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• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

This paper aims to determine a time-varying dilution rate of the feed medium which maximizes mean productivity while minimizing both system sensitivity and control cost in glycerol continuous culture. A multi-objective optimal control problem subjected to a nonlinear control system and its auxiliary system is proposed and transformed into a finite-dimensional large-scale multi-objective optimization problem via discretization and time-scaling transformation. A multi-objective competitive swarm optimization algorithm is constructed to solve this transformed problem based on pairwise competition, mutation operation and environment selection. The Pareto front obtained shows the distribution of the optimal target value which indicates the rationality and feasibility of the control system under the approximate optimal strategy. Numerical simulations verify the robustness of the optimal control system and the effectiveness of our numerical solution method. The value of IGD shows the good accuracy and distribution of the proposed multi-objective optimization algorithm. • We propose a multi-objective optimal control problem of glycerol continuous culture. • The multi-objective optimal control problem is approximated by a parameter optimization problem. • A multi-objective competitive swarm optimization algorithm is developed. • Numerical experiments show the effectiveness and applicability of the approach proposed. [ABSTRACT FROM AUTHOR]

• A novel concept of power-to-gas (P2G)-based virtual power plant (GVPP) is designed. • Power-gas-power effect of GVPP brought by P2G and gas storage tank is discussed. • A multi-objective flexible risk aversion model for GVPP operation is proposed. • Payoff table of optimization model is applied for multiple iterative weighting method. • Different operation cases for verifying the proposed operation strategy are defined. Power-to-gas (P2G) provides a new means for accommodating abandoned new energy that will support the optimal operation of virtual power plants (VPPs) in the future. In this study, a novel structure of a P2G-based virtual power plant (GVPP) is designed. A flexible risk aversion model for GVPP operation is proposed with two objectives—maximum operation profit and minimum operation risk. In the model, the conditional value at risk method and robust optimization theory are utilized to reflect uncertainty risks. To solve the multi-objective model, a solution algorithm was constructed for iteratively obtaining the optimal weight coefficient for transforming the multi-objective model into a single-objective model based on a payoff table and a rough set method. Four simulation cases were set for comparative analysis based on a nine-node energy hub system. The results show the following outcomes: (1) GVPP realizes the complementary utilization of distributed energy and forms a power-gas-power recycling mode, (2) the proposed model can provide an effective decision-making tool for different risk-attitude decision makers by setting a reasonable confidence level and robust coefficient, (3) P2G and price-based demand response (PBDR) improve the grid-connected space of clean energy, in particular, PBDR improves the flexibility of system operation and reduces operation risk, and (4) if the maximum emission trade allowance (META) is considered, P2G preferentially converts carbon dioxide into methane when the META is lower, while clean energy is preferentially used to satisfy load demand and the methane produced by P2G can be sold to the natural gas network when the META is higher. Overall, the proposed optimal decision model achieves the maximum utilization of clean energy to obtain higher economic benefits while rationally controlling operation risks; hence, providing reliable support for decision makers. [ABSTRACT FROM AUTHOR]

• Studying service network design considering multiple types of services. • Proposing both deterministic and two-stage robust optimization formulations. • Implementing a column-and-constraint generation approach to solve the robust model. • The proposed algorithm outperforms the Benders decomposition method. • The diversification of service types reduces total cost and the number of vehicles. Integrated transportation systems providing multiple types of services are attracting increasing attention due to the diversification of demands. In this paper, we propose both deterministic and two-stage robust optimization formulations for the service network design problem considering multiple types of services. A probability-free uncertainty set is employed to deal with demand uncertainty in the proposed robust model. We implement a column-and-constraint generation approach to solve the robust model, which outperforms the Benders decomposition method. Comparative results indicate that it helps reduce the total cost and the number of vehicles to consider multiple service types in the network. [ABSTRACT FROM AUTHOR]

