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This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

Robust optimization over time is a new way to tackle dynamic optimization problems where the goal is to find solutions that remain acceptable over an extended period of time. The state-of-the-art methods in this domain try to identify robust solutions based on their future predicted fitness values. However, predicting future fitness values is difficult and error prone. In this paper, we propose a new framework based on a multipopulation method in which subpopulations are responsible for tracking peaks and also gathering characteristic information about them. When the quality of the current robust solution falls below the acceptance threshold, the algorithm chooses the next robust solution based on the collected information. We propose four different strategies to select the next solution. The experimental results on benchmark problems show that our newly proposed methods perform significantly better than existing algorithms. [ABSTRACT FROM AUTHOR]

Operating in the high-speed range is necessary for high-performance permanent-magnet synchronous motor (PMSM) drives. However, due to the back electromotive force effect, the PMSM is approaching the voltage limit at field decreasing scope. This paper presents a new flux-weakening scheme along with an improved vector control strategy to alleviate the influence of this problem. Control parameters of the anti-windup proportional and integral (AWPI) controller are optimized off-line in relying on an adaptive velocity particle swarm optimization (AVPSO) algorithm. The AVPSO algorithm considers the summation of AWPI measurement error which is the objective function of the optimization problem without knowing the transfer function of the plant. Hence, the tuned flux-weakening controller with a filter is used to set the flux level without saturating the current controllers. Meanwhile, the other controllers of inner and outer loops award a great dynamic and steady-state performance for the PMSM. In the proposed scheme, the flux-weakening control is not dependent on machine parameters that adapts the flux level automatically and provide a fast transition between the constant torque region and the field-weakening region. Effectiveness and advantages of the proposed scheme are presented in this paper through both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Electromagnetic devices containing permanent magnet (PM) are common and typical electromagnetic devices that have advantages of low-power consumption, small size, high sensitivity, and its optimization problem has aroused widespread concern. Calculation accuracy of electromagnetic properties and efficiency of optimization processes are two important factors that affect the optimization effect of electromagnetic devices. A fast calculation method was proposed in this paper that aimed at electromagnetic devices with PM characteristics of strong non-linearity and low solving precision. The response surface method was used to derive the basis function, and the error between actual measured values and calculated values was modified by the Kriging method. On the basis of the calculation model, the distributed collaborative strategy was adopted to fulfill the parallel execution of the fast computation. Simultaneously, the updating strategy of particles was improved, and the robustness of the multi-objective particle swarm optimization algorithm in solving the optimization problem of electromagnetic devices with PM was enhanced. Effectiveness of the method proposed in this paper was verified by the case study of a specific type of electromagnetic relay with PM. [ABSTRACT FROM AUTHOR]

During the running of electric locomotives, a large amount of operating data will be stored through the Analog-Digital Converter (ADC) and the recording oscillograph of the on-board Traction Control Unit (TCU). Analysis based on mass data to detect slipping is of great significance for saving energy, improving the performance and safety of the traction control system of electric locomotives. Therefore, after analyzing and summarizing the advantages of Empirical Wavelet Transform (EWT) and Fuzzy Entropy (FE) algorithm in data processing and the classification theory of Support Vector Machine (SVM), this paper proposed a SVM slipping detection method based on EWT and FE algorithm. Firstly, feature extraction is carried out on the locomotive wheelset velocity data collected by the on-board velocity sensor, that is, the feature vector of data is extracted by calculating fuzzy entropy value after EWT, and the velocity data is converted into feature matrix for slipping-detection of SVM. Then considering the defects of traditional optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the parameter optimization of SVM, Genetic Particle Swarm Optimization Algorithm (GAPSO) is proposed to optimize the parameters of SVM classification model, and the slipping detection model of SVM is established through training samples, which realizes high-precision slipping detection. Finally, combined with a large number of locomotive data, the comparative analysis test is carried out, the results show that the feature extraction method based on EWT and FE algorithm has excellent ability of mode decomposition and feature extraction, GAPSO has efficient optimization ability, and the proposed slipping detection method can accurately detect slipping. [ABSTRACT FROM AUTHOR]

