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Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

New smart building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented. [ABSTRACT FROM AUTHOR]

Actuator configuration and optimization are of great significance in active control of vibration and noise. This paper proposes an actuator position optimization method for active control system with uncertainties. The uncertain parameters were modeled by interval numbers. Based on interval analysis, the boundaries of eigenvalues of the controllability grammian are obtained and uncertainty propagation analysis method for actuator configuration is presented. Mathematical operation on the eigenvalues of controllability grammian is considered to be the optimization criterion in order to maximize the norm of controllability grammian. Both nominal value and radius of the performance index are considered in the optimization model proposed in this paper. The uncertain optimization model for actuator configuration is transformed into a deterministic optimization model based on weighted processing. Node index number on the finite element mesh is selected as design variable. Since the objective function is non-convexity, genetic algorithm is adopted to get the optimal solution. The feasibility of the robust optimization method is demonstrated by two examples. [ABSTRACT FROM AUTHOR]

This paper utilizes the dynamic programming (DP) approach to deal with the integrated optimization problem for deriving the best configuration and energy split strategies of a hybrid energy storage system (HESS), including a battery and a supercapacitor (SC), for an electric city bus. Within the optimization process, a preset cost function is employed to evaluate the HESS life cycle cost based on a dynamic degradation model of the LiFePO 4 battery, which is initially proposed by us. For system hybridization, the battery size is optimized according to the requested minimal mileage, while the optimal configuration of the SC pack (i.e., the numbers of the SC modules in series and parallel) is determined using the DP approach. It is shown that the life cycle cost of the HESS initially decreases rapidly with the addition of SCs, though the rate of this reduction decreases as the amount of SC increases. The HESS candidates occurring in the transition area can therefore be regarded as the best solutions. For the energy split strategy, several control rules can be extracted from the DP results, and a near-optimal rule-based strategy is proposed in this paper. When compared to the battery-only configuration, the HESS controlled by the rule-based strategy can reduce 47% and 60% of the ESS life cycle cost along the typical China Bus Driving Cycle and the Urban Dynamometer Driving Schedule, respectively. This paper also proves that a well-tuned rule-based strategy, which can be easily implemented in a vehicle, presents rather good performance when compared to the DP approach. In addition, the proposed strategy performance can be further improved by increasing the SC usage. [ABSTRACT FROM AUTHOR]

This paper presents a review on combined cooling, heating, and power (CCHP) systems. This work summarizes the methods used to perform energetic and exergetic analyses, system optimization, performance improvement studies, and development and analysis of CCHP systems, as reported in existing literature. In addition, this work highlights the most current research and emerging trends in CCHP technologies. It is envisioned that the information collected in this review paper will be a valuable source of information, for researchers, designers, and engineers, and provides direction and guidance for future research in CCHP technology. [ABSTRACT FROM AUTHOR]

This paper studies optimal sensor resource management in sensor grids. We formalize the problem using nonlinear optimization theory, which incorporates sensor resource constraint, energy, and expense budget. The paper also presents a pricing-based iterative algorithm for sensor management which balances the sensor user׳ QoS requirements to achieve a sensor system optimization based on the preference of the sensor service users. The paper discusses implementation issues of sensor management. Simulations reveal that the proposed sensor management algorithms can obtain better performance than a previous approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper applies constructal design to study the geometry of a X-shaped isothermal cavity and a X-shaped high conductivity pathway that penetrate into a solid conducting wall. The objective is to minimize the maximal excess of temperature of the whole system, i.e. the hot spots, independent of where they are located. There is uniform heat generation on the solid body, which is insulated on the external perimeter. The total volume and the cavity volume, as well as the high conductivity material volume, are fixed, but the geometric lengths and thickness of both X-shaped configurations can vary. The emerged optimal configurations and performance are reported graphically and numerically. The results indicate that the increase of the complexity of the geometry can facilitate the access of heat currents and improve the thermal performance. The degree of freedom L 1/L 0 proved to be significant on the performance of the X-shaped isothermal cavity, e.g. the once optimized ratio (L 1/L 0)o increases approximately 10% as the area fraction ϕ increases from ϕ =0.05 to 0.3. As for the X-shaped pathway case, it has been demonstrated that the dimensionless thermal conductivity of the path and the area fraction ϕ have a strong effect on the performance and configuration of the X-shaped blades: the twice minimized θ max,mm decreases approximately 70% as increases from 30 to 300 and it decreases approximately 84% as ϕ augments from 0.01 to 0.2. Furthermore, the X-shaped conductive pathways configuration increases its performance monotonically with the augmentation of the pathways thermal conductivity: in correspondence to the highest possible value of , the X-shaped conductive pathways present approximately the same heat removal capacity of the X-shaped cavities optimized in the first part of the paper. [Copyright &y& Elsevier]

