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imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical programming

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第4期17卷 1222-1230页

作者： Yasui, Niko Vogiatzis, Chrysafis Yoshida, Ruriko Fukumizu, Kenji Univ Alberta Dept Comp Sci Edmonton AB T6G 2R3 Canada North Carolina A&T State Univ Dept Ind & Syst Engn Greensboro NC 27406 USA Naval Postgrad Sch Dept Operat Res Monterey CA 93943 USA Inst Stat Math 10-3 Midoricho Tachikawa Tokyo 1908562 Japan Inst Stat Math Res Ctr Stat Machine Learning 10-3 Midoricho Tachikawa Tokyo 1908562 Japan Grad Univ Adv Studies 10-3 Midoricho Tachikawa Tokyo 1908562 Japan

Advances in modern genomics have allowed researchers to apply phylogenetic analyses on a genome-wide scale. While large volumes of genomic data can be generated cheaply and quickly, data missingness is a non-trivial and somewhat expected problem. Since the available information is often incomplete for a given set of genetic loci and individual organisms, a large proportion of trees that depict the evolutionary history of a single genetic locus, called gene trees, fail to contain all individuals. Data incompleteness causes difficulties in data collection, information extraction, and gene tree inference. Furthermore, identifying outlying gene trees, which can represent horizontal gene transfers, gene duplications, or hybridizations, is difficult when data is missing from the gene trees. The typical approach is to remove all individuals with missing data from the gene trees, and focus the analysis on individuals whose information is fully available - a huge loss of information. In this work, we propose and design an optimization-based imputation approach to infer the missing distances between leaves in a set of gene trees via a mixed integer non-linear programming model. We also present a new research pipeline, imPhy, that can (i) simulate a set of gene trees with leaves randomly missing in each tree, (ii) impute the missing pairwise distances in each gene tree, (iii) reconstruct the gene trees using the Neighbor Joining (NJ) and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) methods, and (iv) analyze and report the efficiency of the reconstruction. To impute the missing leaves, we employ our newly proposed non-linear programming framework, and demonstrate its capability in reconstructing gene trees with incomplete information in both simulated and empirical datasets. In the empirical datasets apicomplexa and lungfish, our imputation has very small normalized mean square errors, even in the extreme case where 50 percent of the individuals in each gene tree a

关键词： Gene trees missing information mixed integer non-linear programming

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Optimal heat exchanger network synthesis with the detailed heat transfer equipment design

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COMPUTERS & CHEMICAL ENGINEERING 2007年第11期31卷 1432-1448页

作者： Ravagnani, M. A. S. S. Caballero, J. A. Univ Estadual Maringa DEQ UEM BR-87020900 Maringa Parana Brazil Univ Alicante Dept Ingn Quim Alicante 03690 Spain

This paper presents an optimisation model for the synthesis of heat exchanger networks (HEN) including the detailed design of the equipments formulated as a decomposition method. Shell and tube pressure drops and fouling are considered, as well as mechanical aspects, like shell and tube bundle diameters, internal and external diameter of tubes, number of tubes, number of baffles, number of shells, tube length, tube arrangement and the fluid allocation in the heat exchanger. The optimisation model is based on area, energy and pumping costs. The algorithm combines two distinct models, in a decomposition method, a mixed integer non-linear programming (MINLP) superstructure simultaneous optimisation model for the heat exchanger network synthesis considering stream splitting and a MINLP model for the detailed equipment design, following rigorously the standards of the TEMA. Two examples from the literature are used to test the algorithm developed, and the results confirm the achievement of the optimum HEN configuration with the detailed heat exchangers design, following the TEMA standards. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： optimisation heat exchanger network synthesis heat exchanger design mixed integer non-linear programming mathematical programming

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Joint Video Packet Scheduling, Subchannel Assignment and Power Allocation for Cognitive Heterogeneous Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2017年第3期16卷 1703-1712页

