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As part of a larger effort to digitize its global supply chain, Kimberly-Clark is putting a combination of machine learning and optimization algorithms to work to optimize its transportation networks-- [...]

Purpose: This paper aims to describe the use of optimization approaches to increase the range of near-future howitzer ammunition. Design/methodology/approach: The performance of a gliding projectile concept is assessed using an aeroballistic workflow, comprising aerodynamic characterization and flight trajectory computation. First, a single-objective optimization is run with genetic algorithms to find the maximal attainable range for this type of projectile. Then, a multi-objective formulation of the problem is proposed to consider the compromise between range and time of flight. Finally, the aerodynamic model used for the gliding ammunition is evaluated, in comparison with direct computational fluid dynamics (CFD) computations. Findings: Applying single-objective range maximization results in a great improvement of the reachable distance of the projectile, at the expense of the flight duration. Therefore, a multi-objective optimization is implemented in a second time, to search sets of parameters resulting in an optimal compromise between fire range and flight time. The resulting Pareto front can be directly interpreted and has the advantage of being useful for tactical decisions. Research limitations/implications: The main limitation of the work concerns the aerodynamic model of the gliding ammunition, which was initially proposed as an alternative to reduce significantly the computational cost of aerodynamic characterization and enable optimizations. When compared with direct CFD computations, this method appears to induce an overestimation of the range. This suggests future evolution to improve the accuracy of this approach. Originality/value: To the best of the authors' knowledge, this paper presents an original ammunition concept for howitzers, aiming at extending the range of fire by using lifting surfaces and guidance. In addition, optimization techniques are used to improve the range of such projectile configuration. [ABSTRACT FROM AUTHOR]

Solar thermal energy appears poised for a renaissance in the United States, as the Obama Administration presses forward with its green energy agenda. But the revival could be short-lived unless [...]

Purpose: This paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM). Design/methodology/approach: First, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts. Findings: A suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses. Originality/value: This paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function. Design/methodology/approach: In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation. Findings: From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job. Practical implications: This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location. Originality/value: An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications. Design/methodology/approach: The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure. Findings: The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given. Originality/value: This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed. [ABSTRACT FROM AUTHOR]

It is my pleasure to report that we have a couple of very successful on-going special issues, including a Special Issue on neurodynamic systems for optimization and applications and a Special Issue on neural networks and learning systems applications in smart grid. They received 101 and 79 submissions, respectively. You can visit http://www.tnnls.org for the call for papers of other Special Issues. If you and your colleagues would like to propose a Special Issue for TNNLS, please follow the instructions posted at http://cis.ieee.org/tnnls to submit your proposals. Special Issue proposals are always welcome on timely and hot topics related to neural networks and learning systems. [ABSTRACT FROM AUTHOR]

This paper discusses the process of optimizing the shape of systems that are controlled by the Stokes flow with threshold leak boundary conditions. In the theoretical part it focuses on studying the stability of solutions to the state problem in relation to a specific set of domains. In order to facilitate computation, the slip term and impermeability condition are regulated. In the computational part, the optimized portion of the boundary is defined using Bézier polynomials, in order to create a finite dimensional optimization problem. The paper also includes numerical examples to demonstrate the computational efficiency of this approach. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data. Design/methodology/approach: Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade's AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data. Findings: To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics. Practical implications: This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory. Originality/value: Assessment of the reliability of AoA and relative velocity estimation methods for VAWT' blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions. [ABSTRACT FROM AUTHOR]