Highlights: [•] This paper considers the solution of a real-time optimization problem for a class of unknown discrete-time nonlinear systems. [•] The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying discrete-time estimation problem. [•] The approach is shown to minimize the impact of the choice of dither signals on the performance of the control system. [•] A novel application of extremum-seeking control for model-predictive control. [Copyright &y& Elsevier]

Abstract: Data-intensive science frontiers and challenges are emerg ing as computer technology has evolved substantially. Large-scale simu lations demand significant I/O workload, and as a result the I/O performance often becomes a bottleneck preventing high performance in scientific applications. In this paper we introduce a variety of I/O optimization techniques developed and imple mented when scaling a seismic application to petascale. These techniques include file system striping, data aggreg ation, reader/writer limit ing and less interleaving of data, collective MPI-IO, and data staging. The optimizations result in nearly perfect scalability of the target applicat ion on some of the most advanced petascale systems. The techniques introduced in this paper are applicab le to other scientific applications facing similar petascale I/O challenges. [Copyright &y& Elsevier]

Abstract: This paper describes a computational approach suitable for combined material and geometrically nonlinear analysis by the Finite Element Method. Its main advantage is reuse: once a finite element has been developed with good performance in linear analysis, extension to material and geometrically nonlinear problems is simplified. Extension to geometrically nonlinear problems is enabled by a corotational kinematic description, and that to material nonlinear problems by an optimization-based solution algorithm. The approach thus comprises three ingredients—the development of a high performance linear finite element (for example, using the ANDES concept), a corotational kinematic description, and an optimization algorithm. The main constraint in the application of the corotational formulation is restriction to small deformational displacements. The paper illustrates the realization of the three ingredients on plane stress problems that exhibit elasto-plastic material behavior. Numerical examples are presented to illustrate the effectiveness of the approach. Comparison is made with respect to solutions provided by the commercial nonlinear code ABAQUS as reference. [Copyright &y& Elsevier]

Abstract: This paper studies techniques to reduce the search space when an optimizer seeks an optimal value. The paper promotes a new Evolutionary Algorithm (EA) mutation technique called the “Exponential Moving Average” algorithm (EMA). The paper compares its performance to two other similar Computational Intelligence (CI) algorithms to solve a multi-dimensional problem which has a large search space. Testing of the various algorithms is performed against the same Killer Sudoku puzzle and the results compared. The EMA-based solver outperforms an ordinary Evolutionary Algorithm based solver and a “Mean-Variance Optimization” (MVO) solver. [Copyright &y& Elsevier]

Abstract: The 0–1 knapsack problem (KP01) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays important roles in computing theory and in many real life applications. Chemical reaction optimization (CRO) is a new optimization framework, inspired by the nature of chemical reactions. CRO has demonstrated excellent performance in solving many engineering problems such as the quadratic assignment problem, neural network training, multimodal continuous problems, etc. This paper proposes a new chemical reaction optimization with greedy strategy algorithm (CROG) to solve KP01. The paper also explains the operator design and parameter turning methods for CROG. A new repair function integrating a greedy strategy and random selection is used to repair the infeasible solutions. The experimental results have proven the superior performance of CROG compared to genetic algorithm (GA), ant colony optimization (ACO) and quantum-inspired evolutionary algorithm (QEA). [Copyright &y& Elsevier]

Abstract: In this article, optimization problems with bilinear constraints involving one discrete variable are studied. Several industrial problems present bilinear non-convex constraints which are difficult to solve to global optimality. For this purpose models must be reformulated what in general terms increases the problem size. This article proposes two disjunctive transformation techniques which are compared to other approaches presented in the literature. An analysis is made comparing qualitative and quantitative characteristics of the methods employed. In order to implement proposed transformations, three industrial cases are studied: trim-loss in a paper mill, cutting stock in the production of carton board boxes and the purchase, inventory and delivery optimization problem. All of them are reformulated and solved using the strategies included in the paper. Several instances of each problem are evaluated and their results are analyzed comparing performance of the different methods. [Copyright &y& Elsevier]