Highlights • A robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a base matrix with fast implementation. • Designed a robust structured sparse sensing matrix by minimizing the mutual coherence and a surrogate function for the sparse representation error. • Proposed an alternating minimization algorithm for solving the corresponding optimization problem. • Provided global sequence convergence analysis for the proposed algorithm. Abstract We consider designing a robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a dense base matrix for capturing signals efficiently. We design the robust structured sparse sensing matrix through minimizing the distance between the Gram matrix of the equivalent dictionary and the target Gram of matrix holding small mutual coherence. Moreover, a regularization is added to enforce the robustness of the optimized structured sparse sensing matrix to the sparse representation error (SRE) of signals of interests. An alternating minimization algorithm with global sequence convergence is proposed for solving the corresponding optimization problem. Numerical experiments on synthetic data and natural images show that the obtained structured sensing matrix results in a higher signal reconstruction than a random dense sensing matrix. [ABSTRACT FROM AUTHOR]

Highlights • We introduced an additional objective function of the ability of peak load regulation to deal with the reverse peak load regulation characteristic of wind power; • The novelty of the paper is to propose a novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers. • This paper combined Constraint-and-Column Generation and Normalized Normal Constraint to deal with the proposed multi-objective robust optimization problem. • The widely-used IEEE 118-bus test system is used to verify the proposed robust optimization model. Abstract With the increasing applications of wind energy & power technologies, the stochastic fluctuation and reverse peak load regulation characteristic of wind power brings great challenges to unit commitment of power systems. It is difficult to meet the demand of the peak load regulation with the wind power integration for the existing robust unit commitment models when only considering the cost. In order to solve this problem, an additional objective function of the ability of peak load regulation is constructed for unit commitment solutions. As a result, a novel multi-objective robust unit commitment model considering both the cost and peak load regulation is proposed. Moreover, probabilistic scenario analysis is applied to constraint the model of the fluctuant wind power, and temporal correlation of wind powers is also considered to further improve the economy in the long run. In order to deal with multi-objective problem for the robust optimization solutions, the above-mentioned problem is decomposed into two stages, i.e., a main problem and a subproblem. The main problem is to determine unit commitment and the subproblem is a "min–max optimization" one to obtain the unit outputs. Finally, the simulation analysis based on IEEE118 node system verified the robustness and the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method. Highlights • New time-constrained sparse representation method is put forward. • Novel robust characteristic dimensionality reduction method is proposed. • Online fault detection algorithm is developed and fault detectability is analyzed. • Experimental studies on TEP and EMU braking system validate the proposed methods. [ABSTRACT FROM AUTHOR]

Highlights • A robust PTC RD model based on equilibrium stage was developed for the first time. • Optimization of RD systems using the RD PTC model was robust and fast. • An RDWC was optimized using equation-oriented algorithm for the first time. • The bypass efficiency method was applied to optimize RD systems for the first time. • Three intrinsic properties of bypass efficiency method were introduced in this work. Abstract Reactive distillation has significant economic and environmental advantages compared to conventional processes. However, the optimal design of a reactive distillation system is far from mature. This work proposed a novel equation-oriented optimization framework based on a rigorous equilibrium stage model for the optimization of reactive distillation systems. A pseudo-transient continuation (PTC) reactive distillation model was developed to solve the convergence problem of simulations. The bypass efficiency method was incorporated into the PTC model to avoid the intractable mixed integer optimization. The developed model can be integrated well with a steady-state optimization algorithm assisted by PTC models. Two cases demonstrated that the proposed model and optimization framework were robust and fast. However, the bypass efficiency method caused physically meaningless solutions, but they were found to be convertible to physically meaningful solutions without influencing the accurate optimization of number of stages according to the intrinsic properties of the bypass efficiency method introduced in the current work. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A preventive crude oil scheduling method to handle ±10% demand fluctuation is developed. • The scheduling is performed to maximize the profit in single objective optimization. • Inter-period fluctuations in feed rate of crude distillation unit is additionally minimized in multi-objective optimization. • Four industrial-scale examples with time horizon of 3, 7, 14 and 20 days are solved. Abstract Crude oil supply about 33% of the total energy consumption worldwide and is predominantly (>50%) processed in marine access refineries. Therefore, crude oil scheduling which enables the efficient processing of the crude to increase the profitability is an important problem. It often becomes challenging due to the presence of combinatorial constraints, discrete variables and uncertainties. This study addresses the crude oil scheduling under commonly present demand uncertainty. A proactive two-stage approach has been developed to solve such optimization problems. A discrete-time model is used with structure adapted genetic algorithm (SAGA) for solving single- and multi-objective scheduling optimizations. In the first stage, an initial schedule is generated with the nominal parameters provided a priory. The same schedule is then checked and accepted in the second stage only if it is robust with respect to demand uncertainty. This method is applied to four different industrial size problems with scheduling horizon of 3, 7, 14 and 20 days. The objective function in the single objective formulation is the maximization of profit. However, in multi-objective optimization, an additional objective of minimization of fluctuation in crude oil supply to crude distillation units is used as it provides a better control and operability of the plant. The proposed method is successfully implemented for the above four different crude oil scheduling problems to generate the robust schedule. [ABSTRACT FROM AUTHOR]