Purpose: Novel aircraft propulsion configurations require a greater integration of the propulsive system with the airframe. As a consequence of the closer integration of the propulsive system, higher levels of flow distortion at the fan face are expected. This distortion will propagate through the fan and penalize the system performance. This will also modify the exhaust design requirements. This paper aims to propose a methodology for the aerodynamic optimization of the exhaust for novel embedded propulsive systems. To model the distortion transfer, a low order throughflow fan model is included. Design/methodology/approach: As the case study a 2D axisymmetric aft-mounted annular boundary layer ingestion (BLI) propulsor is used. An automated computational fluid dynamics approach is applied with a parametric definition of the design space. A throughflow body force model for the fan is implemented and validated for 2D axisymmetric and 3D flows. A multi-objective optimization based on evolutionary algorithms is used for the exhaust design. Findings: By the application of the optimization methodology, a maximum benefit of approximately 0.32% of the total aircraft required thrust was observed by the application of compact exhaust designs. Furthermore, for the embedded system, it is observed that the design of the compact exhaust and the nacelle afterbody have a considerable impact on the aerodynamic performance. Originality/value: This paper presents a novel approach for the exhaust design of embedded propulsive systems in novel aircraft configurations. To the best of the authors' knowledge, this is the first detailed optimization of the exhaust system on an annular aft-mounted BLI propulsor. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to deal with the exact computation of the Jacobian of a time criteria from a numerical simulation of power electronics structures, for the sizing by gradient-based optimization algorithm. Design/methodology/approach: Runge Kutta 44 is used to solve the state equations. The generic approach combines numerical and symbolic approaches. The modelling of the static converter is based on ideal switches. Findings: The paper extends the state equations to derivate any state variable according a sizing parameter. The integral expressions used for some sizing performances (e.g. average or RMS values) mix symbolic and numerical approaches. Choices are made for the derivatives of the extrema of which the search is not a continuous process. The use of an object-oriented implementation allows to have generic formulation of some design performances. Research limitations/implications: The paper aims to propose and to test formulations of sizing criteria and their gradients; so, the modelling of the study case is carried out manually. Due to generic modelling approach used for the power electronics, the model is not completely continuous. So, the derivatives according some parameters (e.g. switch controls) must be carried out by finite differences. However, as the global behaviour is continuous, it is not critical. Practical implications: The proposed formulations can be easily applied on simple static converter applications. For applications with large state equations, it should be possible to use the basic model of switches used in simulation tools of power electronics. The solving process and the sizing criteria formulation (with their derivatives) are generic and can be instantiate for any study. Originality/value: The approach proposes formulations giving a numerical sizing dynamic model with a Jacobian computed, if possible, by an exact derivation useful for optimization studies. The approach gives fast simulation and fast computation of the derivatives by combining numerical and analytical approaches. [ABSTRACT FROM AUTHOR]

In a very recently presented paper, Turky and Abdullah [5] proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers. [ABSTRACT FROM AUTHOR]

Purpose: Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm. Design/methodology/approach: Cloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance. Findings: The fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services. Practical implications: The findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps. Originality/value: Using a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper. [ABSTRACT FROM AUTHOR]

Purpose: Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network. Design/methodology/approach: The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model. Findings: The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation. Originality/value: There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered. Highlights: Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm). [ABSTRACT FROM AUTHOR]

The scenario approach is a general methodology for data-driven optimization that has attracted a great deal of attention in the past few years. It prescribes that one collects a record of previous cases (scenarios) from the same setup in which optimization is being conducted and makes a decision that attains optimality for the seen cases. Scenario optimization is by now very well understood forconvexproblems, where a theory exists that rigorously certifies the generalization properties of the solution, that is, the ability of the solution to perform well in connection to new situations. This theory supports the scenario methodology and justifies its use. This paper considersnonconvexproblems. While other contributions in the nonconvex setup already exist, we here take a major departure from previous approaches. We suggest that the generalization level is evaluated only after the solution is found and its complexity in terms of the length of a support subsample (a notion precisely introduced in this paper) is assessed. As a consequence, the generalization level is stochastic and adjusted case by case to the available scenarios. This fact is key to obtain tight results. The approach adopted in this paper applies not only to optimization, but also to generic decision problems where the solution is obtained according to a rule that is not necessarily the optimization of a cost function. Accordingly, in our presentation we adopt a general stance of which optimization is just seen as a particular case. [ABSTRACT FROM AUTHOR]

