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The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis. [ABSTRACT FROM AUTHOR]

The work reported in this paper aims at developing an algorithm for identifying the worst contingencies to be used in a security-constrained optimal power flow problem. A bilevel max-min optimization model is proposed to find the worst contingency scenario while solving an ac optimal power flow problem and considering corrective actions. The objective is to minimize the total cost including generation and load shedding cost while satisfying all relevant constraints. The upper-level problem allows representing contingencies using binary variables. We formulate a second-order cone problem in the lower-level to represent the ac power flow equations. Duality is used to merge the upper- and lower-level problems into a single-level one. The resulting mixed-integer second-order conic problem can be efficiently solved. Three case studies are presented as applications of this novel technique. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is an important research area of signal sampling and compression, and the essence of signal recovery in CS is an optimization problem of solving the underdetermined system of equations. Greedy pursuit algorithms are widely used to solve this problem. They have low computational complexity; however, their recovery performance is limited. In this paper, an intelligence recovery algorithm is proposed by combining the Bat Algorithm (BA) and the pruning technique in subspace pursuit. Experimental results illustrate that the proposed algorithm has better recovery performance than greedy pursuit algorithms. Moreover, applied to the microseismic monitoring system, the BA can recover the signal well. [ABSTRACT FROM AUTHOR]

Abstract Source term estimation (STE) of atmospheric dispersion plays an important role in public safety, environmental protection and many other application fields. In this paper, several new composite cost functions for STE using hybrid genetic algorithm are proposed and compared using Nemenyi test based on 68 STE tasks from Prairie Grass field experiment. Results show one of the new composite cost functions, named as WSD, has outstanding performance in estimating both source location and emission rate. Then the patterns in STE results using different cost functions are analyzed based on the 68 tasks mentioned above, which provides further insights into what to expect from STE. At last, the relationship between composite cost functions and multi-objective optimization is analyzed to facilitate the understanding of composite cost functions. To summarize, composite cost functions such as WSD has the potential to achieve a better balance between sensitivity and robustness of cost functions applied in STE, providing the most accurate estimates. Statistical algorithm comparison techniques like Nemenyi test can help us better understand the characteristics and performance of specific settings in STE methods. Graphical abstract Highlights • New composite cost functions are introduced to source term estimation. • Nemenyi test is used to compare different cost functions using multiple data sets. • One of the new composite cost function WSD has outstanding performance. • Bias patterns of the results using different cost functions are analyzed. • The relationship with multi-objective optimization is demonstrated. [ABSTRACT FROM AUTHOR]

Herein, the sample complexity of general weighted $\ell _1$ minimization in terms of support recovery from noisy underdetermined measurements is analyzed. This analysis generalizes prior work on $\ell _1$ minimization by considering arbitrary weighting. The explicit relationship between the weights and the sample complexity is stated such that for random matrices with i.i.d. Gaussian entries, the weighted $\ell _1$ minimization recovers the support of the underlying signal with high probability as the problem dimension increases. This result provides a measure that is predictive of relative performance of different algorithms. Motivated by the analysis, a new iterative reweighted strategy is proposed for binary signal recovery. In the binary sparsity-Promoting Reweighted $\ell _1$ minimization (bPRL1) algorithm, a sequence of weighted $\ell _1$ minimization problems are solved where partial support recovery is used to prune the optimization; furthermore, the weights used for the next iteration are updated by the current estimate. bPRL1 is compared to other weighted algorithms through the proposed measure and numerical results are shown to provide superior performance for a spectrum occupancy estimation problem motivated by cognitive radio. [ABSTRACT FROM AUTHOR]

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts. [ABSTRACT FROM AUTHOR]

A robust newsvendor model with discrete demand is initiatively studied, and the steps to obtain the optimal ordering decision are provided. The study shows that the optimal ordering decision with discrete demand is very different from that with continuous demand. Besides, the total number of demand points has almost no effect on the performance of both ordering decisions. Furthermore, for an ultralow-profit product, the ordering decision with discrete demand performs significantly better than that with continuous demand. [ABSTRACT FROM AUTHOR]

