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The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

In the paper optimization of the motorcycle rear suspension mechanism is presented. The introduction of a four-bar linkage in the classic rear suspension system, composed of a large fork made up of two trailing arms with two spring damper units, one on each side, inclined at a certain angle with respect to the swinging arm, makes it easier to obtain the desired damping and stiffness characteristics of the suspension system. The geometric parameters of the four-bar linkage (Uni-Trak® design of Kawasaki) are used in the paper as the design variables, which are optimized to obtain desired damping characteristics of the rear suspension system. It enables avoiding hitting suspension bumper stops which causes rider's discomfort and also excessive dynamic loads effecting motorcycle parts. Optimization toolbox for use with MATLAB (sequential quadratic programming) and also genetic algorithms were applied to solve the optimization problem. The analysis of numerical results shows that a possibility of varying just one geometric parameter of the four-bar linkage enables tuning the suspension system to different kinds of uneven roads (with different stochastic characteristics). [ABSTRACT FROM AUTHOR]

Landmines still pose a serious threat mostly to civilians. Therefore, there is no doubt that working on the methods of landmines’ detection and liquidation should be considered extremely important. In this paper, we introduce the active infrared thermography with microwave excitation used in nonmetallic landmines’ detection. The effectiveness of the method is strongly dependent on the manner in which microwaves illuminate the ground. In this paper, we propose a new waveguide flange’s construction, which guarantees the uniform field distribution, and through this, a relatively uniform microwave heating on a certain area. The flange’s shape is based on box-horn. Its dimensions will be determined using a hybrid optimization algorithm. Results obtained in an optimization process will be validated using the numerical models. The experiments showing flange’s efficiency, concerning heating of sand itself and sand with buried landmines, will also be presented. [ABSTRACT FROM PUBLISHER]

This paper is focused on the automated and unattended optimization of a type of planar wideband bandpass filters by means of aggressive space mapping (ASM). The considered filters are microstrip filters implemented through a combination of shunt connected stepped impedance resonators (SIRs) and grounded stubs coupled through admittance inverters. The most relevant and novel aspect of this paper is the fact that the filter layout is automatically generated from filter specifications, i.e., central frequency, fractional bandwidth, in-band ripple and order, without the need of any external aid to the design process. To achieve this layout generation, filter optimization has been divided into two independent ASM processes. The first one generates the filter schematic (circuit element values) providing the required specifications. This first iterative process is necessary since, due to the narrow band operation of the admittance inverters, the target specifications are achieved by compensating the effects of such narrow band operation. The purpose of the second ASM algorithm is to automatically generate the layout from the filter schematic resulting from the first ASM process. To validate the new two-step ASM optimization tool, two sets of filter specifications (inputs of the developed tool), are considered. The generated filter layouts exhibit frequency responses that satisfy the specifications, and are in excellent agreement with the responses of the schematics. [ABSTRACT FROM AUTHOR]

This paper extensively evaluates the stability and performance of heterochannel 6T/8T SRAM cells integrated in monolithic 3-D scheme with interlayer coupling. Various bitcell layouts with different gate alignments of transistors from distinct layers are investigated. This paper indicates that stacking the NFET tier over the PFET tier results in larger design margins for cell robustness and performance. Furthermore, the partition of 3-D layout design among distinct layers shows profound impacts on the stability, standby leakage, and performance of monolithic 3-D SRAM cells. Compared with the Si-based cells, the use of heterochannel devices increases the improvements of monolithic 3-D design over the 2-D counterparts and emerges as a suitable candidate for future monolithic 3-D IC applications. [ABSTRACT FROM PUBLISHER]

Purpose The purpose of this paper is to optimize a two-level perishable product supply chain by increasing its revenue with the Internet of Things (IoT). It particularly investigates how radio-frequency identification (RFID) technology impacts the revenue of the supply chain.Design/methodology/approach In this paper, two revenue models were built to calculate the revenue of perishable product supply chain before and after the application of IoT to analyze the influences of IoT on perishable product supply chain. In the case study, particular data of an aquatic product supply chain were analyzed through these models which were later solved by a computer simulation method based on Colonial Competitive Algorithm, a new heuristic algorithm inspired by imperialistic competition in human society.Findings Using these revenue models to compare the revenues of supply chain before and after the application of IoT, this paper concludes that the application of IoT can efficiently optimize a perishable product supply chain by balancing its wholesale profits and its total costs including logistics costs, therefore, increasing its overall revenue. However, this conclusion is only applicable for large enterprises, while small enterprises are not supposed to introduce IoT due to its high cost.Originality/value The revenue models built in this paper can be used to evaluate the profits of supply chain and help enterprises determine how to maximize their profits and whether they should introduce IoT in a perishable product supply chain. In addition, through the analysis of case study, this paper gives several valuable suggestions to help enterprises reduce their logistics costs and increase their overall revenue. [ABSTRACT FROM AUTHOR]

