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The article discusses the result of an experimental study which shows that people do not follow the optimal strategy in practice and suggests two strategies for defeating human opponents in rock-paper-scissors (RPS). The study found that the probability of repeating a game on the next trial is well above one-third, while the strategy of always playing the symbol is assessed to be an effective strategy in defeating the symbol that the other player has previously played. The study also observed that the wins of each competition have increased by 26.4%, while the losses are decreased by 9.51%.

Abstract: In this paper, we determine the modularities of asymptotically optimal towers found by Maharaj and Wulftange in 2005. This is also a continuation of the work of Elkies in 2002. [Copyright &y& Elsevier]

Abstract: A generalization of two sharp inequalities in a recent paper by N. Ujević is established. Applications in numerical integration are also given and the results of N. Ujević are revised and improved. [Copyright &y& Elsevier]

This paper intends to design an integral backstepping Lyapunov redesign controller (IBLRC) for various uncertain strict‐feedback‐form nonlinear systems. The pros and cons of backstepping Lyapunov redesign resulted in the design idea. An integral term is incorporated in conventional backstepping with Lyapunov redesign to indirectly decrease chattering and steady‐state error of a reference signal tracking when unknown static parameters and matched and unmatched dynamic uncertainties exist. The closed‐loop system is mathematically confirmed as stable in a Lyapunov frame, and we ultimately reach uniformly semi‐global boundedness of all signals. Simulation results of the proposed IBLRC are juxtaposed with those of the classic backstepping with the Lyapunov redesign. The proposed IBLRC realizes excellent tracking and enhanced robust performance than the BLRC for time‐varying and step tracking references. [ABSTRACT FROM AUTHOR]

Compared with the conventional two-level dual-active-bridge (DAB) dc–dc converter, the three-level DAB converter could operate with lower voltage rated switching devices and provide more control degrees of freedom, which enhances the control flexibility. This paper proposes a modulation strategy with five control degrees of freedom. In the previous work with multiple control degrees, the optimization strategies for efficiency or inductor current minimization are investigated; however, these strategies all need detailed mode analysis and complex mathematical optimization tools to obtain the optimal duty cycles. In this paper, an optimization strategy for the root-mean-square value of the inductor current is proposed. This method extracts the fundamental and third-harmonic components from the high-frequency voltages on both sides and adjusts the amplitude of the bridge voltages by the vector diagram to obtain the optimal duty cycles. The inductor current can be reduced significantly at light loads with the proposed strategy, so the efficiency of the converter can be improved. Moreover, the optimization process does not need the detailed operation analysis of the converter, nor does it require the complex mathematical optimization tools. Finally, the performance of the three-level DAB and the optimization strategy are verified by the experimental results. [ABSTRACT FROM AUTHOR]

Multi-product pipelines usually transport several products in batches to respective delivery stations. As for a multi-product pipeline with multiple pump stations, this paper develops a continuous-time mixed-integer linear programming (MILP) model based on flowrate database to optimize its detailed scheduling. In the proposed model, various unit pump cost and flowrate constraints, which strongly depend on pump operation schemes, are introduced for the economy and safety of solved scheduling plans. Moreover, this paper considers the actual field processing constraints which vary with batch interface migration and rarely considered in previous work. And a novel method of historical flowrate database preprocessing is presented to enhance solving efficiency. Finally, through comparing with three real-world cases solved by another two available models, the proposed one performs the best in scheduling optimization as well as substantial reduction of pump cost. [ABSTRACT FROM AUTHOR]

This paper presents an optimization model of inspection and replacement planning for a refinery plant under the consideration of corrosion in terms of cost. The management of corrosion is an essential task for processes that operate over several years without a shutdown. This is because corrosion can cause severe failures by thinning the wall thickness and eventually cause pipes or equipment to burst. However, required safety measures, such as the corrosion management, involve costly inspection and replacement. Therefore, a cost-effective safety-action strategy is proposed in this paper. The developed model presents an optimal combination of steel grade, design wall thickness, inspection number, and inspection timing under a given corrosion rate to minimize the cost of design, inspection, replacement, and failure. Three case studies using sensitivity analyses are applied to three major processes in a refinery plant: a crude distillation unit, visbreaker, and hydrocracker. [ABSTRACT FROM AUTHOR]