This paper aims to present an elegant way to design multimodal piezo transducers, i.e., piezoelectric sensors/actuators that succeed in filtering a set of desired eigenmodes among those belonging to a bigger set prescribed beforehand. That can be efficiently done by regarding the design problem as one of the topology optimization, where the design variable taking on the values either −1 or 1 physically represents the polarity of the electrodes. It can be analytically proved that this optimization problem has classical solution, and indeed it is unique. Motivated by fabrication issues happening at the micro scale, it is also considered the robust version of that problem, that is, the design of such devices whenever the feature size of both polarity phases are underneath limited. [ABSTRACT FROM AUTHOR]

The vibration signals of rotating machinery are frequently disturbed by background noise and external disturbances because of the equipment’s particular working environment. Thus, robustness has become one of the most important problems in identifying the unbalance of rotor systems. Based on the observation that external disturbance of the unbalance response often displays sparsity compared with measured vibration data, we present a new robust method for identifying the unbalance of rotor systems based on model residual sparsity control. The residual model is composed of two parts: one part takes regular measurements of noise, while the other part evaluates the impact of external disturbances. With the help of the sparsity of external disturbances, the unbalance identification is converted into a convex optimization problem and solved by a sparse signal reconstruction algorithm. Experiment results have shown that the proposed method is robust and effective in identifying the unbalance of rotor systems in a complex environment, improving the precision of unbalance estimation and simplifying the balancing process. [ABSTRACT FROM AUTHOR]

Rotating machines have become increasingly powerful and rapid over time, often working now close to, or even above, their critical speeds. When, as a result, resonance or dynamic instability occurs, it can cause high vibration levels in these machines, particularly when rolling bearings are used. Vibration control of rotating systems can be made by viscoelastic dynamic vibration neutralizers (VDVNs), which are relatively cheap passive devices with a wide range of applications. For the control of flexural vibration in dynamic rotors, a translational VDVN is usually employed. It should be attached to the rotor by means of a special support, commonly between bearings, where the amplitude of the vibration mode of concern is high. The particular point where this type of device is attached depends on the mode to be controlled and in machines with reduced internal space, it cannot be placed in a suitable position. Therefore, this paper introduces a new type of VDVN, the angular VDVN, and proposes a methodology for the optimal design of a set of these devices for shaft slope degree of freedom control. This control aims at indirectly reducing flexural vibration. According to this approach, the control device can be installed close to the bearing where the slope degree of freedom presents its highest value at any critical speed. The conceptual design of the angular VDVN is presented to illustrate the proposed methodology, and a numerical example is given to demonstrate the influence of the angular VDVN geometry on the response. The corresponding results are fully discussed. [ABSTRACT FROM AUTHOR]

The increased compression ratios achieved by the High Efficiency Video Coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality degradation. This paper proposes a method based on a two-stage approach to improve the error robustness of HEVC streaming, by reducing temporal error propagation in the case of frame loss. The prediction mismatch that occurs at the decoder after frame loss is reduced through the following two stages. First, at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimization, which distributes the use of reference pictures, by reducing the number of prediction units that depend on a single reference. Second, at the streaming stage, a motion vector (MV) prioritization algorithm, based on spatial dependencies, selects an optimal subset of MVs to be transmitted, redundantly, as side information to reduce mismatched MV predictions at the decoder. The simulation results show that the proposed method significantly reduces the effect of temporal error propagation. Compared with the reference HEVC, the proposed reference picture selection method is able to improve the video quality at low-packet-loss rates (e.g., 1%) using the same bitrate, achieving quality gains up to 2.3 dB for 10% of packet loss ratio. It is shown, for instance, that the redundant MVs are able to boost the performance achieving quality gains of 3 dB when compared with the reference HEVC, at the cost using 4% increase in total bitrate. [ABSTRACT FROM AUTHOR]

This paper investigates robust consensus for nonlinear multiagent systems with uncertainty and disturbance. The consensus evolution behavior is studied under general consensus protocol when each node is disturbed by the relative states between the node and its neighbors. At first, the robust consensus condition is obtained and the convergency analysis is given by using Lyapunov stability theory and matrix theory. Then, the practical consensus is investigated and the bound of the error states is presented. Finally, two numerical simulation examples are given to illustrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing multi-connected control of a robotic platform designed to protect technological objects and human operators from low-frequency influences on part of the moving base. The platform includes six drive mechanisms with stepper motors. The problem is solved by the methods of the modern theory of robust stabilization and optimal control based on H∞-optimization in the state space. We construct a mathematical model of the multidimensional system, taking into account the characteristics of electromechanical drives and using signals of feedback sensors as state variables. We give an example of synthesizing a multidimensional optimal stabilizing controller in the form of state feedback for a system with disturbances bounded in L2-norm. We define the feedback control structure and obtain the matrix of feedback coefficients. We also show the results of mathematical modeling. [ABSTRACT FROM AUTHOR]