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost over time, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is $O(1)$ -competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) user-mobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems. [ABSTRACT FROM PUBLISHER]

Control applications are often implemented on highly cost-sensitive and resource-constrained embedded platforms, such as microcontrollers with a small on-chip memory. Typically, control algorithms are designed using model-based approaches, where the details of the implementation platform are completely ignored. As a result, optimizations that integrate platform-level characteristics into the control algorithms design are largely missing. With the emergence of cyber-physical systems (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms, leading to work on networked control systems and computation-aware control algorithms design. However, there has so far been no work on integrating the characteristics of a memory architecture into the design of control algorithms. In this paper we, for the first time, show that accounting for the impact of on-chip memory (or cache) reuse on the performance of control applications motivates new techniques for control algorithms design. This leads to significant improvement in quality of control for given resource availability, or more efficient implementations of embedded control applications. We believe that this paper opens up a variety of possibilities for memory-related optimizations of embedded control systems, that will be pursued by researchers working on computer-aided design for CPS. [ABSTRACT FROM PUBLISHER]

This paper extends the noise theory of linear networks to enable direct deembedding of noise factors for an active device surrounded by a passive four-port. It solves the problem of noise factor deembedding, when there are feedback paths between the output and input of a device-under-test. It also leads to a new approach to obtain intrinsic noise parameters for on-wafer noise measurements by performing deembedding first and optimization last. Verification of the noise factor deembedding algorithm is performed using idealized data, and the evaluation of the new noise parameter deembedding approach is conducted using the experimental data of the n-type MOSFETs fabricated in 28- and 90-nm CMOS technology from United Microelectronics Corporation. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to study different permanent magnet (PM) bearings geometries based on PM rings with a minimum volume of PM for a given load. Simple and general scaling laws (SCLs) are proposed for each bearing geometry to predict its behavior. These SCLs allow a fast sizing of those geometries, as a function of the number of PM rings, the load reference, the air gap, and the remanent flux density. Those SCLs are derived for optimized topologies without any constraint on the geometrical parameters, as well as for optimized geometries with a constraint on the PM dimensions. For both cases, SCLs for the thrust-bearing stiffness are also proposed. [ABSTRACT FROM AUTHOR]

This paper presents a novel formulation of video stabilization by directly solving for optimal image warps toward stabilized sequence. With the estimated shaky motion via long or short feature trajectories, our approach encodes another two steps, motion compensation and image warping, into a single global optimization process, rather than operating as two individual steps. This process is done only with positions of embedded mesh vertices as common variables. Spatial and temporal coherence is therein reformulated with similarity-invariant representation of motion trajectories and intra- (and inter-) frame consistency of similar transformations with respect to mesh vertices. Such a one-shot formulation converts video stabilization into a quadratic energy minimization problem defined for image warps, and thus can be efficiently resolved by using a robust solver for sparse linear systems. Experimental results demonstrate the flexibility and efficiency of our approach in producing visually plausible stabilization effects on a variety of videos. [ABSTRACT FROM AUTHOR]

In cooperative cognitive radio networks (CRNs), multiple secondary base stations are managed by a central unit (CU) via finite-capacity backhaul links, which performs joint encoding and precoding of the messages intended for the secondary users (SUs). In this paper, the limitation of backhaul links is handled with the implementation of compression strategies, by which precoding is followed at the CU. In particular, focusing on underlay cooperative CRNs, we propose the joint optimal design of precoding and backhaul quantization with the aim of maximizing the sum throughput of the secondary network under limited backhaul capacity, transmit power, and interference power constraints. Furthermore, both perfect and imperfect channel state information (CSI) available at the CU are considered. In the imperfect-CSI case, the uncertainty of the channels is explicitly accounted for via a robust or worst-case optimization formulation. The proposed algorithmic solutions leverage rank relaxation and majorization-minimization programming, whose performances are evaluated via numerical results. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. [ABSTRACT FROM AUTHOR]