As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications. [ABSTRACT FROM AUTHOR]

To drive the machine tool along a curve, a commercial computer-aid design and computer-aided manufacturing system usually breaks the curve into a set of line segments. This paper provides a systematic way for generating smooth and fast machining velocity planning for contouring line segments in a machine tool. It is especially useful for mass manufacturing. In this paper, the particle swarm optimization algorithm is employed to optimize the design parameters. The algorithm allows for as many motion limits as possible, not only the limits along the path, but those on the axes, as well. It can automatically determine the existence of acceleration, constant speed, and deceleration zones of the S-curve velocity profile. Also, it can determine the optimal corner speed with a given corner error. The computation time of the algorithm is fast so that it allows for online operation. Simulation and experimental results demonstrate that machine tools using the proposed algorithm go fast where possible and slow down where needed, dramatically increasing the productivity. [ABSTRACT FROM AUTHOR]

In this paper, the downlink of a clustered coordinated multicell multiuser multiple-input multiple-output (MIMO) system is considered, and various low-complexity user scheduling algorithms to maximize the sum rate are proposed. The users' data signals are transmitted cooperatively via the base stations (BSs) of a cluster within the sectorized multicell heterogeneous cellular network. Exhaustive search as the optimal scheduling method is extremely complex, particularly for large numbers of users requesting service. We therefore propose suboptimal scheduling algorithms based on the lower complexity metaheuristic stochastic optimization techniques of simulated annealing (SA) and particle swarm (PS). Moreover, a hybrid algorithm combining the traits of PS and a greedy scheduler is also proposed. The performance and complexity of these algorithms are evaluated for a heterogeneous system model employing different precoding methods. Assessing the algorithms' performance via computer simulations, the effectiveness of the proposed schemes in terms of sum rate is demonstrated, yielding performance very close to that of the exhaustive search at much lower complexity. For each precoding method used in the employed system model, the best corresponding approach to scheduling from our proposed algorithms is determined. [ABSTRACT FROM AUTHOR]

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators. [ABSTRACT FROM AUTHOR]

This paper investigates resource provisioning in cyber-physical systems (CPSs) by developing a new definition of anelasticity. A flat semi-dormant multicontroller (FSDMC) model is established on a special type of CPS platform named arbitrated networked control system with dual communication channels. A novel, quantitative, and formal definition of anelasticity for the FSDMC is proposed. A new finite capacity M/M/c queuing system with ${N}$ -policy and asynchronous multiple working vacations of partial servers is established, and the FSDMC is modeled as a quasi-birth-and-death process to obtain the stationary probability distribution of the system. Based on the queueing model, we quantify various performance indices of the system to build a nonlinear cost-performance ratio (CPR) function. An optimization model is presented to minimize the CPR. A particle swarm optimization (PSO) algorithm is used to find the optimum solution of the optimization model and obtain the optimal configuration values of the system parameters under stability condition. By changing the system parameters, the sensitivity of the system performance indices and the CPR are analyzed, respectively. The unexpected workload varies randomly over time. Thus, an M/M/1/K queue is constructed in a Markovian environment by employing a three-state, irreducible Markov process. In this queue, the conditional average queue length and the probabilities of the three-state process are calculated. Then, the anelasticity value of the system is precisely determined. When the average arrival rate exceeds the average service rate in the queueing system, an optimal CPR unchanged adaptive algorithm based on PSO is designed to dynamically adjust the controller service rate. Extensive numerical results show the usefulness and effectiveness of the proposed techniques and exhibit that the system can maintain elastic invariance in adaptive adjustment parameters. [ABSTRACT FROM AUTHOR]

This paper presents a technique based on the novel bat algorithm (NBA) for the design of the parameters of power system stabilizers and the generalized Unified power flow controller-power oscillation damping in a multimachine power system. The objective is to ensure minimal damping rates to low-frequency electromechanical oscillation modes. Simulations were carried out on the New England system to compare the performance of the NBA with three other techniques: particle swarm optimization, bacterial foraging optimization oriented by particle swarm optimization, and the bat algorithm. For the cases analyzed in this paper, the NBA produced results superior to other techniques. Moreover, the proposed methodology was able to obtain solutions with high damping levels, which showed to be robust when a wide range of loading conditions of the power system were considered. [ABSTRACT FROM AUTHOR]