Variations in the concentrations of biomolecular species in vivo are inevitable, but regulation systems will act to maintain concentrations of certain species within proper levels. Such a ubiquitous trait in biological systems is the so-called concentration robustness. In this work, we study the concentration robustness of glycerol metabolism system, in which some mechanistic details are not clearly known and the true metabolic system need to be identified from all possible ones. We give a quantitative index to measure the concentration robustness of the considered system, which is characterized as the maximum relative variation in certain entries of steady state caused by the perturbations of operating parameters. Based on the proposed robustness index, a minimax dynamic optimization problem is developed for identifying the kinetic parameters as well as the unknown metabolic mechanisms. A scheme based on Monte-Carlo method is proposed to evaluate the robustness index approximately and convergence result is obtained. An algorithm is constructed to solve the dynamic optimization problem and numerical results are presented to show that the proposed robustness index could measure concentration robustness of the considered system properly. [ABSTRACT FROM AUTHOR]

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion. [ABSTRACT FROM AUTHOR]

Currently, in high-performance applications, the vector control (VC) scheme of induction motor (IM) is widely employed in industry. The VC scheme has significant features of decoupling torque and flux; also, its hardware implementation is easier. Conventionally, PID control schemes are frequently used for variable speed operation. However, the performance of the VC scheme is limited over a wide range of speed operation because of de-tuning caused by parameter uncertainties. To address the aforementioned challenging problem, adaptive and robust control strategies are mostly implemented. This paper presents various novel, adaptive, and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg-Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLC based on Gauss-Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM. The focal motive is to accomplish fast dynamic response with fault-tolerant capability, load disturbance rejection qualities, insensitivity to the parameter uncertainties, robustness to speed variation, and to acquire maximum efficiency as well as torque. The d-q modeling of the IVC IM in the synchronous reference frame and space vector pulse width modulation (SVPWM) employed in inverter are designed in MATLAB/Simulink. Our work also presents critical, analytical, and comparative assessment of the proposed controllers with traditional tuned PI control strategy for the electrical faults perturbation, load disturbances, speed variations, and parameter uncertainties. Furthermore, the simulation results of the above-mentioned designed control strategies validated robust, smooth, and faster response with permissible overshoot, undershoot, settling time, and rise time for the IVC IM drive system, compared to prior works. [ABSTRACT FROM AUTHOR]

In this work, the exponential weighted square-of-error functions are exploited for controller design. It is reported that the exponential weight in square-of-error objective function is related to the maximum sensitivity of the process. Therefore, it is possible to control the maximum sensitivity of the process without measuring the frequency response. Moreover, the existing integral-square-error- and integral-time-square-error-based proportional-integral-derivative (PID) tuning rule is modified with the proposed exponential weighted square-of-error functions that allow the user to further tune the maximum sensitivity of the process. The proposed theory is corroborated with the help of a gain optimisation example by the traditional minimisation approach and a PID controller design example by the proposed tuning rule (for the stable/unstable process) and stochastic optimisation approach. [ABSTRACT FROM AUTHOR]

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach. [ABSTRACT FROM AUTHOR]

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed mobility robustness optimization algorithm to minimize handover failures due to radio link failures by adjusting time-to-trigger and offset parameters. According to the reason for failure, the algorithm classifies handover failure into three categories (too late, too early, and wrong cell), and simultaneously optimizes three handover parameters according to the dominant failure. Moreover, the algorithm considers handover failures to each neighboring cell and adjusts handover parameters individually. Via simulation, we show how the proposed algorithm adaptively optimizes the parameters and outperforms previous algorithms in various mobile environments. [ABSTRACT FROM PUBLISHER]