This paper is intended to propose the numerical methodology in optimizing the cusp gun for a 70-kV, 1-A W-band gyrotron traveling wave amplifier working at the second-harmonic TE21 mode. Two numerical methods using the simulated annealing algorithm (SAA) and genetic algorithm (GA) are adopted to perform the gun optimization. Classical electron optics software EGUN is employed to compute the beam trajectories. Maintaining the velocity ratio around 1.6, the axial velocity spreads optimized by SAA and GA are 1.44% and 0.33%, respectively, which means a great improvement compared with the manually optimized value of 2.78%. An analysis of the impact of parametric fluctuation on beam quality indicates that this cusp gun can be stably operated within a reasonable parameter scope. This paper will help to enhance the efficiency of electron gun design for gyrotron devices. [ABSTRACT FROM AUTHOR]

This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and mobile devices are enabled with device-to-device (D2D) communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station (BS) or by connecting through another UE that acts as an access point (AP) and relays the traffic from a cellular BS. This paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision-making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multicell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power while exhibiting much lower computational requirements. [ABSTRACT FROM PUBLISHER]

Matrix factorization is a popular approach for large-scale matrix completion. The optimization formulation based on matrix factorization, even with huge size, can be solved very efficiently through the standard optimization algorithms in practice. However, due to the non-convexity caused by the factorization model, there is a limited theoretical understanding of whether these algorithms will generate a good solution. In this paper, we establish a theoretical guarantee for the factorization-based formulation to correctly recover the underlying low-rank matrix. In particular, we show that under similar conditions to those in previous works, many standard optimization algorithms converge to the global optima of a factorization-based formulation and recover the true low-rank matrix. We study the local geometry of a properly regularized objective and prove that any stationary point in a certain local region is globally optimal. A major difference of this paper from the existing results is that we do not need resampling (i.e., using independent samples at each iteration) in either the algorithm or its analysis. [ABSTRACT FROM PUBLISHER]

We propose an optimization-based power-constrained red-green–blue (RGB)-to-red-green–blue-white (RGBW) conversion algorithm for emissive RGBW displays. We measure the perceived color distortion using a color difference model in a perceptually uniform color space, and compute the power consumption for displaying an RGBW pixel on an emissive display. The central contribution of this paper is to formulate the optimization problem to minimize the color distortion subject to a constraint on the power consumption. Subsequently, we solve the optimization problem efficiently to convert an image in real time. Furthermore, based on the properties of the human visual system, we extend the proposed algorithm to image-dependent conversion that can preserve spatial detail in an input image. The simulation results show that the proposed algorithm provides a significantly less color distortion than the conventional methods, while providing a graceful tradeoff with the amount of power consumed. Specifically, it is shown that the power consumption can be reduced by up to 20%, while providing about 50% less color distortion than the conventional algorithms. In addition, a subjective evaluation on a real RGBW display is performed, which reveals the merits of the proposed image-dependent conversion for improving the perceptual quality over state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper describes a new method aiming at the multi-objective optimization of a transverse flux machine with a claw-pole and flux-concentrating structure (CPTFM). Considering the 3-D main magnetic flux in the CPTFM, the time-consuming 3-D finite-element method (FEM) should be used to accurately analyze the performance of the machine. In order to reduce the calculation time for the optimization of the complex CPTFM, the Taguchi method is first applied to evaluate the importance of all the design parameters in the consideration of the predefined five objectives. Subsequently, a mathematical model is developed to replace the 3-D FEM, so that the objectives depending on the selected four critical parameters can be analytically calculated. With its help, the four parameters are further optimized with the multi-objective particle swarm optimization method and nondominated sorting genetic algorithm 2. Finally, the optimal design of the CPTFM is selected from all the searched Pareto solutions with the help of fuzzy logic. It can be concluded that the developed method is effective and efficient, which can make an appropriate compromise between the calculation time and the improvement of the machine performance. [ABSTRACT FROM PUBLISHER]