Abstract: Frequency response masking (FRM) technique along with the Canonic Signed Digit (CSD) representation is a good alternative for the design of a computationally efficient, sharp transition width, high speed finite impulse response (FIR) filter. This paper proposes two novel approaches for the joint optimization of an FRM FIR digital filter in the CSD space. The first approach uses the recently emerged Artificial Bee Colony (ABC) algorithm and the second approach uses the Differential Evolution (DE) algorithm. In this paper, both the algorithms are modified in such a way that, they are suitable for the solution of the optimization problem posed, in which the search space consists of integers and the objective function is nonlinear. The optimization variables are encoded such that they permit the reduction in computational cost. The salient feature of the above approaches is the reduced computational complexity while obtaining good performance. Simulation results show that the ABC based design technique performs better than that using DE, which in turn outperforms the one using integer coded genetic algorithm (GA). The proposed optimization approaches can be extended to the solution of integer programming problems in other engineering disciplines also. [Copyright &y& Elsevier]

Abstract: Highly structured and hierarchal textiles may be modelled at three scales, ranging from micro modelling of individual fibres within a yarn via meso-scale modelling of a fabric unit cell through to macro-scale modelling of a whole product. The performance of the product can be designed and optimised effectively by variation of the structure at every level. This paper presents an integration of the fibre–yarn, yarn–fabric models. The micro-scale model is developed using an energy-based argument combined with elastica methods to incorporate non-linear frictional behaviour. The meso-scale model is developed using the finite element method, with the yarn modelled as a continuum. The output of the micro-scale model is used to determine constitutive materials properties at the meso-scale model for fabric deformation. The main features of this modelling approach are that many critical design parameters of a yarn and a fabric are being addressed and the friction is incorporated at each scale since inter-fibre and inter-yarn friction may be directly affected by finishing treatments. This paper shows how predictions at the micro scales can pass up the hierarchy to the meso scale to enable designers/engineers to understand and quantify how changes in yarn properties and fabric architectures influence the performance of the fabric, thereby facilitating the design and optimisation of fabric properties by variation of its constituent structural components. [Copyright &y& Elsevier]

Abstract: Power-limited systems with variable Isp, which have been studied theoretically since the beginning of astronautics, are getting closer to practical applications thanks to recent technological advances in the field of magnetosplasma rockets, such as Ad-Astra’s VASIMR concept. This type of propulsion system is considered for high-speed interplanetary transfers, such as Mars missions, with demanding payload fractions that would be compatible with manned missions. This paper explores the problem of the optimization of a power-limited propulsion system through simple performance models, and investigates the trade-off between the technological requirements, the transfer time and the payload fraction 1 [1] The work presented in this paper has been funded by the French space agency CNES. . Following previous works existing in literature, we model the technological characteristics of the vehicle through a small number of parameters, the most important of which being the specific weight (or mass-to-power ratio) of the power generation system. Also, we use in our models the classical “trajectory characteristic” parameter (defined as the integral over time of the squared thrust acceleration) which represents – under certain hypotheses – the propulsion requirements for an orbital or interplanetary transfer with a given time and a given thrust strategy. In this paper, we first give a review of existing methods in literature, then we present the equations of a new class of optimal design which maximizes the payload fraction, for a given transfer time and given technological characteristics. This class of optimal design is described through very simple equations that make possible to study more straightforwardly than existing calculations the links between the main mission requirements (transfer time and payload fraction) and the main technological requirements (specific weight of the power generation and structure mass ratio of the whole vehicle, excluding the power generation system). One important result obtained from these equations is a simple expression which estimates the theoretical upper limit of the power source’s specific weight as a function of transfer time and the payload mass ratio. In the last part of this paper, we apply this simple performance model to discuss the feasibility of a fast Earth-to-Mars transfer using a power-limited system. [Copyright &y& Elsevier]

Abstract: This paper uses TSIM, a cycle accurate architecture simulator, to characterize the memory performance of SPEC CPU2006 Benchmarks under CMP platform. The experiment covers 54 workloads with different input sets, and collects statistical information of instruction mixture and cache behaviors. By detecting the cyclical changes of MPKI, this paper clearly shows the memory performance phases of some SPEC CPU2006 programs. These performance data and analysis results can not only help program developers and architects understand the memory performance caused by system architecture better, but also guide them in software and system optimization. [Copyright &y& Elsevier]