The computation-intensive circuit simulation makes the analog circuit sizing challenging for large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been proposed recently for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or constraints. In this paper, we propose a weighted expected improvement-based Bayesian optimization approach for automated analog circuit sizing. Gaussian processes (GP) are used as the online surrogate models for circuit performances. Expected improvement is selected as the acquisition function to balance the exploration and exploitation during the optimization procedure. The expected improvement is weighted by the probability of satisfying the constraints. In this paper, we propose a complete Bayesian optimization framework for the optimization of analog circuits with constraints for the first time. The existing GP model-based optimization methods for analog circuits take the GP models as either offline models or as assistance for the evolutionary algorithms. We also extend the Bayesian optimization algorithm to handle multi-objective optimization problems. Compared with the state-of-the-art approaches listed in this paper, the proposed Bayesian optimization method achieves better optimization results with significantly less number of simulations. [ABSTRACT FROM PUBLISHER]

Purpose: The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment. Design/methodology/approach: In this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs. Findings: According to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time. Originality/value: The multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time. [ABSTRACT FROM AUTHOR]

This paper presents a localization task for a robot swarm guided by the RAOI behavioral rules (repulsion, attraction, orientation, and influence). Slight changes in these parameters can significantly affect the task's performance. To address this challenge, we have developed a swarm simulator incorporating the robots' dynamic model. The localization task is evaluated using objective functions, represented as metrics, which are minimized using multi-objective optimization techniques. Our results showcase the Pareto fronts, illustrating how the objective functions react to variations in the RAOI parameters, aiming to expedite target localization while maintaining the swarm's integrity. • Repulsion, Attraction, Orientation, and Influence parameters (RAOI) are proposed. • We steer a swarm of robots through RAOI parameters to solve collective tasks. • Swarm efficiency depends on simulations and satisfies several objective functions. • Pareto fronts visualize the interaction of objective functions with RAOI parameters. • Results highlight the potential applicability of these methods in swarm tasks. [ABSTRACT FROM AUTHOR]

The main focus of this paper is the presentation of reliable methods for the determination of the optimum coloring of a graph, commonly known in the literature as vertex coloring problem. It has been shown that networks of capacitively coupled oscillators can be used to solve vertex coloring problems. In this paper we address the negative impact of an unbalanced number of couplings for the oscillators on the performance of the network and compensate for this non-uniform coupling structure by an adjustment in the network itself. The negative effect of the memristor device-to-device variability of the NbOx memristor on the array functionality will be investigated and reduced via an adaptation of the memristor operating point. The main improvement in network performance is achieved by setting up a control procedure allowing the network to bypass the local solutions and converge to the global one. Two strategies inspired by global optimization algorithms will be proposed to allow the network to overcome sub-optimal solutions, and find the solution corresponding to the absolute minimum of a performance measure function of the vertex coloring problem. [ABSTRACT FROM AUTHOR]

Purpose: The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS). Design/methodology/approach: This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied. Findings: The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations. Practical implications: The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system. Originality/value: Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques. [ABSTRACT FROM AUTHOR]