This paper establishes a necessary condition that must be satisfied by the modulation waveforms of any generalized waveforms for multicarrier (GWMC) system with better peak-to-average power ratio (PAPR) than conventional orthogonal frequency division multiplexing (OFDM). GWMC systems include in particular all classical multicarrier modulation systems. As a consequence, we show that OFDM has the best PAPR performance over all GWMC systems that do not satisfy this necessary condition. We also identify an infinite family of GWMC systems with the same PAPR performance as OFDM. To illustrate our results, we present the simulations of the PAPR behavior for different GWMC systems, including some with better PAPR performance than OFDM. [ABSTRACT FROM AUTHOR]

In the robotic engineering field, the main target, especially in industry, manufacturing, and surgical operations, is reaching the optimal performance of manipulators. The purpose of this paper is to quantify the contour tracking performance of collaborative universal manipulator robot (UR5) by setting the gain of position domain controller. In order to improve and enhance the track of manipulator in experimental applications we utilize differential evolution (DE) optimization, using MATLAB toolbox with an applied robot operating system (ROS). The adopted current approach does not only optimize the gain of position domain controller but also prevent collisions by detecting a "border crossing" without turning off the manipulator and allowing the automation agent to be on the scene, coexisting in harmonic mode and avoiding collisions. This requires the implementation of an algorithm that detects an obstacle to avoid anticipated collisions. For this purpose, the adopted algorithm uses the DE algorithm to modify the artificial potential field (APF). The results of this paper present that on one hand, meta-heuristic optimization algorithm features give the best performance indices for linear and non-linear contours, and on the other hand, DE algorithm features give good modification to APF to generate collision free contour path planning. [ABSTRACT FROM AUTHOR]

The security-constrained optimal power flow (SCOPF) is fundamental in power systems and connects the automatic primary response (APR) of synchronized generators with the short-term schedule. Every day, the SCOPF problem is repeatedly solved for various inputs to determine robust schedules given a set of contingencies. Unfortunately, the modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs, which are hard to solve. To address this challenge, leveraging the wealth of available historical data, this paper proposes a novel approach that combines deep learning and robust optimization techniques. Unlike recent machine-learning applications where the aim is to mitigate the computational burden of exact solvers, the proposed method predicts directly the SCOPF implementable solution. Feasibility is enforced in two steps. First, during training, a Lagrangian dual method penalizes violations of physical and operations constraints, which are iteratively added as necessary to the machine-learning model by a Column-and-Constraint-Generation Algorithm (CCGA). Second, another different CCGA restores feasibility by finding the closest feasible solution to the prediction. Experiments on large test cases show that the method results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

We consider a distributed stochastic optimization problem in networks with finite number of nodes. Each node adjusts its action to optimize the global utility of the network, which is defined as the sum of local utilities of all nodes. While Gradient descent method is a common technique to solve such optimization problem, the computation of the gradient may require much information exchange. In this paper, we consider that each node can only have a noisy numerical observation of its local utility, of which the closed-form expression is not available. This assumption is quite realistic, especially when the system is either too complex or constantly changing. Nodes may exchange partially the observation of their local utilities to estimate the global utility at each timeslot. We propose a distributed algorithm based on stochastic perturbation, under the assumption that each node has only part of the local utilities of the other nodes. We use stochastic approximation tools to prove that our algorithm converges almost surely to the optimum, given that the objective function is smooth and strictly concave. The convergence rate is also derived, under the additional assumption of strongly concave objective function. It is shown that the convergence rate scales as O (K−0.5) after a sufficient number of iterations K > K0, which is the optimal rate order in terms of K for our problem. Although the proposed algorithm can be applied to general optimization problems, we perform simulations for a typical power control problem in wireless networks and present numerical results to corroborate our claims. [ABSTRACT FROM AUTHOR]

Aerofoil noise as self-noise is detrimental to system performance, in this paper NACA 0012 optimization parameters are presented for reduction in noise. Designing an aerofoil with little noise is a fundamental objective of designing an aircraft that physically and functionally meets the requirements. Aerofoil self-noise is the noise created by aerofoils interacting with their boundary layers. Using neural networks, the suggested method predicts aerofoil self-noise. For parameter optimization, the quasi-Newtonian method is utilised. The input variables, such as angle of attack and chord length, are used as training parameters for neural networks. The output of a neural network is the sound pressure level, and the Quasi Newton method further optimises these parameters. When compared to the results of regression analysis, the values produced after training a neural network are enhanced. [ABSTRACT FROM AUTHOR]