The following game is played on a weighted graph: Alice selects a matching M and Bob selects a number k. Alice's payoff is the ratio of the weight of the k heaviest edges of M to the maximum weight of a matching of size at most k. If M guarantees a payoff of at least then it is called α-robust. In 2002, Hassin and Rubinstein gave an algorithm that returns a 1/ √2-robust matching, which is best possible. We show that Alice can improve her payoff to 1/ln(4) by playing a randomized strategy. This result extends to a very general class of independence systems that includes matroid intersection, b-matchings, and strong 2-exchange systems. It also implies an improved approximation factor for a stochastic optimization variant known as the maximum priority matching problem and translates to an asymptotic robustness guarantee for deterministic matchings, in which Bob can only select numbers larger than a given constant. Moreover, we give a new LP-based proof of Hassin and Rubinstein's bound. [ABSTRACT FROM AUTHOR]

In this paper we consider ambiguous stochastic constraints under partial information consisting of means and dispersion measures of the underlying random parameters. Whereas the past literature used the variance as the dispersion measure, here we use the mean absolute deviation from the mean (MAD). This makes it possible to use the 1972 result of Ben-Tal and Hochman (BH) in which tight upper and lower bounds on the expectation of a convex function of a random variable are given. First, we use these results to treat ambiguous expected feasibility constraints to obtain exact reformulations for both functions that are convex and concave in the components of the random variable. This approach requires, however, the independence of the random variables and, moreover, may lead to an exponential number of terms in the resulting robust counterparts.We then show how upper bounds can be constructed that alleviate the independence restriction, and require only a linear number of terms, by exploiting models in which random variables are linearly aggregated. Moreover, using the BH bounds we derive three new safe tractable approximations of chance constraints of increasing computational complexity and quality. In a numerical study, we demonstrate the efficiency of our methods in solving stochastic optimization problems under mean-MAD ambiguity. [ABSTRACT FROM AUTHOR]

Given a classification task, an approach to improve accuracy relies on the use of abstaining classifiers. These classifiers are trained to reject observations for which predicted values are not reliable enough: these rejected observations belong to an abstaining area in the feature space. Two equivalent methods exist to theoretically compute the optimal abstaining area for a given classification problem. The first one is based on the posterior probability computed by the model and the other is based on the derivative of the ROC function of the model. Although the second method has proved to give the best results, in small-sample settings such as the one found in omics data, the estimation of posterior probabilities and derivative of ROC curve are both lacking of precision leading to far from optimal abstaining areas. As a consequence none of the two methods bring the expected improvements in accuracy. We propose five alternative algorithms to compute the abstaining area adapted to small-sample problems. The idea of these algorithms is to compute an accurate and robust estimation of the ROC curve and its derivatives. These estimation are mainly based on the assumption that the distribution of the output of the classifier for each class is normal or mixture of normal distributions. These distributions are estimated by a kernel density estimator or Bayesian semiparametric estimator. Another method works on the approximation of the convex hull of the ROC curve. Once the derivative of the ROC curve are estimated, the optimal abstaining area can be directly computed. The performance of our algorithms are directly related to their capacity to compute an accurate estimation of the ROC curve. A sensitivity analysis of our methods to the dataset size and rejection cost has been done on a set of experiments. We show that our methods improve the performances of the abstaining classifiers on several real datasets and for different learning algorithms. [ABSTRACT FROM AUTHOR]

An autodisturbance-rejection control (ADRC) of an induction motor based on an ant colony optimization (ACO) algorithm is proposed in this paper, in order to realize the precise decoupling of the induction motor and the disturbance compensation. A novel control method employs ACO as an automatic tune mechanism for an ADRC controller. According to the feedback information from the induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of ACO after the iterative calculation; therefore, the reliance of the ADRC controller on parameters can be reduced. The simulation and experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than that of the conventional ADRC when the disturbances occur and that the method is feasible and effective. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study characterizations of minimal and approximate minimal solutions for a unified vector optimization problem in a Hausdorff real topological vector space. These characterizations have been obtained via scalarizations which are based on general order representing and order preserving properties. A nonlinear scalarization based on Gerstewitz function is shown to be a particular case of the proposed scalarizations. Furthermore, in the setting of normed space, characterizations are given for minimal solutions by using scalarization function based on the oriented distance function. Finally, under appropriate assumptions it is shown that this function satisfies order representing and order preserving properties. [ABSTRACT FROM AUTHOR]