Abstract: One of the important research and technological problems in data warehouse query optimization concerns star queries. So far, most of the research focused on optimizing such queries by means of join indexes, bitmap join indexes, or various multidimensional indexes. These structures neither support navigation well along dimension hierarchies nor optimize joins with the Time dimension, which in practice is used in most of the star queries. In this paper we propose an index, called Time–HOBI, for optimizing the star queries that compute aggregates along dimension hierarchies. Time–HOBI, created on a dimension hierarchy, is composed of (1) a Hierarchically Organized Bitmap Index (HOBI), where one bitmap index is maintained for one dimension level, and (2) a Time Index (TI) that implicitly encodes time in every dimension. HOBI allows to quickly search for fact rows satisfying predicates defined on different levels of dimension hierarchies. With the support of TI joining a fact table with the Time dimension is avoided. Thus, Time–HOBI supports a broad class of star queries. In this paper we explain how query execution plans for star queries can profit from Time–HOBI. We show, based on experiments, the efficiency of Time–HOBI for different classes of queries, as compared to HOBI and a traditional bitmap index. Based on the experiments, we also demonstrate how sensitive Time–HOBI is to variable selectivity of queries. We also analyze the maintenance time of Time–HOBI as compared to HOBI and a traditional bitmap index. The experiments used in the paper have been conducted on a real dataset, coming from the biggest East-European Internet auction platform Allegro.pl. The experiments show that Time–HOBI can be successfully applied to the optimization of star queries as it offers promising performance improvement. [Copyright &y& Elsevier]

Abstract: Multi-objective optimization problem is reaching better understanding of the properties and techniques of evolutionary algorithms. This paper presents the Dynamic Particle Swarm Optimization algorithm for solving multiobjective optimization problem. This Dynamic PSO is different from the existing PSO''s and some local version of PSO in terms of swarm size, topology and search space. In this paper swarm size criteria for dynamic PSO is considered. Experiment conducted for standard benchmark functions of multi-objective optimization problem, which shows the better performance rather the basic PSO. [Copyright &y& Elsevier]

Abstract: A scientific workflow can be viewed as formal model of the flow of data between processing components. It often involves a combination of data integration, computation, analysis, and visualization steps. An emerging use case involves determining some input parameters that minimize (or maximize) the output of a computation. Kepler is a good framework for specifying such optimizations because arbitrary computations can be composed into a pipeline, which is then repeated until an optimal set of inputs is found. Genetic Algorithms are generic optimization algorithms based on the principles of genetics and natural selection, and are well suited for models with discontinuous objective functions. This paper discusses an implementation of a Genetic Algorithm in Kepler, building on the Nimrod/OK framework. The resulting tool is generic and flexible enough to support a variety of experimental domains. The paper reports a number of experiments that demonstrate the performance with a set of benchmarking functions. [Copyright &y& Elsevier]

Abstract: Many practical problems culminate with solving optimization problems. Thus, many methods have been introduced for solving these types of problems. The need for algorithms that are fast and more accurate at finding global minimums is ever increasing. One of the promising methods is a heuristic and iterative method called Evolutionary Programming (EP). It is one of the computational methods used in optimization that is implemented for many practical applications. Many papers have shown the capability of this algorithm for addressing a variety of optimization problems. These studies have opened a vast new and interesting field of research. Recently, many methods have been proposed for promoting the performance of EP when finding the optimum point of functions or applications; however, EP has some shortcomings that cause slow convergence on some functions, especially multimodal functions. By overcoming these shortcomings, EP could be more effective in the optimization research field. This paper introduces new methods for overcoming these disadvantages and promoting the performance of EP. One of these methods, which has the best results on cost functions, changes the searching procedure by adding a new factor to produce offspring and pulling offspring toward a gathering point (the mean value of the parents). This method was tested on 50 well-known test functions discussed in the literature and was compared with state-of-the-art algorithms on twenty-two new cost functions. Finally, a hybrid method of CEP and MCEP (Momentum Coefficient Evolutionary Programming) called IMCEP (Improved Momentum Coefficient Evolutionary Programming) is introduced. The results of the calculations reported here show the efficiency of MCEP and IMCEP. [Copyright &y& Elsevier]

Abstract: In this paper, performance-based parameter tuning method of model-driven Two-Degree-of-Freedom PID (MD TDOF PID) control system has been proposed to enhance the control performances of a process. Known for its ability of stabilizing the unstable processes, fast tracking to the change of set points and rejecting disturbance, the MD TDOF PID has gained research interest recently. The tuning methods for the reported MD TDOF PID are based on internal model control (IMC) method instead of optimizing the performance indices. In this paper, an Integral of Time Absolute Error (ITAE) zero-position-error optimal tuning and noise effect minimizing method is proposed for tuning two parameters in MD TDOF PID control system to achieve the desired regulating and disturbance rejection performance. The comparison with Two-Degree-of-Freedom control scheme by modified smith predictor (TDOF CS MSP) and the designed MD TDOF PID tuned by the IMC tuning method demonstrates the effectiveness of the proposed tuning method. [Copyright &y& Elsevier]