The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages. [ABSTRACT FROM AUTHOR]

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features' set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm "Random Forest" is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency. [ABSTRACT FROM AUTHOR]

The design of AC filters must meet the criteria of harmonic distortion mitigation and reactive power support in various operating modes. The stringent reactive power-sharing requirements currently lead to sophisticated filter schemes with high component ratings. In this regard, triple-tuned filters (TTFs) have good potential in harmonic mitigation of a broad range of harmonics. In the literature, the TTF design has been presented using a parametric method, assuming that the TTF is equivalent to a three-arm single-tuned filter (TASTF). However, no direct methods of designing it or finding its optimal parameters have been provided. This paper presents novel mathematical designs of TTFs. Three different design methods are considered—the direct triple-tuned filter (DTTF) design method, as a TASTF, and a method based on the equivalence between the two design methods called the equivalence hypothesis method to design the triple-tuned filter (EHF). The parameters of the three proposed design methods are optimized based on the minimization of a proposed multi-objective function using a recent metaheuristic algorithm called artificial rabbits optimization (ARO) to mitigate harmonics, improve power quality, and minimize power losses in an exemplary system presented in IEEE STD-519. Further, the system's performance has been compared to the system optimized by the ant lion optimizer (ALO) and whale optimization algorithm (WOA) to validate the effectiveness of the proposed design. Simulation results emphasized harmonics mitigation in the system, the system losses reduction, and power quality improvement with lower reactive power filter ratings than conventional single and double-tuned filters. [ABSTRACT FROM AUTHOR]

Composite materials are gaining popularity as an alternative to classical materials in many different applications. Moreover, their design is even more flexible due to the potential of additive manufacturing. Thus, one can produce a tailored composite laminate with the optimal values of some design parameters providing the desired mechanical performance. In this context, having a parametric numerical model for the mechanical response of the composite laminate is essential to compute the optimal parameters. In the present paper, the design parameters under consideration are the angles describing the orientation of the reinforcement fibers in different layers or patches of the composite laminates. We obtain a generalized solution using Proper Generalized Decomposition (PGD) which is adopted to provide solutions with explicit parametric dependence. The Tsai-Wu failure criterion is used to estimate first ply failure. In this context, Tsai-Wu criterion is used as the objective function for the optimization of the fibre orientations in the laminate. The PGD solution provides also sensitivities for a gradient-based optimization algorithm. The potentiality and efficiency of the presented approach is demonstrated through some numerical tests. [ABSTRACT FROM AUTHOR]

Throughout history, humans have observed living or non-living things in nature and then imitated them in relation to these observations. This is due to the fact that the energy found in nature is generally consumed at an optimal level in order for it to endure. Biomimetic inspiration in many designs and applications is widely displayed, including within the field of engineering. In this paper, we were inspired by the double set of jaws found in the moray eel, which gives this fish a huge advantage while hunting, with a mobile pharyngeal jaw that works together with its oral jaw in order to overcome ineffective suction capabilities. A procedure that mimics the hunting motion of the moray eel was utilized by considering the overall movement as a single degree of freedom with multiple outputs on account of the repeating motion that is required during hunting. This procedure includes structural and dimensioning synthesis, wherein the latter was utilized with analytic kinematic synthesis for each linkage transfer. The flexibilities in parameters were taken into account with a novel multiple iterative kinematic synthesis algorithm that resulted in various mechanisms with the same purpose. Among the excessive number of resultant mechanisms, the optimization was carried out by considering the highest torque transmission ratio at critical timings that were specified as bio-constraints. In the end, the kinematic movement validation was utilized. [ABSTRACT FROM AUTHOR]

With the increasing of the data size and the development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and also makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates at all iterations. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both strongly convex and non-strongly convex problems, while existing asynchronous algorithms (e.g., KroMagnon and ASAGA) only have convergence guarantees for strongly convex problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical experimental results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++. [ABSTRACT FROM AUTHOR]