This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance maximizing the revenue stream. This is done by adjusting control inputs like choke valves, artificial lift parameters and routing of well streams. It is well known that the daily production optimization problem is well suited for mathematical optimization. The contribution of this paper is a discussion on appropriate formulations, in particular the use of static models vs. dynamic models. We argue that many important problems can indeed be solved by repetitive use of static models while some problems, in particular related to shale gas systems, require dynamic models to capture key process characteristics. The reason for this is how reservoir dynamics interacts with the dynamics of the production system. [ABSTRACT FROM AUTHOR]

This paper studies the optimality in the problem of cyclic harvesting of a resource distributed on a circle with a certain prescribed density. The velocity ofmotion of the collecting device and the fraction of the resource harvested at a given time play the role of control. The problem is to choose a control maximizing a given quality functional. The paper presents the maximum principle for this (infinite-dimensional) problem. The maximum principle can be written as two inequalities which can be conveniently verified. The class of problems with a concave profit function is solved completely. At the end of the paper, several examples are considered to illustrate the developed technique. [ABSTRACT FROM AUTHOR]

The critical data we share through computer network gets stolen by unethical means. This unethical way of accessing one's data without proper authentication becomes intrusion. To solve this issue, in this paper we propose a new network intrusion detection method, Multi-Class Classification Multiple Criteria Linear Programming (MCC-MCLP) model. MCLP is a mathematical classification technique that is used widely to solve real-time data mining problems. So far, the literature discusses only about binary classification MCLP. But in this paper we propose a Multi-Class Classification MCLP model. We use PSO for fine-tuning the parameters of MCC-MCLP. KDD CUP 99 data set is used for performance evaluation of the proposed method. Our MCC-MCLP method classifies the data better and helps in fine-tuning the parameters with the help of PSO. The results clearly show that the proposed model performs better in terms of detection rate, false alarm rate and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an improved NSGA2 algorithm is proposed, which is used to solve the multiobjective problem. For the original NSGA2 algorithm, the paper made one improvement: joining the local search strategy into the NSGA2 algorithm. After each iteration calculation of the NSGA2 algorithm, a kind of local search strategy is performed in the Pareto optimal set to search better solutions, such that the NSGA2 algorithm can gain a better local search ability which is helpful to the optimization process. Finally, the proposed modified NSGA2 algorithm (MNSGA2) is simulated in the two classic multiobjective problems which is called KUR problem and ZDT3 problem. The calculation results show the modified NSGA2 outperforms the original NSGA2, which indicates that the improvement strategy is helpful to improve the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces. These results serve as mathematical foundation for the analysis of machine learning algorithms based on compositions of functions. As a direct consequence in the finitesample case, the corresponding infinite-dimensional minimization problems can be recast into (nonlinear) finite-dimensional minimization problems, which can be tackled with nonlinear optimization algorithms. Moreover, we show how concatenated machine learning problems can be reformulated as neural networks and how our representer theorem applies to a broad class of state-of-the-art deep learning methods. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel cross-domain transfer networks (CDTN) that is employed for multi-view gait recognition. CDTN consists of two VGAN layers and a GAN unit. The VGAN layer merges variational autoencoder (VAE) into generative adversarial network (GAN) by replacing the generator of GAN with VAE. Gait energy images (GEIs) are taken as the input of the VGAN layer, and then discriminative loss, reconstruction loss and Kullback–Leibler divergence are adopted to optimize CDTN synchronously. Two VGAN layers are connected by a GAN unit that takes the two gait samples collected under different views as input. After optimization, the output of generator is decoded by a decoder, the decoding results are taken as the output of CDTN. Finally, extensive experiments are conducted on two famous gait datasets. Compare with the state-of-the-art methods, CDTN achieves better gait recognition accuracies, such as 94.78% under single view angle and 93.68% under multi-view angles, which outperforms the existing methods by a significant margin. The results indicate that CDTN is effective for improving the accuracy of multi-view gait recognition. Besides, CDTN provides an important reference for solving the similar problems. [ABSTRACT FROM AUTHOR]

