocols and results are also presented. less

关键词： Conjugate Gradient Method Protein Structure Analysis HPLC Electrospray Ionization (ESI) Liquid Chromatography-mass Spectrometry library design targeted library Multiproperty lead optimization adamantyl amide 11β-hydroxysteroid dehydrogenase type 1 11β-HSD1 PGVL Pfizer Global Virtual Library structure-based AGDOCK piecewise linear evolutionary programming

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A DNR by Using Rank evolutionary Particle Swarm Optimization for Power Loss Minimization

A DNR by Using Rank Evolutionary Particle Swarm Optimization...

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International Conference on Intelligent Systems, Modelling and Simulation

作者： Mohamad Fani Sulaima Siti Noratika Othman Mohd Saifuzam Jamri Rosli Omar Marizan Sulaiman Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka

ISBN: (纸本)9781479938599

Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional evolutionary programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.

关键词： Distribution network reconfiguration Rank evolutionary particle swarm optimization Particle swarm optimization evolutionary programming

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