With the increasing awareness of the importance of environmental protection and the fierce competition in the construction machinery market, improving the vibration comfort of a whole construction machine has become a new focus of competition; therefore, optimizing the performance of cab mounts has become an urgent problem to be solved. At present, the problems of low modeling efficiency, serious technical difficulties, and long development cycles exist in the design and optimization of cab mounts. In this paper, a multi-target regression forests method is introduced into the design and optimization of the construction machinery installation system, which circumvents the traditional complex modeling process and establishes a mapping relationship between cab assembly parameters and the mounts' stiffness, as well as introduces the system decoupling rate and vibration isolation rate as the boundary conditions. Furthermore, the MRFs method is compared and evaluated with MLRP and Multi-SVR prediction results. Finally, a complete, accurate, and efficient design method for the cab mount system optimization is developed, improving the decoupling rate and vibration isolation rate of the cab system. This design method can predict the stiffness of the mounts in multiple directions. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms (MAs) are used to find the answers to NP-Hard problems. NP-Hard problems basically refer to a set of optimization problems that cannot be solved in a polynomial at a time. MAs try to find the optimal or near-definitive answer in the shortest possible time to solve such problems and a set of optimization algorithms with different origins. These algorithms may be inspired by the natural sciences, physics, mathematics, and political science. However, a particular Metaheuristic algorithm may not provide the best answer to all problems. Each MA may have a better response to specific problems than other similar algorithms. Therefore, researchers will try to introduce and discover new algorithms to find optimal answers to a wide range of problems. In this paper, a new Meta-heuristic algorithm called the Water optimization algorithm (WAO) is presented. WAO is inspired by the chemical and physical properties of water molecules. The main idea of the proposed algorithm is to link water molecules together to find the optimal points. Factors such as particle motion, particle evaporation, and particle bonding have created a mechanism based on swarm intelligence and physical intelligence that inspired this algorithm to solve persistent problems. In this algorithm, answers are defined as a water molecule, a set of them is defined as a local answer. Water bonds provide the right move towards the optimal response. In evaluating the performance of the proposed algorithm, the proposed method is applied to some standard functions and some practical problems. The results obtained from the experiments show that the proposed algorithm has provided appropriate and acceptable answers in terms of execution time and accuracy compared to some similar algorithms. [ABSTRACT FROM AUTHOR]

One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the state-of-the-art optimization algorithms are capable of approximating the shape of many-objective PF by generating a limited number of non-dominated solutions. The exponential increase of the population size is an inefficient strategy that increases the computational complexity of the algorithm dramatically—especially when solving many-objective problems. In this paper, we introduce a machine learning-based framework to cover sparse PF surface which is initially generated by many-objective optimization algorithms; either by classical or meta-heuristic methods. The proposed method, called many-objective reverse mapping (MORM), is based on constructing a learning model on the initial PF set as the training data to reversely map the objective values to corresponding decision variables. Using the trained model, a set of candidate solutions can be generated by a variety of inexpensive generative techniques such as Opposition-based Learning and Latin Hypercube Sampling in both objective and decision spaces. Iteratively generated non-dominated candidate solutions cover the initial PF efficiently with no further need to utilize any optimization algorithm. We validate the proposed framework using a set of well-known many-objective optimization benchmarks and two well-known real-world problems. The coverage of PF is illustrated and numerically compared with the state-of-the-art many-objective algorithms. The statistical tests conducted on comparison measures such as HV, IGD, and the contribution ratio on the built PF reveal that the proposed collaborative framework surpasses the competitors on most of the problems. In addition, MORM covers the PF effectively compared to other methods even with the aid of large population size. [ABSTRACT FROM AUTHOR]

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

Concentric circular antenna arrays (CCAAs) can obtain better performance than other antenna arrays. However, high overhead and excessive sidelobes still make its application difficult. In this paper, we consider the sparse synthesis optimization of CCAAs. Specifically, we aim to turn off a specific number of antennas while reducing the sidelobe of CCAAs. First, we formulate an optimization problem and present the solution space. Then, we propose a novel evolutionary method for solving the optimization problem. Our proposed method introduces hybrid solution initialization, hybrid crossover method, and hybrid update methods. Simulation results show the effectiveness of the proposed algorithm and the proposed improvement factors. [ABSTRACT FROM AUTHOR]

The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method's efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique's issue of predicting the accurate clusters' number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark's parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters' number automatically based on the dataset's structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%. [ABSTRACT FROM AUTHOR]