Purpose: In the vast majority of published papers, the optimal allocation of photovoltaic distributed generation (PVDG) units and reconfiguration problems are proposed along with the number of PVDG used in the simulation. However, optimisation without selecting the number of PVDG units installed in the distribution grid is insufficient to achieve a better operational performance of power systems. Moreover, multi-objective installation of PVDG units and reconfiguration aims to simultaneously relieve congestion problems, improve voltage profile and minimise the active and reactive power losses. Therefore, this paper aims to propose a new modified camel algorithm (NMCA) to solve multi-objective problems considering radial distribution system to achieve secure and stable operation of electric power system with good performance. Design/methodology/approach: In this paper, the decision variables include the location and size of PVDG units with specific rang to determine the number of PVDG units needed to install and open network lines determined using NMCA based on the L_∞ technique. This also satisfies the operating and radial constraints. Furthermore, a benchmark comparison with different well known optimisation algorithms has been made to confirm the solutions. Finally, an analysis of the findings was conducted, and the feasibility of solutions was fully verified and discussed. Findings: Two test systems – the institute of electrical and electronics engineers (IEEE) 33-bus and IEEE 69-bus, were used to examine the accuracy and effectiveness of the proposed algorithm. The findings obtained amply proved the efficiency and superiority of the NMCA algorithm over the other different optimisation algorithms. Originality/value: The proposed approach is applied to solve the installation PVDG unit's problem and reconfiguration problem in the radial distribution system, satisfying the operating and radial constraints. Also, it minimises active and reactive power losses and improves voltage profile. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque while keeping low the amount of material used, by means of gradient-based free-form shape optimization. Design/methodology/approach: The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. This paper presents an extension of a standard gradient-based free-form shape optimization algorithm to the case of multiple objective functions by determining updates, which represent a descent of all involved criteria. Moreover, this paper illustrates a way to obtain an approximate Pareto front. Findings: The presented method allows to obtain optimal designs of arbitrary, non-parametric shape with very low computational cost. This paper validates the results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm. Originality/value: This paper applies the presented gradient-based multi-objective optimization algorithm in the context of free-form shape optimization using the mathematical concept of shape derivatives. The authors obtain a set of Pareto-optimal designs, each of which is a shape that is not represented by a fixed set of parameters. To the best of the authors' knowledge, this approach to multi-objective free-form shape optimization is novel in the context of electric machines. [ABSTRACT FROM AUTHOR]

This paper considers a Markov decision process (MDP) model with safety state constraints, which specify polytopic invariance constraints on the state probability distribution (pd) for all time epochs. Typically, in the MDP framework, safety is addressed indirectly by penalizing failure states through the reward function. However, such an approach does not allow imposing hard constraints on the state pd, which could be an issue for practical applications where the chance of failure must be limited to prescribed bounds. In this paper, we explicitly separate state constraints from the reward function. We provide analysis and synthesis methods to impose generalized safety constraints at all time epochs, unlike current constrained MDP approaches where such constraints can only be imposed on the stationary distributions. We show that, contrary to the unconstrained MDP policies, optimal safe MDP policies depend on the initial state pd. We present novel algorithms for both finite- and infinite-horizon MDPs to synthesize feasible decision-making policies that satisfy safety constraints for all time epochs and ensure that the performance is above a computable lower bound. Linear programming implementations of the proposed algorithms are developed, which are formulated by using the duality theory of convex optimization. A swarm control simulation example is also provided to demonstrate the use of proposed algorithms. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

Neuromorphic processors have gained momentum recently due to their high energy efficiency in artificial intelligence applications compared to DNN accelerators. Most neuromorphic processors are executing SNNs (Spiking Neural Networks). Liquid State Machine (LSM), as the spiking version of reservoir computing, shows advantages and great potential in image classification, speech recognition, language translation, etc.. Comparing with other SNN models, LSM has the characteristics of easy to train and low resource utilization, which is suitable for low-power and resource-constrained edge computing scenarios. In this paper, we propose a novel design of a neuromorphic processor, LSMCore, aiming at LSM acceleration. LSMCore supports both training and inference of LSM. It consists of 256 input neurons, 1024 liquid neurons, and 1.31M synapses. Besides, multiple optimization techniques, including weight quantization for reducing storage, zero-skipping for decreasing dynamic sparsity, and mini-batch training are adopted in this processor. The experimental results show that the frequency of LSMCore achieves 400 MHz, the power is 4.9W and the area is 18.49 mm2 with a 40nm library. Comparing with the baseline, LSMCore achieves up to $80.7\times $ ($49.6\times $), $91.3\times $ ($56.3\times $), and $83.1\times $ ($56.8\times $) speedup on MNIST, N-MNIST, and Free Spoken Digital Dataset (FSDD) respectively for training (inference), while the accuracy of LSMCore on these three datasets are 96.8%, 97.6%, and 90% respectively. [ABSTRACT FROM AUTHOR]