We study convex optimization problems that feature low-rank matrix solutions. In such scenarios, non-convex methods offer significant advantages over convex methods due to their lower space complexity, as well as practical faster convergence. Under mild assumptions, these methods feature global convergence guarantees. In this paper, we extend the results on this matter by following a different path. We derive a non-Euclidean optimization framework in the non-convex setting that takes nonlinear gradient steps on the factors. Our framework enables the possibility to further exploit the underlying problem structures, such as sparsity or low-rankness on the factorized domain, or better dimensional dependence of the smoothness parameters of the objectives. We prove that the non-Euclidean methods enjoy the same rigorous guarantees as their Euclidean counterparts under appropriate assumptions. Numerical evidence with Fourier ptychography and FastText applications, using real data, shows that our approach can enhance solution quality, as well as convergence speed over the standard non-convex approaches. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing. Some common phenomena are considered in QS: (1) customers actively follow the queue that provides fast service; (2) each customer service is mainly affected by the staff or customer itself; and (3) a customer can be influenced by others during the service when the queue order is not strictly maintained. The performance of QS is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems. Meanwhile, comparisons are performed among the results of QS and some state-of-the-art or well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Highlights • An enhanced surrogate assisted framework for constrained global optimization is proposed. • Maximizing probability of improvement approach is used for selecting infill points. • Kriging meta-models of objective and constraints functions are updated in every iteration. • Meta-model of objective function is local optimized when it's sufficient mature. • Numerical results indicate that the framework is suitable for use in solving computationally expensive and constrained black-box optimization. Abstract An enhanced surrogate assisted framework, based on Probability of Improvement (PI) method, is proposed in this paper. We made some modifications to the original PI approach to enhance the performance of the modeling and optimization framework, leading to fewer rigorous simulations to find the optimal solution without loss of accuracy. We also extended the algorithm for handling general constraints using a fully probabilistic approach. The behavior of the proposed framework was investigated through a set of 9 Unconstrained Test Functions (UTF), 7 Constrained Optimization Problems (COP) and 3 Chemical Engineering Problems (CEP). The numerical results indicate that a lower number of rigorous model simulations were needed for optimizing UTF compared to the classic PI method and that the proposed framework was capable of achieving sustained near optimal solutions for COP and CEP. These results indicate that the proposed framework is suitable for solving computationally expensive constrained black-box optimization problems. [ABSTRACT FROM AUTHOR]

Highlights • A novel piecewise state feedback controller is introduced. • The chemical process optimal control problem is formulated as an optimal control problem of switched dynamical systems. • A gradient-based parallel optimization algorithm is developed for solving the optimal control. • Convergence results indicate that the gradient-based parallel optimization algorithm is global convergent. • Three chemical process optimal control problems are given to illustrate the effectiveness of the proposed algorithm. Abstract This paper considers an optimal control problem of chemical processes with a novel piecewise state-feedback controller. Firstly, the chemical process optimal control problem is formulated as a switched dynamical system optimal control problem, which can be transformed into a parameter optimization problem. Next, to achieve rapid convergence from remote starting points, we propose a novel gradient-based optimization algorithm, which is suitable to a parallel implementation because the step is improved by updating its direction as well as its length simultaneously before moving to the next iteration, and the step computation involves only the inner products of vectors. Then, the convergent properties of the parameter optimization problem to the original optimal control problem are discussed. Finally, the numerical simulation results show that the gradient-based parallel optimization algorithm is an effective alternative method for solving the chemical process optimal control problems. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

In this paper, we consider near-optimal operation for a class of unconstrained batch processes using the self-optimizing control (SOC) methodology. The existing static SOC approach is extended to the dynamic case by means of a static reformulation of the dynamic optimization problem. However, the dynamic SOC problem is posed as a structure-constrained controlled variable (CV) selection problem, which is different from the static cases. A lower-block triangular structure is specified for the combination matrix, H , to allow for optimal operation whilst respecting causality. A new result is that the structure-constrained SOC problem still results in a convex formulation, which has an analytic solution where the optimal CVs associated with discrete time instants are solved separately. In addition, the inputs are directly determined based on current CV functions for on-line utilization. A fed-batch reactor and a batch distillation column are used to demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