Since the increases in electricity demand, environmental awareness, and power reliability requirements, solutions of single-objective optimal power flow (OPF) and multi-objective OPF (MOOPF) (two or three objectives) problems are inadequate for modern power system management and operation. Solutions to the many-objective OPF (more than three objectives) problems are necessary to meet modern power-system requirements, and an efficient optimization algorithm is needed to solve the problems. This paper presents a many-objective marine predators algorithm (MaMPA) for solving single-objective OPF (SOOPF), multi-objective OPF (MOOPF), and many-objective OPF (MaOPF) problems as this algorithm has been widely used to solve other different problems with many successes, except for MaOPF problems. The marine predators algorithm (MPA) itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to the MPA in this work. The considered objective functions include cost, emission, transmission loss, and voltage stability index (VSI), and the IEEE 30- and 118-bus systems are tested to evaluate the algorithm performance. The results of the SOOPF problem provided by MaMPA are found to be better than various algorithms in the literature where the provided cost of MaMPA is more than that of the compared algorithms for more than 1000 USD/h in the IEEE 118-bus system. The statistical results of MaMPA are investigated and express very high consistency with a very low standard deviation. The Pareto fronts and best-compromised solutions generated by MaMPA for MOOPF and MaOPF problems are compared with various algorithms based on the hypervolume indicator and show superiority over the compared algorithms, especially in the large system. The best-compromised solution of MaMPA for the MaOPF problem is found to be greater than the compared algorithms around 4.30 to 85.23% for the considered objectives in the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

With the increasing importance of the platform service supply chain (PSSC) in creating economic value, academic research is paying more and more attention to it. The current literature's research topics and problems cover broad areas. This review adopts bibliometric analysis and thematic analysis to review the related literature systematically and comprehensively. We divided the literature about PSSC into six groups according to the literature's research topic and research question. Each literature's research problem and research method are categorized and summarized. Our review results demonstrate that the supply chain's members' operational decisions and the supply chain's coordination are two main types of research questions. Pricing decisions have received the most attention. In terms of the research method, game models are the most common method used in research to achieve the optimization of the PSSC. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush–Kuhn–Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes. [ABSTRACT FROM AUTHOR]

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan. [ABSTRACT FROM AUTHOR]

Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system. [ABSTRACT FROM AUTHOR]

Face milling of AISI 2062 steel was applied an optimization technique by ANOVA based on optimal factorial method is proposed in this paper. Which cover dynamic behavior of facemill with different shim and cutting condition for reducing chatter by amplitude ratio method. To illustrate the effectiveness of this approach Experimental results are provided. Cutting tools Damping and stability of is very much essential now a day in VMC Milling Centre operations. During the experimental work we have done experiment with carbide shim followed by without shim and Stainless Steel shim to see drop in chatter. An experimental methodology was developed using the DOE (Design of experiment) technique. The optimal factorial method was used DOE for four factors having three levels. The main results are based on amplitude ratio and ANOVA analysis. Analysis of variance (ANOVA) was used to determine the effects of the cutting condition on vibration amplitude ratio in X, vibration amplitude ratio in Y. This is useful for evaluating This is useful for evaluating the stability of milling operations via time domain FFT and it validates with ANOVA by design expert 8.0.6. It is found that the most critical parameter in this study is Shim material. At high cutting speed and feed carbide shim and low and medium cutting speed and feed SS shim represent good result for reducing chatter. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimized design for a 75 kW, 24 000 r/min high-speed surface-mounted permanent magnet synchronous motor (SMPSM) for a magnetically levitated flywheel energy storage system. The main goal of the optimization process is to determine the optimal motor geometry, and thus obtain maximum efficiency, minimum weight, and minimum torque ripple in the SMPSM. To achieve this goal, three objective functions are used. The first function is used to obtain maximal output torque, and thus produce highest efficiency; the second function is used to minimize materials utilization to realize the lowest possible production cost; and the third function is used to achieve minimal torque ripple, and thus constrain the motor’s vibration and noise. The optimization process uses an ant colony algorithm with continuous domains. The optimized motor parameters can be selected from the Pareto solution set obtained. A comparison of the results calculated using the optimization algorithm with the results obtained using finite-element analysis software shows a good agreement. [ABSTRACT FROM AUTHOR]

