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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]

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]

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]

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]

Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others. [ABSTRACT FROM AUTHOR]

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on. [ABSTRACT FROM AUTHOR]

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper’s main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller. [ABSTRACT FROM AUTHOR]

This paper uses a time-delay dependent H∞ control model to analyze the effect of manufacturing decisions on the process of transmission from resources to capability. We establish a theoretical framework of manufacturing management process based on three terms: resource, manufacturing decision, and capability. Then we build a time-delay H∞ robust control model to analyze the robustness of manufacturing management. With the state feedback controller between manufacturing resources and decision, we find that there is an optimal decision to adjust the process of transmission from resources to capability under uncertain environment. Finally, we provide an example to prove the robustness of this model. [ABSTRACT FROM AUTHOR]

A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction. Under reasonable assumptions, the global convergence of the method is established for unconstrained nonconvex optimization. Numerical results show that the new method is efficient and robust for solving unconstrained optimization problems. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm. [ABSTRACT FROM AUTHOR]

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust. [ABSTRACT FROM AUTHOR]

A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA) for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Wireless sensor network consists of hundreds or thousands of low cost, low power, and self-organizing tiny sensor nodes that are deployed within the sensor network. Sensor network is susceptible to physical attacks due to deprived power and restricted resource capability and is exposed to external environment for transmitting and receiving data. Node capture attack is one of the most menacing attack in the wireless sensor network and may be physically captured by an adversary for extracting confidential information regarding cryptographic keys, node’s unique id, and so forth, from its memory to eliminate the confidentiality and integrity of the wireless links. Node capture attack suffers from severe security breach and tremendous network cost. We propose an empirically designed multiple objectives node capture attack algorithm based on optimization functions as an effective solution against the attacking efficiency of node capture attack. Finding robust assailant optimization-particle swarm optimization and genetic algorithm (FiRAO-PG) consists of multiple objectives: maximum node participation, maximum key participation, and minimum resource expenditure to find optimal nodes using PSO and GA. It will leverage a comprehensive tool to destroy maximum portion of the network realizing cost-effectiveness and higher attacking efficiency. The simulation results manifest that FiRAO-PG can provide higher fraction of compromised traffic than matrix algorithm (MA) so the attacking efficiency of FiRAO-PG is higher. [ABSTRACT FROM AUTHOR]

The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness. [ABSTRACT FROM AUTHOR]

In dynamic multiobjective optimization problems, the environmental parameters change over time, which makes the true pareto fronts shifted. So far, most works of research on dynamic multiobjective optimization methods have concentrated on detecting the changed environment and triggering the population based optimization methods so as to track the moving pareto fronts over time. Yet, in many real-world applications, it is not necessary to find the optimal nondominant solutions in each dynamic environment. To solve this weakness, a novel method called robust pareto-optimal solution over time is proposed. It is in fact to replace the optimal pareto front at each time-varying moment with the series of robust pareto-optimal solutions. This means that each robust solution can fit for more than one time-varying moment. Two metrics, including the average survival time and average robust generational distance, are present to measure the robustness of the robust pareto solution set. Another contribution is to construct the algorithm framework searching for robust pareto-optimal solutions over time based on the survival time. Experimental results indicate that this definition is a more practical and time-saving method of addressing dynamic multiobjective optimization problems changing over time. [ABSTRACT FROM AUTHOR]

Although the original particle swarm optimizer (PSO) method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO). In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO) is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED) learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms. [ABSTRACT FROM AUTHOR]

We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust optimisation approaches were also compared. [ABSTRACT FROM AUTHOR]

This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of robust fault detection filter (FDF) design and optimization is investigated for a class of networked control systems (NCSs) with random delays. The NCSs are modeled as Markovian jump systems (MJSs) by assuming that the random delays obey a Markov chain. Based on the model, an observer-based residual generator is constructed and the corresponding fault detection problem is formulated as an H∞ filtering problem by which the error between the residual signal and the fault is made as small as possible. A sufficient condition for the existence of the desired FDF is derived in terms of linear matrix inequalities (LMIs). Furthermore, to improve the performance of the robust fault detection systems, a time domain optimization approach is proposed. The solution of the optimization problem is given in the form of Moore-Penrose inverse of matrix. A numerical example is provided to illustrate the effectiveness and potential of the proposed approach. [ABSTRACT FROM AUTHOR]

The Fourth Party Logistics (4PL) network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA) and the genetic algorithm (GA) are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design. [ABSTRACT FROM AUTHOR]

Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA) based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP) with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel subband adaptive filter (SAF) for systemidentificationwhere an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function, the proposed l0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed l0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise. [ABSTRACT FROM AUTHOR]

As amajor energy-saving industry, power industry has implemented energy-saving generation dispatching. Apart from security and economy, low carbon will be the most important target in power dispatch mechanisms. In this paper, considering a power system with many thermal power generators which use different petrochemical fuels (such as coal, petroleum, and natural gas) to produce electricity, respectively, we establish a self-scheduling model based on the forecasted locational marginal prices, particularly taking into account CO2 emission constraint, CO2 emission cost, and unit heat value of fuels. Then, we propose a distributionally robust self-scheduling optimizationmodel under uncertainty in both the distribution formand moments of the locational marginal prices, where the knowledge of the prices is solely derived from historical data. We prove that the proposed robust self-scheduling model can be solved to any precision in polynomial time. These arguments are confirmed in a practical example on the IEEE 30 bus test system. [ABSTRACT FROM AUTHOR]