We treat uncertain linear programming problems by utilizing the notion of weighted analytic centers and notions from the area of multi-criteria decision making. After introducing our approach, we develop interactive cutting-plane algorithms for robust optimization, based on concave and quasi-concave utility functions. In addition to practical advantages, due to the flexibility of our approach, we prove that under a theoretical framework proposed by Bertsimas and Sim (Oper Res 52:35-53, 2004), which establishes the existence of certain convex formulation of robust optimization problems, the robust optimal solutions generated by our algorithms are at least as desirable to the decision maker as any solution generated by many other robust optimization algorithms in the theoretical framework. We present some probabilistic bounds for feasibility of robust solutions and evaluate our approach by means of computational experiments. [ABSTRACT FROM AUTHOR]

This paper deals with a novel robust current control scheme for permanent magnet synchronous machine drives that improves the conventional cascade structure. The main idea is to substitute the commonly used decoupling network, which requires the precise knowledge of the system parameters in any working condition, with a more flexible and robust observer scheme. In particular, the traditional configuration is improved by means of two proportional-integral observers that allow the preservation of the desired drive performances even in the presence of disturbances and uncertainties. The increased robustness is achieved by maintaining the reliable basic scheme with a pure proportional controller in the forward path with advantages in terms of design and tuning. A genetic algorithm is used to optimize the controller and observer parameters. This multiobjective optimization leads to good stability and an overall enhanced performance of the drive above the operative range. The choice of the optimal solution is based on a novel fitness function that takes into account both dynamical features and disturbance rejection capabilities of the system. The effectiveness of the proposed control scheme compared with a standard proportional-integral control is verified by means of numerical simulations and experiments as well. [ABSTRACT FROM AUTHOR]

In this paper, a genetic algorithm with new multi-parent crossover (GA-MPC) is used for solving the optimal power flow problem. This method is tested on the IEEE 30-bus test system for 10 different cases where, in addition to the total fuel cost, different complexities and constraints like: emission, multi-fuels, valve-point effect and prohibited zones are considered. The effectiveness and robustness of the GA-MPC method are assessed using a statistical study and a comparative study with some well-known optimization methods. The obtained results along with the comparison study indicate that the GA-MPC method provides effective and high-quality solution when solving the optimal power flow problem even for complicated and highly constrained working conditions. [ABSTRACT FROM AUTHOR]

We propose a novel edge-based visual–inertial fusion approach to address the problem of tracking aggressive motions with real-time state estimates. At the front-end, our system performs edge alignment, which estimates the relative poses in the distance transform domain with a larger convergence basin and stronger resistance to changing lighting conditions or camera exposures compared to the popular direct dense tracking. At the back-end, a sliding-window optimization-based framework is applied to fuse visual and inertial measurements. We utilize efficient inertial measurement unit (IMU) preintegration and two-way marginalization to generate accurate and smooth estimates with limited computational resources. To increase the robustness of our proposed system, we propose to perform an edge alignment self check and IMU-aided external check. Extensive statistical analysis and comparison are presented to verify the performance of our proposed approach and its usability with resource-constrained platforms. Comparing to state-of-the-art point feature-based visual–inertial fusion methods, our approach achieves better robustness under extreme motions or low frame rates, at the expense of slightly lower accuracy in general scenarios. We release our implementation as open-source ROS packages. [ABSTRACT FROM AUTHOR]

Robust optimization is a powerful means to handle optimization problems where there is a set of parameters that are uncertain. The effectiveness of the method is especially noticeable when these parameters are only known to lie inside some uncertainty region. Unfortunately, there are important computational considerations that have prevented the methodology from being fully adopted in fields of practice where the cost function that needs to be robustified is nonlinear with respect to such parameters. In this paper, we propose a new robust optimization formulation that circumvent the computational burden in problems where the cost decomposes as the sum of costs that each involve a single decision variable. This is done by exploiting the fact that in this formulation the worst-case cost function can be expressed as a convex combination between a nominal and an upper-bounding cost function. One can still control the conservatism of the robust solution by adjusting how many terms of this total cost can simultaneously reach their respective most pessimistic value. To demonstrate the potential of this "practicable robust counterpart" formulation, we present how it can be employed for the robust optimization of packet routing on a telecommunication network with congestion. In such problems, an important source of uncertainty stems from the queueing delay, which is usually approximated by a nonlinear convex function using the theory of M/M/1 queues. Computational results on a large number of problem instances of realistic size confirmthat it is possible to identify robust solutions that significantly outperform a deterministic approach in terms of both the amount of congestion and the risks of excessive congestion. Moreover, our proposed method also improves significantly the quality of solutions that are obtained using two other natural approximation schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the singlerun efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments. [ABSTRACT FROM AUTHOR]