Abstract A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the H ∞ , H 2 or H 1 sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN. [ABSTRACT FROM AUTHOR]

Highlights • Multimodal biometric system based on Iris, finger vein and fingerprint. • Performance optimization using Backtracking Search Optimization (BSA). • Optimal score level fusion model resolves conflicts among classifier scores. • The results exhibits low equal error rate and high accuracy. Abstract Multimodal biometric systems fuse information from multiple modalities to overcome limitations of individual classifiers. Score level fusion of multiple classifiers can effectively combine information from different modalities. However, most of the multimodal biometric systems are impaired by conflicting classifier scores under dynamic environment, which results in degradation in system's robustness and reliability. To address this, we propose a multimodal biometric system based on an optimal score level fusion model. The key idea of this work is to optimally integrate three complementary biometric traits namely iris, finger vein and fingerprint. For this, individual classifier performance is optimized using evolutionary Backtracking Search Optimization Algorithm (BSA). In addition, conflicting beliefs from individual classifiers are resolved using proportional conflict redistribution rules (PCR-6) to obtain a concurrent solution. The system exhibits optimal behaviour under dynamic environment through boosting or suppression of concurrent classifiers and resolving conflicts among discordant classifiers. The proposed biometric system is evaluated over chimeric multimodal datasets created from benchmark images. On an average, we achieve an accuracy of 98.43% and an EER of 1.57%. The proposed biometric system not only outperforms state-of-the-art techniques but also shows directions towards development of an expert multimodal biometric system. [ABSTRACT FROM AUTHOR]

Highlights • OMA modal identification through ambient vibration testing and FEM modelization of a historic centennial reinforced concrete bridge. • Automated Latin Hypercube Sampling implementation. • FEM model parametrization based on Sensitivity Analysis with analytical derivatives. • Global optimization procedure for model updating. • Effective estimation of modal bridge characteristics after model updating. Abstract In this paper, a self-implemented model updating global optimization procedure is successfully applied to a remarkable case study concerning a historic centennial Reinforced Concrete (RC) bridge with parabolic arches, based on recorded experimental vibrational data and arising identification of modal properties. In order to boost the degree of confidence and robustness of the developed model updating procedure, appropriate computational strategies are proposed at the level of both Sensitivity Analysis (SA) and global optimization. In particular, Latin Hypercube Sampling (LHS) is employed in drawing up both strategies, as a systematic automated way to determine appropriate multi-start sets of initiation points, optimally distributed throughout the parametric domain. The procedure involves a gradient-based method and proposes an interaction algorithm between mechanical FEM solver and numerical computing environment. Moreover, the gradient of the objective function involved in the model updating is analytically derived, instead of by often-used Finite Differences (FD), toward better accuracy and computational efficiency. Comprehensive updating results starting from a first FEM base model are achieved, for the considered case study, and show that the relative eigenfrequency and mode shape estimations are considerably improved, for all the structural modes accounted for within the updating process, with a very good final matching between experimentally extracted and FEM modelled modal properties. [ABSTRACT FROM AUTHOR]