The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size ($n$ $ >$ 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the development of a controller for a fleet of unmanned aerial vehicles based on a distributed path planning strategy under a multiagent systems framework. The issue, treated as an online optimization problem, is solved using a particle swarm optimization (PSO) algorithm. The proposal was validated in experiments, considering different scenarios like fixed and mobile targets, external disturbances, and the loss of an agent. The proposed PSO is implemented independently in each vehicle in order to determine, by minimizing a cost function, the best paths that ensure the fleet formation control, target tracking, and collision avoidance. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient reliability-based optimization algorithm based on adaptive dynamic Taylor Kriging (ADTK). In the ADTK model, the basis functions are optimally selected by the binary particle swarm optimization algorithm and the minimal number of sampling data with a desired fitting error is decided. To evaluate performance of reliability calculation, the Monte Carlo simulation method is employed, where the performance constraint functions are constructed by the ADTK surrogate model. Overall, an electromagnetic application-superconducting magnetic energy storage device (TEAM problem 22) is used to investigate performances of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an optimal design for nuclear magnetic resonance and magnetic resonance imaging based multiphase flow meter is presented. The overall apparatus consists of an electromagnetic coil surrounded by 12 rings of Halbach arrays of 12 magnets each. Both the coil and the magnets arrays were simultaneously and optimally designed using a three-dimensional (3-D) finite-element method (FEM) package and particle swarm optimization algorithm. The aim of the design is to uniformly prepolarize the hydrogen spins of the atoms composing the flowing fluid, with a predefined static magnetic field, before they enter into the measurement area within which they are homogenously polarized with a perpendicular ac magnetic field. Cheap and lightweight hardware are the other two design parameters of the design. Results of extensive 3-D simulations of the design indicate that an optimized and homogenous static magnetic field distribution could be achieved within an area of 40 mm diameter and 606 mm length. In addition, using a multiturn coil of 6 cm diameter and 2000 turns, an ac magnetic field of 13 mT amplitude and 112 ppm homogeneity could be achieved, which is enough to handle fluids flowing at speed of up to 2 m/s. Experimental validation, which was done using newly constructed two Halbach arrays of cuboid and trapezoid magnet elements, respectively, indicates a good match with FEM simulations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive synthesis using the particle swarm optimization (PSO) for implementations of resonator-coupled filters is proposed. The coupling matrix of in-band filtering response is achieved and optimized by the PSO-based synthesis. Meanwhile, the stopband coupling matrix of the filter is predicted based on the combination of extra resonant nodes representing stopband spurious and parasitic effect of input/output ports. In addition, an enhanced accurate prediction of filter performance is calculated by the proposed approach, considering the practical fabrication tolerance on filter design. To verify principles mentioned earlier, various resonator-coupled filters are implemented. The calculation, EM-simulation, and measurement of filters show good agreements in both passband and stopband. [ABSTRACT FROM AUTHOR]

Optimally designed arrays are used to obtain high gain and directivity. Based on the desired application, different radiation patterns can be generated. These patterns have generally a main beam and some sidelobes. These last ones are undesirable because they can cause electromagnetic interference and compatibility issues. Therefore, sidelobe levels (SLLs) reduction is one of the most important factors for the design of antenna arrays and arrays must be designed in a way to reduce SLLs. In this paper, the optimal design of circular antenna arrays (CAA) for maximum SLLs reduction is investigated using a newly developed metaheuristic that is the most valuable player algorithm. This algorithm is inspired from sport and it has been proven to be an excellent optimization algorithm for mathematical benchmarks. Three cases with different number of arrays are investigated. For comparison purposes, ten other well-known optimization algorithms are used. The design results obtained show the superiority of the proposed algorithm for the optimal design of CAA and for sidelobe reduction over many other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel memetic algorithm, which is explorative particle swarm optimization (ePSO), combined with mesh adaptive direct search and apply it to the design of a permanent magnet synchronous machine (PMSM). The ePSO, which is modified from the PSO, drastically improves search time and iteration number at an exploration search stage. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. By applying the proposed algorithm to PMSM, we clarify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Metaheuristics have recently become the forefront of the current research as a powerful way to deal with many electromagnetic optimization problems. Based on the simulation of the herding behavior of krill individuals, a krill herd (KH) algorithm was recently proposed to solve optimization problems. In order to extend the classical mono-objective KH algorithm approach, this paper proposes a new multiobjective KH (MOKH) algorithm and a modified MOKH approach using the beta distribution in the inertia weight tuning. Numerical results on a multiobjective constrained brushless direct current motor design problem show that the evaluated MOKH algorithms present a promising performance. [ABSTRACT FROM AUTHOR]