For a competitive design of a distillation system, heat integration is currently a necessary component in the chemical industry. In distillation systems with heat integration, the complex interaction among the column design variables and heat integration calls for a systematic optimization method. In this study, the development of a simultaneous optimization framework for distillation systems and heat integration is described. To achieve an accurate and reliable design, rigorous distillation column, heat exchanger and compressor models were used for the optimization. These models were reformulated using a pseudo-transient continuation (PTC) approach to improve their robustness. Because the framework is based on the equation-oriented environment, the automatic or symbolic differentiation is accessible for highly efficient gradient-based optimization algorithms. The results of two optimization cases of distillation systems with heat integration validated the proposed framework. In addition, the application of a bypass efficiency method while optimizing the number of stages is further discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality. [ABSTRACT FROM AUTHOR]

Over the past few years, many crowd related accidents have happened at places of mass gatherings such as religious places, venues of sports, political and social events, and railway stations due to poor crowd management or poor system design. To mitigate the impact of such tragedies, developing adequate crowd management strategies is essential. A critical component of crowd management strategies in such emergencies is an evacuation plan. A proactive measure for evacuation strategies is to make a plan to utilize the available capacity by efficient network redesign. Redesigning the network helps in influencing the demand by enhancing or limiting the use of certain routes, without the use of force or explicit guiding mechanisms. A bi-level network based optimization model is proposed for network redesign. This approach aims at an optimal capacity change for an efficient crowd flow and evacuation during mass gatherings. To determine the optimal capacity change required to achieve the desired evacuation state, an Interior Point Algorithm (IPA) is applied. Further, to increase the computational efficiency, a heuristic is developed that finds a good starting point for IPA. Finally, the robustness of the redesigned network with respect to evacuation demand is examined along with alternate measures that further help in reducing the evacuation time. [ABSTRACT FROM AUTHOR]

A complex optimization criterion that takes into account the integral quadratic error and the stability margin of a robust digital control system for the rectification column used for the butylene-divinyl fraction is proposed. The influence of the weight coefficient, taking into account the ratio of the performance and stability indicators, on the dynamic characteristics of the system is investigated. High system performance and system stability indices over a sufficiently broad range of variation of the parameters of the rectification column are achieved through the use of the proposed optimization criterion and a stability estimation algorithm in the synthesis of a robust digital control system. [ABSTRACT FROM AUTHOR]

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated. [ABSTRACT FROM AUTHOR]