This paper proposes necessary and sufficient conditions for stability and performance analysis of 2-D mixed continuous-discrete-time systems that can be checked with convex optimization, in particular linear matrix inequalities (LMIs). Specifically, the first contribution of the paper is a condition for exponential stability based on the introduction of a complex Lyapunov function depending polynomially on a parameter and on the use of the Gram matrix method. It is shown that this condition is sufficient for any chosen degree of the complex Lyapunov function, and necessary for an a priori known degree. The second contribution is a non-Lyapunov condition for exponential stability based on eigenvalue products. This condition is necessary and sufficient, and has the advantage of requiring a significantly smaller computational burden for achieving necessity. Lastly, the third contribution is to show how upper bounds on the \cal H\infty norm of 2-D mixed continuous-discrete-time systems can be obtained through a semidefinite program based on complex Lyapunov functions. A necessary and sufficient condition is provided for establishing the tightness of the found upper bounds. [ABSTRACT FROM PUBLISHER]

In this paper, we study a class of risk-sensitive mean-field stochastic differential games. We show that under appropriate regularity conditions, the mean-field value of the stochastic differential game with exponentiated integral cost functional coincides with the value function satisfying a Hamilton –Jacobi– Bellman (HJB) equation with an additional quadratic term. We provide an explicit solution of the mean-field best response when the instantaneous cost functions are log-quadratic and the state dynamics are affine in the control. An equivalent mean-field risk-neutral problem is formulated and the corresponding mean-field equilibria are characterized in terms of backward-forward macroscopic McKean–Vlasov equations, Fokker–Planck–Kolmogorov equations, and HJB equations. We provide numerical examples on the mean field behavior to illustrate both linear and McKean–Vlasov dynamics. [ABSTRACT FROM PUBLISHER]

Explicit model predictive control (EMPC) maps offline the control laws as a set of regions as a function of bounded uncertain parameters using multi-parametric programming. Then, in online mode, it seeks the best solution within these areas. Unfortunately, the offline solution can be computationally demanding because the number of regions can grow exponentially. Thus, this paper presents the application of a deep neural network (DNN) to learn the EMPC's regions for a photovoltaic-based charging station. The main uncertain parameters in this study are the forecast error of photovoltaic power production and the battery's state of charge. Additionally, the connection or disconnection of an electric vehicle is considered a disruption. The final controller creates the regions at the start of each prediction time or when a disruption occurs, only using the previously created DNN. The obtained solution is validated using data from an e-vehicle charging station installed at the University of Trieste, Italy. • Uncertainties like EV consumption affect the performance of optimization techniques. • EMPC creates offline critical regions that are a function of uncertain parameters. • The dimensionality of the problem can be untractable and time-consuming with EMPC. • DNN can be trained to create critical regions in a reduced computation time. [ABSTRACT FROM AUTHOR]

The article discusses research articles on topics including tracing and debugging of distributed systems and the use of programming by examples. Articles cited include "Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems" in the publication "Proceedings of the 25th Symposium on Operating Systems Principles," "Automating Failure-Testing Research at Internet Scale" in the 2016 publication "Proceedings of the 7th ACM Symposium on Cloud Computing," and "Scaling Up Superoptimization" in the 2016 publication "Proceedings of the 21st International Conference on Architectural Support for Programming Languages and Operating Systems."

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where exhaustive means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum. [ABSTRACT FROM AUTHOR]