Highlights • Analytical solution of flexural-torsional buckling of arches with shear deformation. • Neglect of shear deformations causes a higher buckling load for arches under radial load alone. • Neglect of shear deformations causes a lower critical temperature for arches under temperature field alone. • Flexural-torsional buckling load decreases with increase of temperature field. • Critical temperature field decreases with increase of radial point load. Abstract This paper presents an analytical investigation of the thermoelastic lateral-torsional buckling of circular arches under an arbitrary radial point load in a uniform thermal environment accounting for shear deformations, which has not been reported in the literature. The interaction of thermal environment with the arbitrary load and elastic end restraints produces complex non-uniform compressive, bending, and shearing actions in the arch. The theoretical solutions of the critical load and temperature field for the elastic lateral-torsional buckling of arches accounting for shear deformations are derived. Comparisons of theoretical solutions with finite element results show that their agreements are excellent. Effects of various factors on the lateral-torsional buckling are investigated. An increase of the temperature field reduces the buckling load, while an increase of the radial load reduced the critical temperature field for buckling. The effect of shear deformations on the lateral-torsional buckling load is also explored. It is shown that the neglect of shear deformations causes errors in the prediction of the critical buckling load. When shear deformations are considered, the buckling load for arches with a small and intermediate lateral slenderness ratio under the radial point load alone is shown to be lower than that without consideration of shear deformations. However, under the uniform temperature field alone, the critical temperature field for lateral-torsional buckling of the arches considering shear deformations is higher than that neglecting shear deformations. [ABSTRACT FROM AUTHOR]

Highlights • A generic framework for structural design optimization is presented. • The framework is combined with a life safety acceptance criterion. • Optimal and acceptable reliabilities for structural design are derived. • The application for component and system level design is discussed. • A clear relation to structural robustness is established. Abstract A common approach to define criteria to reliability performance of structures in the development of semi-probabilistic design codes and for probabilistic design of individual structures is to make use of the tentative target reliabilities provided in the JCSS Probabilistic Model Code. An acceptable level of life safety is, however, not guaranteed when applying the target reliabilities provided by the JCSS, as these have been derived based on monetary optimization. In the present paper, the underlying generic framework for structural design optimization is reviewed and extended for the derivation of life safety related target reliabilities based on the marginal lifesaving costs principle. The resulting minimum acceptable reliabilities may be used in combination with the JCSS target reliabilities to achieve designs that are both cost-efficient and consistent with societal preferences for lifesaving investments. The framework presented in this paper forms the basis for the target reliabilities defined in ISO 2394:2015, where the target reliabilities proposed by the JCSS have been combined with minimum acceptable reliabilities to ensure societal acceptability in terms of life safety. An extended discussion of the general framework and its underlying assumptions includes considerations about the application of the target reliabilities at component or system level, accounting for structural robustness, as well as implications for the interpretation of the main variables entering the framework. [ABSTRACT FROM AUTHOR]

Cascading failure is an ubiquitous phenomenon in many types of complex networks, and it has attracted significant concerns in the past decade. In this paper, we consider a generic transmission network consisting of two kinds of nodes, i.e., hosts and routers, and only the hosts can generate and receive packets. In such a host–router network model, the network robustness against cascading failure is closely related to the hosts’ locations. We employ the memetic algorithm (MA) to find a near-optimal way to locate the hosts, which remarkably improves the network robustness compared with three degree-based methods. Furthermore, we investigate the optimal configuration of the hosts given by the MA algorithm and put forward a faster method to select the hosts. This work provides a start point for systematically analyzing and optimizing network robustness of complex networks with heterogeneous node functions. [ABSTRACT FROM AUTHOR]

Abstract We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues. Highlights • A comprehensive scenario-based robust approach embedded in the rolling horizon framework. • Multi-objective mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran. • Restorative strategies to increase the efficiency and robustness of the relief network under disaster. • An efficient solution algorithm for the two-phase rolling horizon robust optimization approach. [ABSTRACT FROM AUTHOR]

Abstract The increasing amount of researches have proven the advantages of fractional order PID controllers. For fractional order PI (FO-PI) controller, the classical tuning approach is based on specifying a certain gain crossover frequency, a phase margin and a robustness to plants gain variations, it can be implemented by applying a series of open-loop experiments without an accurate process model. Additionally, the performance optimization is a widely used auto-tuning method, it searches online for the controller parameters that minimize a cost function which quantifies the closed-loop performance. Based on extremum seeking (ES) algorithm, a novel indirect optimization scheme for tuning a FO-PI controller which combines the regular ES optimization with the classical tuning approach is proposed in this paper. Instead of optimizing the controller parameters directly, we parametrize all the controllers leading to the given performance specifications (cross-over freq, phase margin, iso-damping) and then do the optimization of the cross-over freq and phase margin to achieve the best step-response performance. Simulation and experimental results demonstrate that the proposed ES optimization scheme can achieve a great balance between robustness and performance. [ABSTRACT FROM AUTHOR]