This paper explores the application of optimal design and operational strategies under uncertainty to a transient multiscale catalytic flow reactor system. The catalytic reactor is modeled using a spatially-dependent multiscale model that comprises lattice-based kinetic Monte Carlo (kMC) models coupled with continuum partial differential equations (PDEs) to account for the fine-scale and the macroscale reactor behaviour, respectively. This work compares two uncertainty propagation techniques, power series expansion (PSE) and polynomial chaos expansion (PCE), to assess their performance in multiscale process systems. The analysis reveals that PCE provides accurate results at minimal computational cost for the multiscale catalytic reactor model under the conditions considered in this work. PCE is subsequently used to perform robust dynamic optimization studies on the catalytic reactor system under uncertainty. The first study determines the optimal temperature trajectories that maximize the reactor’s performance under uncertainty. The second study aims to identify the optimal design and operating policies that allow the reactor, under uncertainty in the multiscale model parameters, to meet targeted performance specifications within a level of confidence. Both studies illustrate the benefits of performing dynamic optimization studies to improve performance for multiscale process systems under uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the search and rescue (SAR) in disaster relief through agent-based simulation. We simulate rescue teams' search behaviors with the improved Truncated Lévy walks. Then we propose a cooperative rescue plan based on a distributed auction mechanism, and illustrate it with the case of landslide disaster relief. The simulation is conducted in three scenarios, including "fatal", "serious" and "normal". Compared with the non-cooperative rescue plan, the proposed rescue plan in this paper would increase victims' relative survival probability by 7-15%, increase the ratio of survivors getting rescued by 5.3-12.9%, and decrease the average elapsed time for one site getting rescued by 16.6-21.6%. The robustness analysis shows that search radius can affect the rescue efficiency significantly, while the scope of cooperation cannot. The sensitivity analysis shows that the two parameters, the time limit for completing rescue operations in one buried site and the maximum turning angle for next step, both have a great influence on rescue efficiency, and there exists optimal value for both of them in view of rescue efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional value-at-risk ( CVaR). First, we introduce a model that optimizes the worst-case multivariate CVaR and show that its optimal solution lies on a particular type of stochastic efficient frontier. To solve this model, we develop a finitely convergent delayed cut generation algorithm for finite probability spaces. We also show that the proposed model can be reformulated as a compact linear program under certain assumptions. In addition, for the cut generation problem, which is in general a mixed-integer program, we give a stronger formulation than the existing ones for the equiprobable case. Next, we observe that similar polyhedral enhancements are also useful for a related class of multivariate CVaR-constrained optimization problems that has attracted attention recently. In our computational study, we use a budget allocation application to benchmark our proposed maximin type risk-averse model against its risk-neutral counterpart and a related multivariate CVaR-constrained model. Finally, we illustrate the effectiveness of the proposed solution methods for both classes of models. [ABSTRACT FROM AUTHOR]

Probabilistic robustness evaluation is a promising approach to evolutionary robust optimization; however, high computational time arises. In this paper, we apply this approach to the Bayesian optimization algorithm (BOA) with a view to improving its computational time. To this end, we analyze the Bayesian networks constructed in BOA in order to extract the patterns of non-robust solutions. In each generation, the solutions that match the extracted patterns are detected and then discarded from the process of evaluation; therefore, the computational time in discovering the robust solutions decreases. The experimental results demonstrate that our proposed method reduces computational time, while increasing the robustness of solutions. [ABSTRACT FROM AUTHOR]

In this paper, the researchers present a multi-objective model for multiproduct, multi-site aggregate production planning model in a supply chain. The goals are to minimize the total cost of the supply chain, including inventory costs, manufacturing costs, work force costs, hiring and firing costs, and also to maximize the minimum of suppliers' reliability by considering probabilistic lead times to simultaneously improve the system performance. Since the problem is NP-Hard, a Pareto-based multi-objective harmony search algorithm is proposed. To demonstrate the performance of the presented algorithm, a Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Ranking Genetic Algorithm (NRGA) are applied. The results demonstrate the robustness of the proposed algorithm to probe the Pareto solutions. [ABSTRACT FROM AUTHOR]

In this paper, the solution of large-scale real-time optimization problems of multi-agent systems (MAS) is tackled in a distributed and a cooperative manner without the requirement of exact knowledge of network connectivity. Each agent in the communication network measures a local disagreement cost in addition to its local cost. The agents must work collaboratively to ensure that the system's unknown overall cost (i.e., the sum of the local cost of all the agents) is minimized. In order to minimize this cost, the local disagreement cost of all the agents must first be minimized. This minimization requires the solution of a consensus estimation problem and ensures that the agents reach agreement on their decision variables. To address this challenging problem, a distributed proportional-integral extremum seeking control technique is proposed, one that solves both problems simultaneously. Three simulation examples are included, they demonstrate the effectiveness and robustness of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper investigates an optimal small satellite orbit design problem subject to the atmospheric drag uncertainty, and the small satellite is assumed to have no orbital control systems due to volume and mass constraints. The optimization objective of maximum target observation time is considered, which is constrained by the priority and minimum observation duration of each target site. Given the adverse impacts of atmospheric drag uncertainty on the designed orbit, the optimal solution is expected to maximize observation duration for given target sites while being less sensitive to substantial variations of atmospheric coefficients involved in atmospheric drag modeling. As a result, a robust orbit design approach is proposed by combining the robust optimization model with multi-objective optimization algorithm. Finally, the Monte Carlo simulation results are provided to demonstrate that the robust orbit solutions are more reliable compared to the nominal orbit solution designed from the traditional single-objective stochastic optimization algorithm. [ABSTRACT FROM AUTHOR]