Purpose: T-shaped cavities occur by design in many technical applications. An example of such a stator cavity is the side space between the guide vane carriers and the outer casing of a steam turbine. Thermal conditions inside it have a significant impact on the deformation of the turbine casing. In order to improve its prediction, the purpose of this paper is to provide a methodology to gain better knowledge of the local heat transfer at the cavity boundaries based on experimental results. Design/methodology/approach: To determine the heat transfer coefficient distribution inside a model cavity with the help of a scaled generic test rig, an inverse heat conduction problem is posed and a method for solving such type of problems in the form of linear combinations of Trefftz functions is presented. Findings: The results of the calculations are compared with another inverse method using first-order gradient optimization technique as well as with estimated values obtained with an analytic two-dimensional thermal network model, and they show an excellent agreement. The calculation procedure is proved to be numerically stable for different degrees of complexity of the sought boundary conditions. Originality/value: This paper provides a universal and robust methodology for the fast direct determination of an arbitrary distribution of heat transfer coefficients based on material temperature measurements spread over the confining wall. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present an approach to the design of the compensation networks (CNs) based on a genetic optimization algorithm. The algorithm is applied to CNs with T-topology and considers the effects of the parasitic series resistances of their inductive components. The effectiveness of the algorithm is verified using Bode diagrams and simulation results. Design/methodology/approach: The paper at first describes the problem and the approach followed to reach a set of optimal solutions, then explains the optimization algorithm, reports the obtained solutions and selects the optimal CNs. Finally, the actual performance of the wireless power transfer system (WPTS) when the selected CNs are used are checked. Findings: This approach gave interesting results and made available a number of different sizing solutions of complex networks in a very short time. Most of the obtained solutions outperform the widely used series-series compensation. An accurate post processing of the obtained result is mandatory to discriminate the solutions that could be implemented from those that in a real system would originate uncontrolled high frequency current oscillation. Originality/value: This paper offers a rather new approach to solve the problem of sizing the CNs of a dynamic WPTS. This approach makes available a large number of optimal solutions to the problem in a short time, without solving complex system of equations. [ABSTRACT FROM AUTHOR]

This paper endeavors to investigate the output-feedback sliding mode control (SMC) issue of the networked singularly perturbed systems (SPSs) under fast sampling. In order to improve the reliability of network communication, a redundant channel transmission protocol is introduced in the SMC design. Based on the measurement outputs, a sliding function is constructed with the consideration of the transmission protocol. With the aid of some appropriate Lyapunov functions, the sufficient conditions are derived to ensure the mean-square exponentially ultimately boundedness of the sliding mode dynamics and the reachability of the specified sliding surface. Moreover, a convex optimization algorithm is formulated to solve the output-feedback SMC law by searching the available upper bound of the singularly perturbed parameter. Finally, an operational amplifier circuit is exploited to explore the influences from the redundant channel transmission protocol to the output-feedback SMC performance and the estimated $\varepsilon$ -bound. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance.Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system.Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes.Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach: The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on "how exactly the matching process is done for evaluating the document." Finally, the e-learners are classified based on their grades. Findings: On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value: This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors' knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations. Design/methodology/approach: Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. Findings: An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. Originality/value: In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach: Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings: The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value: The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach: To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings: In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value: The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper. [ABSTRACT FROM AUTHOR]

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimization stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimization outcomes. • Proposed Gooseneck Barnacle Optimizer mimics gooseneck barnacles' mating behaviour. • Inspiration: sperm casting, wind direction, intertidal wave action during mating. • Address the issues with original Barnacle Mating Optimizer. • Standard benchmark functions evaluate GBO's performance. • Hybridize with Least Square Support Vector Machine for real time-series forecasting. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions (TOIT2FNN-AMF), for nonlinear system identification problems. The proposed TOIT2FNN-AMF uses an asymmetric Gaussian interval type-2 membership function to enhance the network's ability to describe and solve nonlinear and uncertain problems. The twice optimization algorithm consists of structure learning and parameter learning. Firstly, this paper proposes a multi-strategy adaptive differential evolution (MSADE) algorithm as the first optimization algorithm, which is used to determine the structure and the initial values of the parameters of the TOIT2FNN-AMF. It applies the root mean square error (RMSE) of the TOIT2FNN-AMF as the fitness function to determine the structure (number of rules) and initial parameters of the IT2FNN by searching for the RMSE values under different structures. When the fitness value reaches the minimum, that is, the RMSE value of the TOIT2FNN-AMF, the corresponding number will become the optimal one of fuzzy rules of the TOIT2FNN-AMF. Then, the second optimization algorithm of the TOIT2FNN-AMF turns into a hybrid optimization algorithm composed of an adaptive moment estimation (Adam) algorithm and recursive least squares (RLS) algorithm. Adam is used to optimize the antecedent parameters of TOIT2FNN-AMF rules, so as to maintain rapid convergence without generating oscillation during the training process; RLS is used to optimize the consequent parameters of TOIT2FNN-AMF rules, so that the network parameters can be optimized rapidly. In this way, the problems of excessive parameters to be adjusted and excessive slow convergence of the network can be solved. Finally, this paper evaluates the proposed TOIT2FNN-AMF by testing on problems of nonlinear system identification and chaotic time-series prediction. The simulation results are compared with those of similar methods in the existing literatures, which demonstrates that the proposed TOIT2FNN-AMF model yields a lower RMSE value and a simpler network structure than the other type-2 fuzzy neural networks (T2FNNs). [ABSTRACT FROM AUTHOR]