Abstract: Heterojunction silicon wafer solar cells, using a microcrystalline silicon (μc-Si:H) thin-film emitter and a very thin intrinsic amorphous silicon (a-Si:H) passivation layer between the crystalline silicon wafer and the emitter layer, have been reported to exhibit stable performance and high efficiency. Desired properties for the emitter layer include wide bandgap, low surface and interface recombination, and good doping efficiency. In this study, we report on the thin-film properties of p-doped μc-Si:H emitter layers deposited using RF (13.56MHz) PECVD, at different SiH4/H2 gas flow ratios, pressures, and temperatures at the same RF power. Trends relating deposition conditions to relevant film characteristics such as thickness, crystalline fraction and conductivity are discussed. Finally, device relevant symmetrical p+/i/c-Si/i/p+ heterojunction lifetime test structures are investigated, using the optimised parameters for p-doped μc-Si:H layers (discussed in this paper) and for intrinsic a-Si:H layers (discussed in a companion paper ). These exhibit promising effective lifetimes of up to 2.4ms at an injection level of 1015cm-3. [Copyright &y& Elsevier]

Highlights • Inspected non-repairable systems subject to a delay-time failure are considered. • A hidden failure cannot stop system functioning while a fatal failure causes the entire system loss. • A probabilistic model for evaluating performance metrics of the system is suggested. • A problem of finding the inspection schedule minimizing the total expected loss is formulated and solved. • Examples demonstrating the effects of system parameters on the optimization solutions are provided. Abstract Motivated by real-world critical applications such as aircraft, medical devices, and military systems, this paper models non-repairable systems subject to a delay-time failure process involving hidden and fatal failures in two stages during their missions. A hidden failure cannot cause the system to stop functioning while a fatal failure causes the entire system loss. The system undergoes scheduled inspections for detecting the hidden failure. In the case of a positive inspection result, the system main mission is aborted and a rescue operation is started to mitigate the risk of the entire system loss. The inspections are imperfect and may produce false positive and negative failures. We propose probabilistic models for evaluating performance metrics of the system considered, including mission success probability, system survival probability, expected number of inspections during the mission, and total expected losses. Based on the evaluation models, we formulate and solve an optimization problem of finding the optimal inspection schedule on a fixed mission time horizon to minimize the total expected loss. Examples are provided to demonstrate the proposed methodology and effects of key system parameters on system performance and optimization solutions. [ABSTRACT FROM AUTHOR]

Abstract Searching within the sample space for optimal solutions is an important part in solving optimization problems. The motivation of this work is that today's problem environments have increasingly become dynamic with non-stationary optima and in order to improve optima search, memetic algorithm has become a preferred search method because it combines global and local search methods to obtain good solutions. The challenge is that existing search methods perform the search during the iterations without being guided by solid information about the nature of the search environment which affects the quality of a search outcome. In this paper, a spy search mechanism is proposed for memetic algorithm in dynamic environments. The method uses a spy individual to scope out the search environment and collect information for guiding the search. The method combines hyper-mutation, random immigrants, hill climbing local search, crowding and fitness, and steepest mutation with greedy crossover hill climbing to enhance the efficiency of the search. The proposed method is tested on dynamic problems and comparisons with other methods indicate a better performance by the proposed method. Graphical abstract Highlights • Addition of extra comparison algorithm in the results (MOGA). • Deeper discussion of the methods and all components that form the structure of SSM. • Highlighting specific references for problems covered in the scope of this work. • These extensions are relevant because, (1) they show how SSM performs when compared with an array of different problems (2) give more information and situate the methods in relevant existing body of work (3) provide additional clarification on the references for problems tackled in the article. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we compare some traditional statistical methods for predicting financial distress to some more “unconventional” methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including 33 financial, 8 non-financial and 1 combined macroeconomic index, using principle component analysis (PCA) to extract suitable variables. This paper makes four critical contributions: (1) with nearly 80% fewer financial ratios by the PCA method, the prediction performance is still able to provide highly-accurate forecasts of financial bankruptcy; (2) we show that traditional statistical methods are better able to handle large datasets without sacrificing prediction performance, while intelligent techniques achieve better performance with smaller datasets and would be adversely affected by huge datasets; (3) empirical results show that C5.0 and CART provide the best prediction performance for imminent bankruptcies; and (4) Support Vector Machines (SVMs) with evolutionary computation provide a good balance of high-accuracy short- and long-term performance predictions for healthy and distressed firms. Therefore, the experimental results show that the Particle Swarm Optimization (PSO) integrated with SVM (PSO–SVM) approach could be considered for predicting potential financial distress. [Copyright &y& Elsevier]