We consider the linear programming problem with uncertainty set described by (ρ, ω)-norm. We suggest that the robust counterpart of this problem is equivalent to a computationally convex optimization problem. We provide probabilistic guarantees on the feasibility of an optimal robust solution when the uncertain coefficients obey independent and identically distributed normal distributions. [ABSTRACT FROM AUTHOR]

This paper develops an adjustable robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, a cell transmission model based network design problem of linear programming type is considered to describe dynamic traffic flows, and a polyhedral uncertainty set is used to characterize the demand uncertainty. The major contribution of this paper is to formulate such an adjustable robust network design problem as a tractable linear programming model and justify the model which is less conservative by comparing its solution performance with the robust solution from the usual robust model. The numerical results using one network from the literature demonstrate the modeling advantage of the adjustable robust optimization and provided strategic managerial insights for enacting capacity expansion policies under demand uncertainty. [ABSTRACT FROM AUTHOR]

Grain transportation plays an important role in many relief and emergency supply chains. In this paper, we take the grain emergency vehicle scheduling model between multiware houses as the research object. Under the emergency environment, the aim of the problem is to maximize the utilization of vehicles and minimize the delay time. The randomness of the key parameters in grain emergency vehicle scheduling, such as time and demand, is determined through statistical analysis and the model is solved through robust optimization method. Besides, we apply the numerical examples for experimental analysis. We compare the robust optimization model with classic model to illustrate the differences and similarities between them. The results show that the uncertainty of both time and demand has great influence on the efficiency of grain emergency vehicle scheduling problem. [ABSTRACT FROM AUTHOR]

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness. [ABSTRACT FROM AUTHOR]

A control law for retarded time-delay systems is considered, concerning infinite closed-loop spectrum assignment. An algebraic method for spectrum assignment is presented with a unique optimization algorithm for minimization of spectral abscissa and effective shaping of the chains of infinitely many closed-loop poles. Uncertainty of plant delays of a certain structure is considered in a sense of a robust simultaneous stabilization. Robust performance is achieved using mixed sensitivity design, which is incorporated into the addressed control law. [ABSTRACT FROM AUTHOR]

The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints' geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers. [ABSTRACT FROM AUTHOR]

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. In this paper, we propose a new conjugacy condition which is similar to Dai-Liao (2001). Based on this condition, the related nonlinear conjugate gradient method is given. With some mild conditions, the given method is globally convergent under the strong Wolfe-Powell line search for general functions. The numerical experiments show that the proposed method is very robust and efficient. [ABSTRACT FROM AUTHOR]

We present a novel linear observer with an extension dealing with polytopic uncertainties in a vehicle dynamic system to identify the side-slip angle. The performance optimization issue is addressed by the minimization of H∞ norm of the system considering the estimation error as an output and the steer angle as an input. Contrary to the standard robust optimal design approaches, we use a convex inner approximation technique to reduce the order of the observer and this enables us to derive suboptimal, fixed-order, and efficiently practicable estimators. Moreover, the numerical examples performed on two-track nonlinear model of the system are provided to illustrate the impacts of design parameters on the optimization results and the efficiency of the technique. [ABSTRACT FROM AUTHOR]

We propose a robust probability classifier model to address classification problems with data uncertainty. A class-conditional probability distributional set is constructed based on the modified χ2-distance. Based on a "linear combination assumption" for the posterior class-conditional probabilities, we consider a classification criterion using the weighted sum of the posterior probabilities. An optimal robust minimax classifier is defined as the one with the minimal worst-case absolute error loss function value over all possible distributions belonging to the constructed distributional set. Based on the conic duality theorem, we show that the resulted optimization problem can be reformulated into a second order cone programming problem which can be efficiently solved by interior algorithms. The robustness of the proposed model can avoid the "overlearning" phenomenon on training sets and thus keep a comparable accuracy on test sets. Numerical experiments validate the effectiveness of the proposed model and further show that it also provides promising results on multiple classification problems. [ABSTRACT FROM AUTHOR]

The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of predictive function control (PFC) for a class of hydraulic vibration servo control systems. Our aim is to design a new advanced control strategy such that the control system can track trajectory in a fast and accurate way. For this end, the mathematical model of the hydraulic vibration servo control system is firstly studied. By analyzing the nonlinear, time-varying, and model structure uncertainty features of the objects, the desired control strategy is presented based on PFC. Finally, the simulation results show that our proposed method is effective and can be used to improve the tracking speed, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable. [ABSTRACT FROM AUTHOR]