Stochastic configuration networks (SCNs) have universal approximation capability and fast modeling properties, which have been successfully employed in large-scale data analytics. Based on SCNs, Stochastic configuration networks with block increments (BSC) use the node block increments mechanism to improve training speed but increase the complexity of the model. This paper presents a parallel configuration method (PCM), develops an extension of the original BSC with chaos theory and proposes stochastic configuration networks with chaotic maps (SCNCM), and establishes a hierarchical learning strategy (HLS) to enhance the compactness and construction speed of the model. Firstly, PCM randomly assigns the input weights w and biases b of hidden layer nodes by using uniform and normal distributions. In PCM, an iterative learning algorithm is intended to generate the scope control set and improve configuration efficiency. Secondly, the paper presents two kinds of stochastic configuration networks with chaotic maps, which are SCNCM-I and SCNCM-II. SCNCM-I adjusts block size by using multiple error values and chaotic maps to improve the training speed. Based on SCNCM-I, SCNCM-II utilizes node removal mechanism to enhance the compactness. Finally, HLS integrates with SCNCM-I, SCNCM-II, and the Harris-hawks optimization algorithm (HHO). The purpose of training is to enhance the training speed and compactness for three algorithms. The experiments are conducted on four benchmark data sets and an industrial application shows its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article. [ABSTRACT FROM AUTHOR]

UK paperboard plant achieves big savings through the use of advanced process modelling. Patrick Raleigh reports Iggesund Paperboard Ltd of Workington, UK, has recently completed a project to optimise the [...]

This paper develops a neural network for solving the general nonsmooth convex optimization problems. The proposed neural network is modeled by a differential inclusion. Compared with the existing neural networks for solving nonsmooth convex optimization problems, this neural network has a wider domain for implementation. Under a suitable assumption on the constraint set, it is proved that for a given nonsmooth convex optimization problem and sufficiently large penalty parameters, any trajectory of the neural network can reach the feasible region in finite time and stays there thereafter. Moreover, we can prove that the trajectory of the neural network constructed by a differential inclusion and with arbitrarily given initial value, converges to the set consisting of the equilibrium points of the neural network, whose elements are all the optimal solutions of the primal constrained optimization problem. In particular, we give the condition that the equilibrium point set of the neural network coincides with the optimal solution set of the primal constrained optimization problem and the condition ensuring convergence to the optimal solution set in finite time. Furthermore, illustrative examples show the correctness of the results in this paper, and the good performance of the proposed neural network. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach: This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings: From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value: This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to discuss inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches that may be used to obtain results that lie within a small region of uncertainty. Therefore, the non-uniqueness of the solution is reduced so that the final design and boundary conditions may be determined. Optimization methods that may be used to reduce the uncertainty and to select locations for experimental data and for minimizing the error are presented. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. Design/methodology/approach: In most analytical and numerical solutions, the basic equations that describe the process, as well as the relevant and appropriate boundary conditions, are known. The interest lies in obtaining a unique solution that satisfies the equations and boundary conditions. This may be termed as a direct or forward solution. However, there are many problems, particularly in practical systems, where the desired result is known but the conditions needed for achieving it are not known. These are generally known as inverse problems. In manufacturing, for instance, the temperature variation to which a component must be subjected to obtain desired characteristics is prescribed, but the means to achieve this variation are not known. An example of this circumstance is the annealing, tempering or hardening of steel. In such cases, the boundary and initial conditions are not known and must be determined by solving the inverse problem to obtain the desired temperature variation in the component. The solutions, thus, obtained are generally not unique. This is a review paper, which discusses inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches or strategies that may be used to obtain results that lie within a small region of uncertainty. It is important to realize that the solution is not unique, and this non-uniqueness must be reduced so that the final design and boundary conditions may be determined with acceptable accuracy and repeatability. Optimization techniques are often used for minimizing the error. This review presents several methods that may be applied to reduce the uncertainty and to select locations for experimental data for the best results. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. By considering a variety of systems, the paper also shows the importance of solving inverse problems to obtain results that may be used to model and design thermal processes and systems. Findings: The solution of inverse problems, which frequently arise in thermal processes, is discussed. Different strategies to obtain the conditions that lead to the desired result are given. The goal of these approaches is to reduce uncertainty and obtain essentially unique solutions for different circumstances. The error of the method can be checked against known conditions to see if it is acceptable for the given problem. Several examples are given to illustrate the use of these methods. Originality/value: The basic strategies presented here for solving inverse problems that arise in thermal processes and systems, as well as the optimization techniques used to reduce the domain of uncertainty, are fairly original. They are used for certain challenging problems that have not been considered in detail earlier. Several methods are outlined for considering different types of problems. [ABSTRACT FROM AUTHOR]