作者： Xu, Lei Nallanathan, Arumugam Song, Xiaoqin Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Kings Coll London Dept Informat London WC2R 2LS England Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210096 Jiangsu Peoples R China

In this paper, a joint video scheduling, subchannel assignment, and power allocation problem in cognitive heterogeneous networks are modeled as a mixed integer non-linear programming (MINLP), which maximizes the minimum video transmission quality among different secondary mobile terminals (MTs) subject to the total available energy at each secondary, the total interference power at each primary base station, the total available capacity at each radio interface of each secondary MTs, and the video sequence encoding characteristic. In order to solve it, we decompose the original MINLP as joint subchannel and power allocation problem and video packet scheduling problem. Then, we model the joint subchannel and power allocation problem as a max-min fractional programming, and transform it as a convex optimization problem. Finally, we utilize dual decomposition method to design a joint subchannel and power allocation algorithm, and propose a video packet scheduling scheme based on auction theory to maximize the video quality for each secondary MT. Simulation results demonstrate that the proposed framework not only improves the video transmission quality significantly, but also guarantees the fairness among different secondary MTs.

关键词： Cognitive heterogeneous networks packet scheduling video traffic auction theory mixed integer non-linear programming

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Market-based transmission expansion planning by improved differential evolution

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2010年第5期32卷 450-456页

作者： Georgilakis, Pavlos S. Natl Tech Univ Athens Elect Power Div Sch Elect & Comp Engn Athens 15780 Greece

The restructuring and deregulation has exposed the transmission planner to new objectives and uncertainties. As a result, new criteria and approaches are needed for transmission expansion planning (TEP) in deregulated electricity markets. This paper proposes a new market-based approach for TEP. An improved differential evolution (IDE) model is proposed for the solution of this new market-based TEP problem. The modifications of IDE in comparison to the simple differential evolution method are: (1) the scaling factor F is varied randomly within some range, (2) an auxiliary set is employed to enhance the diversity of the population, (3) the newly generated trial vector is compared with the nearest parent, and (4) the simple feasibility rule is used to treat the constraints. Results from the application of the proposed method on the IEEE 30-bus test system demonstrate the feasibility and practicality of the proposed IDE for the solution of TEP problem. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Competitive electricity markets Transmission expansion planning mixed integer non-linear programming Differential evolution

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An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

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CONTROL ENGINEERING PRACTICE 2018年 70卷 98-113页

作者： Mogale, D. G. Kumar, Sri Krishna Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a mixed integer non-linear programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Food grain distribution problem Transportation Inventory mixed integer non-linear programming Chemical reaction optimization

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Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第1期19卷 293-309页

作者： Ti Ti Nguyen Vu Nguyen Ha Long Bao Le Schober, Robert Univ Quebec INRS Montreal PQ H5A 1K6 Canada Ecole Polytech Montreal Montreal PQ H3T 1J4 Canada Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91058 Erlangen Germany

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65 compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

关键词： Servers Resource management Wireless communication Delays Optimization Computational modeling Data compression Fog computing resource allocation computation offloading hierarchical fog cloud data compression energy saving latency mixed integer non-linear programming

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Impact analysis of RES-based DGs with economical considerations in restructured electricity market

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第5期11卷 472-479页

作者： Singh, A. K. Parida, S. K. Inst Infrastruct Technol Res & Management IIRAM Dept Elect Engn Ahmadabad Gujarat India Indian Inst Technol Dept Elect Engn Patna Bihar India

The integration of distributed generators (DGs) into the grid is of great importance in improving system reliability. The criterion of minimizing total system cost used previously by many researchers for locating the optimal sites for DGs using optimal power flow (OPF) formulations was considered in this work. Here, three different cost functions are formulated for three kinds of renewable energy source (RES). Three different objectives were considered separately for determining the optimal locations for each kind of RES using the mixed integer non-linear programming (MINLP) method. Having many alternatives with these three objectives, the analytic hierarchy process (AHP) was used to reach a decision over getting the optimal locations for various kinds of RES. The proposed methodology was demonstrated on a 15-node distribution system.