Abstract: Digital Fountain code is a forward error correction code developed in recent years. The code does not need to know the channel information, and can be decoded with high probability. LT code is the first practical digital fountain code, it has fully inherited the advantages of digital fountain code. This paper introduces the concept and the classifications of Digital Fountain code, especially about the encoding and decoding process, the degree distribution and the application environment of LT code; the parameters of the degree distribution of LT code are discussed through the simulation, the optimization parameters of LT code are given and the importance of each parameter is analyzed; the paper has obtained the specific value of the decoding failure probability of Digital Fountain code; the effects of the amount of data received on LT code as well as the best amount of data have been studied; Finally, a model about how the Digital Fountain code can be used in the real application is given, make it gradually been used in the practical application. [Copyright &y& Elsevier]

Abstract: Shape optimization design has been paid more attention in many industrial fields, but the application of automatic method for naval ship hydrodynamic design has not reached the same level of maturity. Even so, it introduces numerical tools, combines with CFD and optimization methods, which contribute to ship design, can improve the ship performance, and reduce the cost of development and building. This paper proposes to apply the CFD technology to automatic optimization of hull form of naval ship, and builds hull form optimization frame based on CFD, to execute form optimization for sonar position of surface combatant –David Taylor model basin (DTMB) 5415 ship model. The result illustrates that the research of the paper has great value in engineering application. [Copyright &y& Elsevier]

Abstract: High performance processes should operate close to design boundaries and specification limits, while still guaranteeing robust performance without design constraint violations. Since design chemical process is operating close to tighter boundaries safely; much attention has been devoted to integrating design and control, in which the design decisions, dynamics, and control performance are considered simultaneously in some optimal fashion. However, rigorous methods for solving design and control simultaneously lead to challenging mathematical formulations which easily become computationally intractable. In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced (). We showed that substantial problem size reduction can be achieved by embedding control for specific process designs. In this paper, we extend the embedded control methodologies to plantwide flowsheet. The case study for the reactor-column flowsheet will demonstrate the current capabilities of the methodology for integrating design and control under uncertainty. [Copyright &y& Elsevier]

Abstract: This paper presents an optimal control strategy suitable for investigating the mission performance of a spacecraft equipped with a NASAʼs Evolutionary Xenon Thruster. Using an accurate mathematical model of the propulsion system, based on both experimental data and a semi-empirical schematization of the power generation system, the minimum-time rendezvous problem with various celestial bodies is investigated through an indirect approach. Unlike a conventional approach, in which the thruster power is varied with continuity within a given range, here the solar electric propulsion system is described using a finite number of operation points, characterized by different pairs of thruster input power and propellant mass flow rate. The model includes the possibility of obtaining coasting phases along the spacecraft trajectory. The optimal control strategy is found for a three-dimensional problem in which the actual shape and relative orientation of the initial and final orbits are taken into account. The paper presents the simulation results for a number of minimum-time trajectories towards inner planets (Mercury, Venus, and Mars) and three Near Earth Asteroids (NEAs) of particular interest for the scientific community: 1998 KY-26, 99942 Apophis, and 2007 VK-184. [Copyright &y& Elsevier]

Abstract: The paper investigates the periodic operation of a continuous bioreactor described by an unstructured model for which the yield coefficient is linearly dependant on the substrate. The model was studied by a number of authors who showed its ability to predict oscillatory behavior for some range of dilution rates. The paper investigates the different dynamic behaviors predicted by the model when the substrate feed concentration is periodically varied. It is shown that when a region of stable limit cycle is forced with a relatively high forcing frequency, the behavior of the process alternates between periodic and complex chaotic behavior via a number of mechanisms of transition, including period doubling and intermittency. It is shown that the time-average performance of the bioreactor can be substantially improved by an appropriate selection of the forcing amplitude. The effect of the position of the center of forcing on the nature of the emerging patterns are also studied. [Copyright &y& Elsevier]

Abstract: In this work the input amplitude saturation problem is analysed in the Quantitative Feedback Theory (QFT) framework. This paper reviews previous works in the literature dealing with the input amplitude saturation problem in the presence of an uncertain plant in the frequency domain using QFT. The objective of this paper is to compare the different available approaches and summarize the design process for each case so that this paper can be used as a tutorial; there are six main approaches to this problem. Two of these approaches use the classical two degrees of freedom control scheme for QFT; in both of these, the design constraints of a linear QFT compensator are added in the loop shaping stage: they are added in the first approach to avoid excitation of the actuator saturation and in the second one to guarantee global stability. The other three techniques are considered as anti-windup (AW) approaches. Starting from a base design in QFT with two degrees of freedom, the first AW approach introduces a third degree of freedom that guarantees the stability of the system, allowing for base designs for high performance. The other two AW approaches also introduce a third degree of freedom, but they take simple stability considerations into account and focus on the performance of the system. The last solution consists of using a reference governor technique, which guarantees the computation of a reference signal for an inner control loop that is shaped using QFT in such a way that robust stability will be guaranteed. The reference governor technique is a time domain approach that implies the resolution of an optimization problem. The rest of the approaches are frequency domain techniques based on a loop shaping method in the traditional QFT sense. [Copyright &y& Elsevier]