The goethite process is a complicated process with multiple interactive chemical reactions in zinc hydrometallurgy. The use of a dynamic model plays an important role in predicting the key indicator on-line and in process control and optimization. However, because of the coupling influences among the chemical reactions, the conventional continuous stirred tank reactor (CCSTR) model is not adequate to describe this process. In this paper, we develop a weighted-coupling CSTR (WCCSTR) model for the goethite process by introducing weighted parameters. A parameter identification method is proposed to determine the unknown parameters. Then, a model predicted control (MPC) scheme is designed to achieve the process performance goals and minimize the process cost. To overcome the impact of frequent fluctuations in production conditions on the control performance, a novel parameter adaptive correction approach is proposed. The convergence of the adaptive correction approach is proved based on Lyapunov stability theory. Simulation results verify that the WCCSTR model has a higher prediction accuracy than the CCSTR model. The experimental results demonstrate that the MPC scheme performs better in controlling the process and reducing the process costs. [ABSTRACT FROM AUTHOR]

Partial shading is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV) system. When partial shading occurs the system has multiple-peak output power characteristics. In order to track the global maximum power point (GMPP) within an appropriate period a reliable technique is required. Conventional techniques such as hill climbing and perturbation and observation (P&O) are inadequate in tracking the GMPP subject to this condition resulting in a dramatic reduction in the efficiency of the PV system. Recent artificial intelligence methods have been proposed, however they have a higher computational cost, slower processing time and increased oscillations which results in further instability at the output of the PV system. This paper proposes a fast and efficient technique based on Radial Movement Optimization (RMO) for detecting the GMPP under partial shading conditions. The paper begins with a brief description of the behavior of PV systems under partial shading conditions followed by the introduction of the new RMO-based technique for GMPP tracking. Finally, results are presented to demonstration the performance of the proposed technique under different partial shading conditions. The results are compared with those of the PSO method, one of the most widely used methods in the literature. Four factors, namely convergence speed, efficiency (power loss reduction), stability (oscillation reduction) and computational cost, are considered in the comparison with the PSO technique. [ABSTRACT FROM AUTHOR]

Maintenance research catches growing attention for its increasing importance in reality. Spare parts and intermediate buffer are two important factors related to maintenance activity, and both of them may greatly influence the system cost. Although some researchers have considered maintenance and buffer inventory, or maintenance and spare parts inventory together, the joint optimization of the three has not been considered before. This paper is focused on the interaction between maintenance, buffer inventory, and spare parts inventory, to achieve the minimization of the long-term expected cost rate for a production system. The investigated production system of the paper consists of two serial machines, an intermediate buffer, and a spare part inventory. The practical example for this type of system can be an automated work center or an automobile general assembly. The cost model that represents the long-term expected cost rate is developed by mathematical analysis. Four control variables corresponding to decisions made on maintenance, buffer, and spare parts are included. Then by using the genetic algorithm method, the cost model is optimized. The proposed method is applied to different simulation cases to show its efficiency and necessity. Additionally, the related optimization results indicate that the joint optimization can effectively alleviate the influence caused by the change of buffer accumulation speed. [ABSTRACT FROM AUTHOR]

The paper presents the method of facilitating joint decision making in a competitive environment We deal with a joint decision when the group of people with different preferences are to make one single decision. The joint decision selection process is modelled with the use of multi-criteria optimization task. It is solved with the use of reference point method. This method is an interactive method in which every person specifies its requirements in the form of a reference point, expressing the desired values for its evaluation function. On the basis of the provided reference point, a scalar achievement function is built. Maximization of this function generates a solution of the multi-criteria task. This solution is presented to every person for acceptance or as a basis for the modi-fication of the reference point. The paper gives an example of applying the proposed decision making while competing by three persons of different preferences. [ABSTRACT FROM AUTHOR]

This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D) fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D) complex space is proposed, which achieves a far better fault detection ratio (FDR) against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT) method. By adding redundant bypassing-components in the circuit under test (CUT), this method achieves excellent fault isolation ratio (FIR) in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper. [ABSTRACT FROM AUTHOR]

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper. [ABSTRACT FROM PUBLISHER]