Highlights • An enhancement to Bag-of-Words (BoW) is proposed to produce robustness against random initialization and optimal model-order. • Robustness against random initialization is realized by treating performance as a random variable and estimating its limit. • The optimal model order is estimated such that the expected value of the performance is maximized. • The suggested BoW enhancement is applied to the Caltech 101 image set classification and excellent performance is obtained. • Comparison is conducted with the available state-of-the-art techniques and the superiority of the ESRO-BOW is demonstrated. Abstract This article proposes an Enhanced Stochastically Robust and Optimized Bag-of-Words (ESRO-BoW) modeling technique that simultaneously accounts for the problems of robustness against random initialization and optimal model-order selection in BoW modeling. To address the aforementioned problems, the modeling performance of multiple executions of the BoW technique is considered as a discrete random variable and the ESRO-BoW is developed such that a convergence in mean is guaranteed for the resulting sequence of random variables. The BoW model order is tuned such that the expected value of the limit of the random variable of the classification performance is maximized. Hence, the ESRO-BoW realizes both robustness against random initializations and selects the optimal BoW model order. In order to evaluate its efficiency, the ESRO-BoW is applied to the classification of Caltech 101 image set and excellent performance is obtained. Comparison with the state-of-the-art approaches, employed for classifying Caltech 101 image set, demonstrates the superiority of the suggested ESRO-BoW modeling technique. [ABSTRACT FROM AUTHOR]

Abstract To further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously. The objective problem of the proposed method can be solved by an iterative optimization algorithm which is proved to converge at a local maximum point. The experimental results on FERET face database, PolyU palmprint database and Binary Alphadigits database illustrate that the proposed method outperforms three conventional 2DLDA methods when there are outliers. [ABSTRACT FROM AUTHOR]

Abstract Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the ℓ 2 -norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the ℓ 1 -norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the ℓ 1 -norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the ℓ 1 -norm. The ℓ 1 -norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method. [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]

Classical adjoint-based optimization approach for the optimal control of partial differential equations is known to require a large amount of CPU time and memory storage. In this article, in order to reduce these requirements, a posteriori and a priori model order reduction techniques such as POD (Proper Orthogonal Decomposition) and PGD (Proper Generalized Decomposition) are used. As a matter of fact, these techniques allows a fast access to the temporal dynamics of a solution approximated in a suitable subspace of low dimension, spanned by a set of basis functions that form a reduced basis. The costly high fidelity model is then projected onto this basis and results in a system of ordinary differential equations which can be solved in quasi-real time. A disadvantage of considering a fixed POD basis in a suboptimal control loop, is basically the dependence of such bases on a posteriori information coming from high fidelity simulations. Therefore, a non robustness of the POD basis can be expected for certain perturbations in the original parameter for which it was built. As a result, update the reduced bases with respect to each variation in the control parameter using the POD method is still costly. To get over this difficulty, we equip the usual reduced optimal control algorithm with an intermediate basis adaptation step. The first proposed approach consists in adapting the reduced basis for a new control parameter by interpolating over a set of POD bases previously computed for a range of control parameters. To achieve that, an interpolation technique based on properties of the tangent subspace of the Grassmann manifold (ITSGM) is considered. The second approach is the PGD method, which by nature, enrich a space time decomposition trying to enhance the approximation by learning from its own errors. Relaying on this property, this method is employed in the control loop as a basis corrector, in such a way the given spatial basis is adapted for the new control parameter by performing just few enrichments. These two approaches are applied in the sub-control of the two dimensional non-linear reaction-diffusion equations and Burgers equations. [ABSTRACT FROM AUTHOR]