This paper presents an online maximum torque per ampere (MTPA) control that considers both magnetic saturation and cross-magnetization effects of interior permanent-magnet synchronous machines (IPMSMs). For IPMSM drives, especially in torque-controlled applications, torque accuracy and high-efficiency operations are important issues. They can be dealt as a constrained optimization problem to satisfy both torque reference tracking and loss-minimizing operation. In this paper, nonlinear simultaneous equations are derived from Lagrange multiplier method, which could be solved by numerical algorithms. Among them, Levenberg–Marquardt algorithm (LMA) is employed to guarantee a robust calculation of optimal current references. It is optimized to alleviate calculation burden while maintaining the stability of the proposed algorithm. In addition, a torque reference limiter is implemented to satisfy a current limit in real time. The feasibility of the proposed method is verified under various operating conditions by simulation and experimental results. Through the proposed algorithms, accurate MTPA control is achieved under not only unsaturated but also highly saturated operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes dot sensitivity analysis of a current region for topology optimization. A continuum shape sensitivity formula is employed to derive a dot sensitivity formula in a simple closed form. The derived dot sensitivity formula provides the information on where the current dots should be created for topology optimization. This is necessary to generate the current dots in a vacant region of the design domain. Because this dot sensitivity analysis can obtain various topologies of material distribution, the possibility of local optimum convergence is reduced. Furthermore, because this dot sensitivity analysis does not require a specific initial design, its numerical implementation is simple. This dot sensitivity method is combined with the level set method to perform topology optimization. To demonstrate the usefulness of the proposed method, it is applied to two optimization problems of magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Contactors are typical electromagnetic devices whose optimization is multi-objective optimization problems. Contactors’ static and dynamic characteristics obtained by the finite-element method (FEM) are typical objectives. Due to the low efficiency of FEM, the intelligent optimization algorithms that require multiple computations cannot be applied well here. In this paper, a fast algorithm for characteristics of contactors based on radial basis function neural network approximate model is proposed. The orthogonal least squares, data pre-processing and subsection modeling methods are adopted here to make the model more accurate. Meanwhile, a hybrid multi-objective optimization algorithm which combines the differential evolution and the particle swarm optimization is also proposed to speed up convergence and obtain the limit optimal solutions. The feasibility of the above-mentioned methods has been verified by solving a multi-objective optimization problem of the contactor with solenoid structure. [ABSTRACT FROM AUTHOR]

In view of Internet of Energy, advanced operational optimization in microgrid energy management system (MEMS) is expected to be scalable to accommodate various participants, support plug-and-play, and optimize energy utilization. Based on potential game, this paper presents a fully distributed operational optimization for MEMS with high penetration of renewable energy and demand response. After analyzing the microgrid and potential game, the establishment and proof of an exact potential game model is presented in detail. Then the best-strategy-response iterative algorithm is proposed to find the game Nash equilibrium in a distributed way. Finally, the proposed approach is simulated in an islanded microgrid to verify its efficiency and feasibility. The significances of this paper are as follows. First, the scheme and algorithm are fully distributed. All heterogeneous individuals are treated as independent decision-making entities without a central coordinator/processor. Second, the optimization process is open and dynamic. The convergence is achieved with no limits to type or number of players, even if the process of execution is corrupted with delay or losses in communication information. Third, the consistency between individual rationality and overall importance is guaranteed, and individual best-strategy-response behaviors necessarily improve overall optimality. [ABSTRACT FROM AUTHOR]

In this paper, an optimal design procedure forLCC-series compensation network is proposed for a stationary wireless electric vehicle charger. The main focus of this paper is to optimize the resonant network suitable for a wide range of operation from no-load to full-power operation. The conventional methods only consider the full-load condition to design the resonant network; in contrast, the proposed method employs a time-weighted average efficiency for different coupling conditions to achieve high efficiency over a wide load range including light-load and no-load operation. The resonant network is tuned to realize zero voltage switching for the primary side inverter. Moreover, a finite-element analysis is performed to calculate self- and mutual inductances as well as core losses for magnetic couplers. In order to validate the feasibility of the proposed design, a 1 kW/85 kHz prototype with circular magnetic couplers is implemented. According to simulations and experiments, flat profiles for both efficiency and output voltage against output power variations are achieved. Experimental results demonstrate a 94.8% peak efficiency for the full-load operation. [ABSTRACT FROM AUTHOR]