Rough sets, developed by Pawlak [15], are important tool to describe situation of incomplete or partially unknown information. In this article we give the formal characterization of two closely related rough approximations, along the lines proposed in a paper by Gomolinska [2]. We continue the formalization of rough sets in Mizar [1] started in [6]. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach to design wideband, accurate, stable, and minimum-phase fractional-order digital differentiators (FODDs) in terms of the infinite impulse response (IIR) filters using an evolutionary optimization technique called flower pollination algorithm (FPA). The efficiency comparisons of FPA with real-coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE)-based designs are conducted with respect to different magnitude and phase response error metrics, parametric and nonparametric statistical hypotheses tests, computational time, and fitness convergence. Exhaustive simulation results clearly demonstrate that FPA significantly outperforms RGA, PSO, and DE in attaining the best solution quality consistently. Extensive analysis is also conducted in order to determine the role of control parameters of FPA on the performance of the designed FODDs. The proposed FPA-based FODDs outperform all the designs published in the recent literature with respect to the magnitude responses and also achieve a competitive performance in terms of the phase response. [ABSTRACT FROM AUTHOR]

For a set H of graphs, a graph G is H -free if G does not contain any subgraph isomorphic to some graph in H . In this paper, we study the minimum H -free node deletion problem (Min H FND) and the maximum H -free node set problem (Max H FNS), which include a lot of extensively-studied problems such as the minimum k -path vertex cover problem, the dissociation number problem, and the minimum degree bounded node deletion problem. For a large class of H , PTASs are given for Min H FND and Max H FNS on disk graphs whose heterogeneity is bounded by a constant, where the heterogeneity of a disk graph is the ratio of the maximum radius to the minimum radius of disks. [ABSTRACT FROM AUTHOR]

Time consistency is one of desirable properties for any solution of a cooperative dynamic game. If a solution is time-consistent, the players do not need to break a cooperative agreement. In this paper, we consider the core as the solution and establish conditions for its strong time consistency. When the core is not strongly time-consistent, we show that in some cases its elements can be yielded using a strongly time-consistent imputation distribution procedure. An explicit form of the procedure is given. [ABSTRACT FROM AUTHOR]

The paper is inspired by recent experiment with a two-member discrete column subjected to dry friction force of interaction between the moving column and a moving plane. The experiment was presented in the form of a YouTube film and recommended as an experimental evidence for flutter in the Ziegler column. We show that the tested mechanism cannot be identified with the original Ziegler column. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multiobjective linear programs where coefficients in the objective function belong to uncertainty sets. We introduce the concept of robust efficient solutions to uncertain multiobjective linear programming problems. By using two scalarization methods, the weighted sum method and the ϵ-constraint method, we obtain that the robust efficient solutions for uncertain multiobjective linear programs with ellipsoidal uncertainty sets and general norm uncertainty sets can be computed by some deterministic optimization problems. [ABSTRACT FROM AUTHOR]

The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems. [ABSTRACT FROM AUTHOR]

This paper presents the results of development and optimization of an industrial combined-cycle gas turbine system for plasma disposal of sewage sludge. The enhanced system flow diagram, critical elements selection, and heat and mass balance optimization provide a solid basis for prototyping a plant for the disposal of sewage sludge with up to 75% water content. [ABSTRACT FROM AUTHOR]

Oil and gas companies play an important role in the global economy since they supply a large portion of the necessary energy to the world. The optimal production of oil and gas should be performed in an integrated fashion for the whole supply chain. The downstream oil and gas supply chain (OGSC) has attracted the interest of many researchers due to its central role in the world economy. This paper develops an integrated multi-objective OGSC model for medium-term tactical decision making for the OGSC downstream segment. The selected objectives related to downstream activities are the following: minimize the total cost, maximize the total revenue, and maximize the service level. The model includes multi-period and multi-product inputs. The model is verified and solved using an improved augmented ε-constraint algorithm to generate Pareto optimal solutions. The model assists in assessing various trade-offs among different objectives and guides decision makers for the effective management of the downstream OGSC. The utility of the proposed model is demonstrated using a real case from a Saudi Arabian downstream OGSC. Sensitivity analysis is conducted to investigate the effects of input parameters on the set of Pareto optimal solutions. The model is expected to have a positive impact on the future management of this important component of the energy sector. [ABSTRACT FROM AUTHOR]