We present a novel dual-mode MPC scheme that significantly reduces the computational effort of robust MPC (RMPC). Specifically, we propose a method for the computation of a large set C on which no optimal control problem (OCP) needs to be solved online. The method is motivated by the trivial observation that, for classical MPC, no optimization is required for the states in the terminal set T , because the unconstrained linear–quadratic regulator is optimal there. While this observation cannot be directly transferred to RMPC, we show that suitable sets C exist in the neighborhood of T and state an algorithm for their computation. We stress that the resulting sets C are significantly larger than robust positively invariant sets that are typically exploited in RMPC and on which it is well-known that no OCP needs to be solved online. The approach is illustrated with three examples for which we observe a reduction of the numerical effort between 22.36% and 95.60%. [ABSTRACT FROM AUTHOR]

Highlights • A two-fold understanding of Rényi entropy PCA is given. • The bridge between Rényi entropy PCA, kernel learning and graph embedding is built. • A generalized graph embedding framework is proposed. • The framework explores high order statistics of data in dimensionality reduction. Abstract In this paper, we study the connections between Rényi entropy PCA, kernel learning and graph embedding. A natural complementary formulation of maximum entropy PCA, namely minimum error entropy PCA, is presented. These two formulations can be combined together to give a two-fold understanding of Rényi entropy PCA. Further, we establish connections between Rényi entropy PCA, kernel learning and graph embedding, and propose a generalized graph embedding framework that unifies a variety of existing algorithms. This proposed framework essentially covers previous graph embedding framework, and partially answers the problem of how to make use of high order statistics of data in dimensionality reduction. The theoretic development enables a close relationship between information theoretic learning, kernel learning and graph embedding. [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]

We explore how to overcome phase damping decoherence when imperfect projective measurements are available. It is demonstrated that a “relaxed” control goal may be achieved by combining simple open-loop coherent control with periodic imperfect projective measurements. Inspired by the idea of soft optimization, we propose to control the state of a qubit staying near a reference pure state with high probability for a sufficiently long time. This “relaxed” control goal is expressed in terms of three-parameter performance indexes, and it is in remarked contrast to the “hard” requirement that the state of the controlled qubit always stays at the reference pure state. We not only establish necessary conditions for relaxed robust problems, but also present sufficient conditions for the existence of a solution to the relaxed robust problems. With the help of main results, one can estimate how the maximal total time depends on the target state and the perturbation bounds. Furthermore, it is demonstrated that imperfect realization of projective measurements will worsen the three-parameter performance indexes. [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]

In recent years, there have been several L1-norm-based linear projection methods and max–min-based dimensionality reduction methods, which show robustness to outliers and noises and show large margin for discrimination. In this paper, we propose L1-norm-based max–min large margin (MLM-L1) for linear projection-based dimensionality reduction. It makes use of the robustness of L1-norm to outliers and noises and the max–min idea for large margin. A non-greedy iterative algorithm (NMLM-L1) is proposed to solve the optimization problem of the proposed MLM-L1. Experiments on several face image databases show that the proposed method has better classification performance than its closely related methods. [ABSTRACT FROM AUTHOR]

Interdependent networks, where two networks depend on each other, are becoming more and more significant in modern systems. From previous work, it can be concluded that interdependent networks are more vulnerable than a single network. The robustness in interdependent networks deserves special attention. In this paper, we propose a metric of robustness from a new perspective—the balance. First, we define the balance-coefficient of the interdependent system. Based on precise analysis and derivation, we prove some significant theories and provide an efficient algorithm to compute the balance-coefficient. Finally, we propose an optimal solution to reduce the balance-coefficient to enhance the robustness of the given system. Comprehensive experiments confirm the efficiency of our algorithms. [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]

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network’s degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability. [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]

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]

Batch-end quality prediction is of paramount importance in industries producing high added-value products. However, all available methods hereto only use raw data at its native resolution. This paper proposes a new multi-resolution quality prediction (MRQP) methodology for batch-end quality, which exploits structured correlation in both the time and variables dimensions. The implementation of this methodology results in a more parsimonious and robust model structure, with a predictive performance bounded to be at least as good as the current standard approach. The implementation of MRQP is illustrated in three different case studies, where improvements over the standard single-resolution approach were found to be in the range of 10%–50%. From an interpretation standpoint, multi-resolution models are more robust with respect to the selection of too many predictors, facilitating the identification of key process variables, and providing information on the process time scales that influence final product quality, which can be further exploited for diagnosis, control, and optimization. [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]