This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation. [ABSTRACT FROM PUBLISHER]

In this paper, we present a unified framework for solving a general class of problems arising in the context of set-membership estimation/identification theory. More precisely, the paper aims at providing an original approach for the computation of optimal conditional and robust projection estimates in a nonlinear estimation setting, where the operator relating the data and the parameter to be estimated is assumed to be a generic multivariate polynomial function, and the uncertainties affecting the data are assumed to belong to semialgebraic sets. By noticing that the computation of both the conditional and the robust projection optimal estimators requires the solution to min-max optimization problems that share the same structure, we propose a unified two-stage approach based on semidefinite-relaxation techniques for solving such estimation problems. The key idea of the proposed procedure is to recognize that the optimal functional of the inner optimization problems can be approximated to any desired precision by a multivariate polynomial function by suitably exploiting recently proposed results in the field of parametric optimization. Two simulation examples are reported to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Checking validity of a model is a crucial step in the process of system identification. This is especially true when dealing with switched affine systems since, in this case, the problem of system identification from noisy data is known to be generically NP-Hard and can only be solved in practice by using heuristics and relaxations. Therefore, before the identified models can be used for instance for controller design, they should be systematically validated against additional experimental data. In this paper we address the problem of model (in)validation for multi-input multi-output switched affine systems in output error form with unknown switches. As a first step, we prove that necessary and sufficient invalidation certificates can be obtained by solving a sequence of convex optimization problems. In principle, these problems involve increasingly large matrices. However, as we show in the paper by exploiting recent results from semialgebraic geometry, the proposed algorithm is guaranteed to stop after a finite number of steps that can be be explicitly computed from the a priori information. In addition, this algorithm exploits the sparse structure of the underlying optimization problem to substantially reduce the computational burden. The effectiveness of the proposed method is illustrated using both academic examples and a non-trivial problem arising in computer vision: activity monitoring. [ABSTRACT FROM AUTHOR]

The aim of this paper is to approximate a Finite-State Markov (FSM) process by another process defined on a lower dimensional state space, called the approximating process, with respect to a total variation distance fidelity criterion. The approximation problem is formulated as an optimization problem using two different approaches. The first approach is based on approximating the transition probability matrix of the FSM process by a lower-dimensional transition probability matrix, resulting in an approximating process which is a Finite-State Hidden Markov (FSHM) process. The second approach is based on approximating the invariant probability vector of the original FSM process by another invariant probability vector defined on a lower-dimensional state space. Going a step further, a method is proposed based on optimizing a Kullback-Leibler divergence to approximate the FSHM processes by FSM processes. The solutions of these optimization problems are described by optimal partition functions which aggregate the states of the FSM process via a corresponding water-filling solution, resulting in lower-dimensional approximating processes which are FSHM or FSM processes. Throughout the paper, the theoretical results are justified by illustrative examples that demonstrate our proposed methodology. [ABSTRACT FROM AUTHOR]