This paper considers the phase-only sequence design problem for a cognitive radar in order to achieve both the desired autocorrelation and the stopband properties, which is useful to obtain good range compression as well as the spectral compatibility. We develop a new objective function accounting for the weighted integrated sidelobe level and the spectral power within specific stopbands. An iteration algorithm based on pattern search (PS) is proposed to minimize the nonconvex multidimensional objective function with the continuous phase case, which is split into multiple one-dimensional problems that are simplified and solved with closed-form solutions. In particular, the PS algorithm can be applied to the discrete phase case. Finally, we evaluate the effectiveness of the PS algorithm compared with the weighted-stopband cyclic algorithm new algorithm via numerical simulations in terms of the autocorrelation function, the spectral power function, and the convergence speed. [ABSTRACT FROM AUTHOR]

The paper is devoted to investigating a class of neutral stochastic integro-differential equations with impulses driven by fractional Brownian motion. By establishing two new impulsive integral inequalities which improve the inequalities established by Li (Neurocomputing 177:620-627, 2016) and Long et al. (Stat. Probab. Lett. 82(9):1699-1709, 2012), attracting and quasi-invariant sets of the system are obtained. Moreover, exponential stability of the mild solution is established with sufficient conditions. [ABSTRACT FROM AUTHOR]

In the present paper a multidisciplinary optimization procedure for the self-generation of re-entry vehicle shapes has been developed. The procedure relies on a novel parametric model of blended wing-body shapes which is used to create a re-entry configuration around a fixed volume. The flexibility of the model allows us to create lifting body or winged re-entry vehicle from an optimization procedure as monolithic bodies. Multidisciplinary analysis is performed with engineering methods valid in conceptual design. Results of shape optimization for a minimum mass configuration, performed for a Low Earth Orbit Re-entry mission, confirmed the suitability of the procedure by indicating a decrease of vehicle mass configuration that is obtained by reducing the wingspan parameter for a conceptual lifting body configuration. [ABSTRACT FROM AUTHOR]

PID control design using optimized modified Ziegler-Nichols tuning is for active suspensions of tilting nature is presented. The study of this refers to non-precedent tilt active suspensions for railway vehicles which comprises a cumbersome design trade-off. No study exists on detailed Ziegler-Nichols PID tuning for Single-Input-Single-Output type non-precedent tilt control. We therefore investigate such an approach, referred to here as simple3 tilt, emphasizing control performance that can be achieved in such type of tilting suspension problem. The aim is to provide a baseline design tool for control practicioners, in active suspensions of that nature, who may be more familiar with traditional PID tuning rules. Without loss of generality the suggestions in this paper can be considered in other applications of tilting suspension nature. [ABSTRACT FROM AUTHOR]

The g-good-neighbor conditional diagnosability is the maximum number of faulty vertices a network can guarantee to identify, under the condition that every fault-free vertex has at least g fault-free neighbors. In this paper, we study the 1-good-neighbor conditional diagnosabilities of some general k-regular k-connected graphs G under the PMC model and the MM* model. The main result under some conditions is obtained, where l is the maximum number of common neighbors between any two adjacent vertices in G. Moreover, the following results are derived: for the hierarchical star networks, for the BC networks, for the alternating group graphs . [ABSTRACT FROM AUTHOR]

To the best of our knowledge, there is only one approach for solving neutrosophic cost minimization transportation problems. Since neutrosophic transportation problems are a new area of research, other researchers may be attracted to extend this approach for solving other types of neutrosophic transportation problems like neutrosophic solid transportation problems, neutrosophic time minimization transportation problems, neutrosophic transshipment problems, and so on. However, after a deep study of the existing approach, it is noticed that a mathematical incorrect assumption has been used in these existing approaches; therefore there is a need to modify these existing approaches. Keeping the same in mind, in this paper, the existing approach is modified. Furthermore, the exact results of some existing transportation problems are obtained by the modified approach. [ABSTRACT FROM AUTHOR]

Scheduling of jobs and resources on a shop floor is an ever green optimization problem. Job shop scheduling problem (JSSP) is an allocation of 'n' jobs on 'm' machines so as to complete processing of all jobs in a minimum possible time. The JSSP has been addressed by various direct, indirect methods, programs, and algorithms in the last 40 year of literature. This paper presents a new paradigm of invasive weed optimization which mimics the process of weed colonization and distribution to solve JSSPs. The algorithm had shown encouraging and promising outputs on standard benchmarking problems. [ABSTRACT FROM AUTHOR]