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]

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]

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]

In multi-channel wireless networks, a fundamental problem is to find node-disjoint paths minimising global cost or the maximum individual path cost, under the constraint that each path operates on a separate channel to maximise the fault tolerance and robustness against channel instability and malicious attacks. Meanwhile, the quality of service (QoS) requirement (e.g., in terms of end-to-end delay) needs to be satisfied on each path. In this paper, we provide a comprehensive formulation and analysis on this multi-path optimization problem by casting it to the problem k -disjoint path with different colours ( k -DPDC). We further formulate the Restricted MinSum k -DPDC and Restricted MinMax k -DPDC to denote the problems of finding multiple node- and channel-disjoint paths minimising the global cost and the maximum individual path cost under the QoS constraint on the path end-to-end delay. Given the NP-hardness of both problems, we focus on directed acyclic graphs and propose fully polynomial-time approximation algorithms for both problems. [ABSTRACT FROM AUTHOR]

This paper aims to take a step forward to enhance the performance of the optimization kernel of electronic design automation (EDA) tools by coping with the existing challenges in the analog circuit sizing problems. For this purpose, a novel co-evolutionary-based optimization approach, called Co-AGSA, is proposed. In the Co-AGSA, a self-adaptive penalty technique based on the concept of the co-evolution model is incorporated into a powerful optimization algorithm, named advanced gravitational search algorithm (AGSA), to efficiently solve more realistic constrained optimization problems. The performance of the Co-AGSA approach is first evaluated by solving three constrained engineering design problems. Then, the optimization capability of the Co-AGSA-based IC sizing tool is validated using three different case studies, i.e., a two-stage op-amp, a folded-cascode op-amp and a two-stage telescopic cascode amplifier, to show the applicability of the proposed approach. The results demonstrate that the Co-AGSA gives better performance compared to other approaches in terms of efficiency, accuracy and robustness. [ABSTRACT FROM AUTHOR]

For non-probabilistic reliability-based design optimization (NRBDO), existing methods cannot satisfy both efficiency and robustness requirements, especially for complex engineering problems. In this study, an efficient adaptive-loop (EAL) method for NRBDO is proposed based on an improved single loop method and enhanced chaos control method, aiming to enhance the optimization efficiency without sacrifice of numerical stability. The constraint function is reconstructed to improve the efficiency of reliability analysis. Moreover, a criteria condition is defined to guarantee the rationality of rough optimum and eliminate redundant constraints. The proposed EAL method can adaptively switch to a proper method, thus both high efficiency and robustness can be guaranteed. Nonlinear numerical examples and complex engineering problems are utilized to demonstrate the effectiveness of the proposed method by comparison with other existing methods. [ABSTRACT FROM AUTHOR]

We analyze the inherent robustness properties of an economic NMPC formulation in which the controller trades off rate of convergence and economic performance. We show that this controller is input-to-state practically stable under reasonable assumptions. Our formulation does not require dissipativity with respect to the stage costs being optimized, as is required by existing economic MPC formulations. Instead, our formulation enforces dissipation in the form of a Lyapunov inequality that is constructed by using traditional tracking cost terms. Consequently, the proposed approach can be applied to a wider range of systems. We also demonstrate that the controller provides high flexibility to optimize economic performance and remains robust in the face of disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel absolute value inequalities discriminant analysis (AVIDA) criterion for supervised dimensionality reduction. Compared with the conventional linear discriminant analysis (LDA), the main characteristics of our AVIDA are robustness and sparseness. By reformulating the generalized eigenvalue problem in LDA to a related SVM-type “concave-convex” problem based on absolute value inequalities loss, our AVIDA is not only more robust to outliers and noises, but also avoids the SSS problem. Moreover, the additional L1-norm regularization term in the objective makes sure sparse discriminant vectors are obtained. A successive linear algorithm is employed to solve the proposed optimization problem, where a series of linear programs are solved. The superiority of our AVIDA is supported by experimental results on artificial examples as well as benchmark image databases. [ABSTRACT FROM AUTHOR]