关键词： Analytic hierarchy process distributed generators mixed integer non-linear programming optimal power flow renewable energy sources

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Integrating reliability optimization into chemical process synthesis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2002年第3期78卷 247-258页

作者： Goel, HD Grievink, J Herder, PM Weijnen, MPC Delft Univ Technol Fac Technol NL-2600 GA Delft Netherlands Delft Univ Technol Fac Sci Appl NL-2600 GA Delft Netherlands

The growing need to achieve high availability for large integrated chemical process systems demands higher levels of system reliability at the operational stage. In these circumstances, it has become critical to consider the reliability aspects of a system and its components at the design stage. Traditional reliability/availability analysis methods and maintenance optimization frameworks, commonly applied at the design stage, are limited in their application, as in most of these methods the designer is required to specify the process system components, their connectivity and their reliabilities a priori. As a result, these traditional methods do not provide the flexibility to reconfigure a process or select initial reliabilities of equipment in a way that maximizes the inherent plant availability at the design stage. In this paper, we developed an optimization framework by combining the reliability optimization and process synthesis challenges and the combined optimization problem is posed as a mixed integer non-linear programming optimization problem. The proposed optimization framework features an expected profit objective function, which takes into account the trade-off between initial capital investment and the annual operational costs by supporting appropriate estimation of revenues, investment cost, raw material and utilities cost, and maintenance cost as a function of the system and its component availability. The effectiveness and usefulness of the proposed optimization framework is demonstrated for the synthesis of the hydrodealkylation process (HDA) process. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： reliability optimization process synthesis mixed integer non-linear programming conceptual design

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Modeling and forecasting energy flow between national power grid and a solar-wind-pumped-hydroelectricity (PV-WT-PSH) energy source

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ENERGY CONVERSION AND MANAGEMENT 2017年 136卷 382-394页

作者： Jurasz, Jakub AGH Univ Sci & Technol Fac Management Dept Engn Management Krakow Poland

The structure of modern energy systems has evolved based on the assumption that it is the demand side which is variable, whilst the supply side must adjust to forecasted (or unforecasted) changes. But the increasing role of variable renewable energy sources (VRES) has led to a situation in which the supply side is also becoming more and more unpredictable. To date, various approaches have been proposed to overcome this impediment. This paper aims to combine mixed integer modeling with an Artificial Neural Networks (ANN) forecasting method in order to predict the volume of energy flow between a local balancing area which is using PV-WT-PSH and the national power system (NPS). Calculations has been performed based on the hourly time series of wind speed, irradiation and energy demand. The results indicate that both probabilistic and ANN models generate comparably accurate forecasts;however, the opportunity for improvement in the former appears to be significantly greater. The mean prediction error (for a one hour ahead forecasts) for the best model was 0.15 MW h, which amounts to less than 0.2% of a mean hourly energy demand of the considered energy consumer. The proposed approach has huge potential to reduce the impact of VRES on the NPS operation as well as can be used to facilitate the process of their integration and increase their share in covering energy demand. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： non-dispatchable energy sources Artificial neural networks RES integration mixed integer non-linear programming

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Parallel solution of disjunctive MINLP problems

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CHEMICAL ENGINEERING COMMUNICATIONS 2001年第1期185卷 115-124页

作者： Björkqvist, J Westerlund, T Abo Akad Univ Proc Design Lab Dept Chem Engn FIN-20500 Turku Finland

In this paper, a system for parallel solution of large scale disjunctive optimization problems is presented. Disjunctive optimization problems occur in process engineering;both in production scheduling and in process synthesis. In general these problems are computationally very hard to solve. This system combines the demands of process engineering optimization problems with the potential CPU-power of the computers connected to the Internet. The system is capable of using an arbitrary number of computers connected to the Internet for distributed solution of rigorous industrial optimization problems.

关键词： optimization disjunctive constraints mixed integer non-linear programming transformations scheduling problems

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