Abstract: This paper presents joint contexts optimization in mobile grid. The paper describes device context information for context-aware services in the mobile device collaboration. The objective of the paper is to dynamically deliver services to mobile grid users according to current context of mobile grid environment. A utility function is used as objective function that expresses values for the current contexts. The optimization is carried out by the joint context parameter optimizer with respect to an objective function. A joint contexts optimization algorithm is proposed which decomposes mobile grid system optimization problem into sub-problems. In the experiment, the performance evaluation of joint contexts optimization algorithm is conducted. [ABSTRACT FROM AUTHOR]

Abstract: Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for multi-category sparse data classification problems. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for sparse data classification problem depends critically on three free parameters. They are, the number of hidden neurons, the input weights and the bias values which need to be optimally chosen. Selection of these parameters for the best performance of ELM involves a complex optimization problem. In this paper, we present a new, real-coded genetic algorithm approach called ‘RCGA-ELM’ to select the optimal number of hidden neurons, input weights and bias values which results in better performance. Two new genetic operators called ‘network based operator’ and ‘weight based operator’ are proposed to find a compact network with higher generalization performance. We also present an alternate and less computationally intensive approach called ‘sparse-ELM’. Sparse-ELM searches for the best parameters of ELM using K-fold validation. A multi-class human cancer classification problem using micro-array gene expression data (which is sparse), is used for evaluating the performance of the two schemes. Results indicate that the proposed RCGA-ELM and sparse-ELM significantly improve ELM performance for sparse multi-category classification problems. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel approach to improve prediction accuracy of GM(1,1) model through optimization of the initial condition in this paper. The new initial condition is comprised of the first item and the last item of a sequence generated from applying the first-order accumulative generation operator on the sequence of raw data. Weighted coefficients of the first item and the last item in the combination as the initial condition are derived from a method of minimizing error summation of square. We can actually find that the newly modified GM(1,1) model is an extension of the original GM(1,1) model and another modified model which takes the last item in the generated sequence as the initial condition when weighted coefficients takes distinctly specific values. The new optimized initial condition can express the principle of new information priority emphasized on in grey systems theory fully. The result of a numerical example indicates that the modified GM(1,1) model presented in this paper can obtain a better prediction performance than that from the original GM(1,1) model. [Copyright &y& Elsevier]

Abstract: Voltage regulation is an important task in electrical engineering for controlling node voltages in a power network. A widely used solution for the problem of voltage regulation is based on adjusting the taps in under load tap changers (ULTCs) power transformers and, in some cases, turning on Flexible Alternating Current Transmission Systems (FACTS), synchronous machines or capacitor banks in the substations. Most papers found in the literature dealing with this problem aim to avoid voltage drops in radial networks, but few of them consider power losses or meshed networks. The aim of this paper is to present and evaluate the performance of several multi-objective algorithms, including hybrid approaches, in order to minimize both voltage deviation and power losses by operating ULTCs located in high voltage substations. In particular, a well-known multi-objective algorithm, PAES, is used for this purpose. PAES finds a set of solutions according to Pareto-optimization concepts. Furthermore, this algorithm is hybridized with simulated annealing and tabu search to improve the quality of the solutions. The implemented algorithms are evaluated using two test networks, and the numerical results are analyzed with two metrics often used in the multi-objective field. The results obtained demonstrate the good performance of these algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems. [Copyright &y& Elsevier]

The robustness and efficiency of performance measure approach (PMA) depend on the reliability loop in reliability-based design optimization (RBDO). For the reliability loop in the PMA using the minimum performance target point (MPTP) search, existing approaches can obtain stable results but may converge to inaccurate results, and higher computational efforts are required to achieve the optimum results for highly nonlinear problems. In this paper, a hybrid descent mean value (HDMV) approach is proposed based on a novel merit function, which is applied to combine the MPTP search formulas of the descent mean value (DMV) and advanced mean value (AMV). The merit function is used to adaptively control the numerical instability of the inverse reliability analysis for RBDO-based PMA. The accuracy, robustness and efficiency of the proposed DMV and HDMV methods are compared with existing methods through four nonlinear performance functions, two structural RBDO problems and a complex aircraft panel problem. The results illustrate that the DMV and HDMV methods are more robust, efficient and accurate than existing reliability methods. For the aircraft panel problem, a simultaneous buckling pattern is finally achieved by the proposed methods with better performance in terms of both convergence rate and computational efficiency. [ABSTRACT FROM AUTHOR]