We propose doubly constrained robust least-squares constant modulus algorithm (LSCMA) to solve the problem of signal steering vector mismatches via the Bayesian method and worst-case performance optimization, which is based on the mismatches between the actual and presumed steering vectors. The weight vector is iteratively updated with penalty for the worst-case signal steering vector by the partial Taylor-series expansion and Lagrange multiplier method, in which the Lagrange multipliers can be optimally derived and incorporated at each step. A theoretical analysis for our proposed algorithm in terms of complexity cost, convergence performance, and SINR performance is presented in this paper. In contrast to the linearly constrained LSCMA, the proposed algorithm provides better robustness against the signal steering vector mismatches, yields higher signal captive performance, improves greater array output SINR, and has a lower computational cost. The simulation results confirm the superiority of the proposed algorithm on beampattern control and output SINR enhancement. [ABSTRACT FROM AUTHOR]

The problem of network-based robust filtering for stochastic systems with sensor nonlinearity is investigated in this paper. In the network environment, the effects of the sensor saturation, output quantization, and network-induced delay are taken into simultaneous consideration, and the output measurements received in the filter side are incomplete. The random delays are modeled as a linear function of the stochastic variable described by a Bernoulli randombinary distribution. The derived criteria for performance analysis of the filtering-error system and filter design are proposed which can be solved by using convex optimization method. Numerical examples show the effectiveness of the design method. [ABSTRACT FROM AUTHOR]

Considering the load frequency control (LFC) of large-scale power system, a robust distributed model predictive control (RDMPC) is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC) is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI) that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results showgood dynamic response and robustness in the presence of power system dynamic uncertainties. [ABSTRACT FROM AUTHOR]

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. InHSTL algorithm, four strategies (harmonymemory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems. [ABSTRACT FROM AUTHOR]

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH)method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill's position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods. [ABSTRACT FROM AUTHOR]

We study resource-constrained project scheduling problems with perturbation on activity durations. With the consideration of robustness and stability of a schedule, we model the problem as a multiobjective optimization problem. Three objectives-- makespan minimization, robustness maximization, and stability maximization--are simultaneously considered. We propose a hybrid multiobjective evolutionary algorithm (H-MOEA) to solve this problem. In the process of the H-MOEA, the heuristic information is extracted periodically from the obtained nondominated solutions, and a local search procedure based on the accumulated information is incorporated. The results obtained from the computational study show that the proposed approach is feasible and effective for the resource-constrained project scheduling problems with stochastic durations. [ABSTRACT FROM AUTHOR]

In order to improve the synchronous reliability and dependability of complex dynamical networks, methods need to be proposed to enhance the quality and robustness of the synchronization scheme. The present study focuses on the robust fault detection issue within the synchronization for a class of nonlinear dynamical networks composed by identical Lur'e systems. Sufficient conditions in terms of linear matrix inequalities (LMIs) are established to guarantee global robust ...-/...∞ synchronization of the network. Under such a synchronization scheme, the error dynamical system is globally asymptotically stable, the effect of external disturbances is suppressed, and at the same time, the network is sensitive to possible faults based on a mixed ...-/H∞ performance. The fault sensitivity ...- index, moreover, can be optimized via a convex optimization algorithm. The effectiveness and applicability of the analytical results are demonstrated through a network example composed by the Chua's circuit, and it shows that the quality and robustness of synchronization has been greatly enhanced. [ABSTRACT FROM AUTHOR]

Researchers often identify robust design, based on the concept of building quality into products or processes, as one of the most important systems engineering design concepts for quality improvement and process optimization. Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time-insensitive. In pharmaceutical manufacturing processes, time-oriented quality characteristics, such as the degradation of a drug, are often of interest. As a result, current robust design models for quality improvement which have been studied in the literature may not be effective in finding robust design solutions. In this paper, we show how the robust design concepts can be applied to the pharmaceutical production research and development by proposing experimental and optimization models which should be able to handle the time-oriented characteristics. This is perhaps the first attempt in the robust design field. An example is given, and comparative studies are discussed for model verification. [ABSTRACT FROM AUTHOR]

This paper proposes a robust design optimization methodology under design uncertainties of an aerospace vehicle propulsion system. The approach consists of 3D geometric design coupled with complex internal ballistics, hybrid optimization, worst-case deviation, and efficient statistical approach. The uncertainties are propagated through worst-case deviation using first-order orthogonal design matrices. The robustness assessment is measured using the framework of mean-variance and percentile difference approach. A parametric sensitivity analysis is carried out to analyze the effects of design variables variation on performance parameters. A hybrid simulated annealing and pattern search approach is used as an optimizer. The results show the objective function of optimizing the mean performance and minimizing the variation of performance parameters in terms of thrust ratio and total impulse could be achieved while adhering to the system constraints. [ABSTRACT FROM AUTHOR]

This article focuses on the use of genetic algorithms in developing an efficient optimum design method for tilting-pad bearings. The approach optimizes based on minimum film thickness, power loss, maximum film temperature, and a global objective. Results for a five tilting-pad preloaded bearing are presented to provide a comparison with more traditional optimum design methods such as the gradient-based global criterion method, and also to provide insight into the potential of genetic algorithms in the design of rotor bearings. Genetic algorithms are efficient search techniques based on the idea of natural selection and genetics. These robust methods have gained recognition as general problem solving techniques in many applications. [ABSTRACT FROM AUTHOR]