In recent years there has been increased concern on energy efficiency of industries. Since the scheduling problems in the shop floors are directly related to the energy consumption, an appropriate way to improve energy efficiency in industrial plants is to develop effective scheduling strategies. Hence, the aim of this paper is to design an energy-efficient scheduling in a shop floor industrial environment, i.e., flexible job-shop scheduling problem (FJSP). To this end, a multi-objective optimization model is developed with three objective functions: (i) minimizing total completion time, (ii) maximizing the total availability of the system, and (iii) minimizing total energy cost of both production and maintenance operations in the FJSP. To cope with this multi-objective optimization problem, an enhanced evolutionary algorithm incorporated with the global criterion, as a multi-objective handling technique, is proposed and then performance evaluation is performed based on an extensive numerical analysis. [ABSTRACT FROM AUTHOR]

Proportional-integral-derivative (PID) control is widely used in industry because of its simple structure and convenient implementation. However, PID control is suitable for small time delay systems; while if too large delay is encountered, PID control may not obtain the desired performance. Proportional-integral-proportional-derivative (PI-PD) control is a modified of PID control and can get improved control performance; however, due to the complex controller parameter tuning, the PI-PD control is used in a limited scope. Inspired by the advantage of predictive functional control (PFC), a new PI-PD control design using PFC optimization is proposed in this paper. The proposed method not only inherits the advantage of PFC, which does well in coping with the time delay, but also has the same structure as the PI-PD controller. The proposed method is tested on the preheated temperature control of crude oil in a fluidized catalytic cracking unit. The results show that the proposed controller improves control performance compared with typical PID control and PI-PD control. [ABSTRACT FROM AUTHOR]

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H ∞ offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement. [ABSTRACT FROM AUTHOR]

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers. [ABSTRACT FROM AUTHOR]

This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example. [ABSTRACT FROM AUTHOR]

Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas – copies of the application with the same functionality – for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer. Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing. To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load-balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF)—believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability. [ABSTRACT FROM AUTHOR]

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori , due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques. [ABSTRACT FROM AUTHOR]

In flotation process, the operating conditions under which the concentrate grade is high are the same conditions that lead to a lower recovery and selectivity. Thus, in the face of contradictory, but inherent characteristics of flotation phenomenon, it is necessary to apply appropriate techniques of multi-objective optimization in order to meet specific needs. In the present paper the Firefly Algorithm, associated with the Pareto dominance criterion and with an anti-stagnation operator, is used to determine the best operating conditions of the column flotation of an igneous apatite ore. The aim is to find the conditions that lead to a maximum P 2 O 5 grade and recovery maximization, with adequate selectivity. The results indicated that the proposed approach was able to obtain the operating conditions which led to the best performances for this flotation process. [ABSTRACT FROM AUTHOR]

This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between the proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effectiveness for a high-dimensional space but works well on a low-dimensional space. Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of individuals' performance and the classic Pareto nondominated sorting procedure, BiGE divides individuals into different nondominated layers and attempts to put well-converged and well-distributed individuals into the first few layers. From a series of extensive experiments on four groups of well-defined continuous and combinatorial optimization problems with 5, 10 and 15 objectives, BiGE has been found to be very competitive against five state-of-the-art algorithms in balancing proximity and diversity. The proposed approach is the first step towards a new way of addressing many-objective problems as well as indicating several important issues for future development of this type of algorithms. [ABSTRACT FROM AUTHOR]

The Blocks Relocation Problem seeks to find the shortest sequence of movements to retrieve a set of homogeneous blocks placed in a two-dimensional storage according to a predefined order. In this paper we analyze an optimization model recently published in the literature. We illustrate that this model reports infeasible solutions and does not guarantee the optimality of the achieved solutions in some cases. In order to overcome this fact, we propose an alternative optimization model. However, its high computational consumption of temporal and space resources in large environments encourages us to also develop a branch and bound algorithm to solve realistic scenarios to optimality. This algorithm includes an intelligent strategy to explore the most promising nodes in the underlying tree. Additionally, its performance can be easily adapted to report high-quality solutions at the expense of sacrificing the optimality guarantee. In contrast to previous exact proposals, the computational experiments reveal the high efficiency of the branch and bound algorithm, reporting optimal solutions for the most widely extended benchmark suite from the related literature through short computational times. [ABSTRACT FROM AUTHOR]