Nonlinear model predictive control (NMPC) requires a repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of the NMPC technique, particularly for fast systems. This paper presents a fast NMPC algorithm implemented on a field-programmable gate array (FPGA) that employs a particle swarm optimization (PSO) algorithm to handle nonlinear optimization. The FPGA is used to explore the possibilities of parallel architecture for the substantial acceleration of NMPC. PSO is employed to achieve real-time operation due to its naturally parallel capabilities. The proposed FPGA-based NMPC-PSO controller consists of a random-number generator, a fixed-point arithmetic, a PSO solver, and a universal asynchronous receiver/transmitter communication interface. Then, this controller is applied to an engine idle speed control problem and demonstrated with an FPGA-in-the-loop testbench. The experimental results indicate that the NMPC-on-FPGA-chip strategy has good computational performance and achieves satisfactory control performance. [ABSTRACT FROM AUTHOR]

The purpose of this tutorial paper is to make general type-2 fuzzy logic systems (GT2 FLSs) more accessible to fuzzy logic researchers and practitioners, and to expedite their research, designs, and use. To accomplish this, the paper 1) explains four different mathematical representations for general type-2 fuzzy sets (GT2 FSs); 2) demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious; 3) shows how to obtain set theoretic and other operations for GT2 FSs using type-1 (T1) FS mathematics (\alpha - cuts play a central role); 4) reviews Mamdani and TSK interval type-2 (IT2) FLSs so that their mathematical operations can be easily used in a GT2 FLS; 5) provides all of the formulas that describe both Mamdani and TSK GT2 FLSs; 6) explains why center-of sets type-reduction should be favored for a GT2 FLS over centroid type-reduction; 7) provides three simplified GT2 FLSs (two are for Mamdani GT2 FLSs and one is for a TSK GT2 FLS), all of which bypass type reduction and are generalizations from their IT2 FLS counterparts to GT2 FLSs; 8) explains why gradient-based optimization should not be used to optimally design a GT2 FLS; 9) explains how derivative-free optimization algorithms can be used to optimally design a GT2 FLS; and 10) provides a three-step approach for optimally designing FLSs in a progressive manner, from T1 to IT2 to GT2, each of which uses a quantum particle swarm optimization algorithm, by virtue of which the performance for the IT2 FLS cannot be worse than that of the T1 FLS, and the performance for the GT2 FLS cannot be worse than that of the IT2 FLS. [ABSTRACT FROM AUTHOR]

This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method to determine the sizing and siting of diesel generators (DGs), photovoltaic (PV) solar panels, and batteries for off-grid systems. In this work, the objective is to reduce the total system cost while fulfilling the load demand and maintaining the power quality of the system, among other constraints. This is achieved by employing a two-stage particle swarm optimization algorithm; the first for siting and sizing of the system and the second for its scheduling. Three case studies, namely only DGs, DGs + PV, and DGs + PV + batteries systems, based on the energy requirement of an Indonesian island are presented. In addition, real solar irradiance and temperature data are utilized together with a measured load profile. The results reveal that a system that relies only on DGs is not able to keep the system voltage within the tolerable range (from 0.95 to 1.05 p.u.), while yielding a lifetime cost of 104 million USD. The DGs + PV system requires a lower lifetime cost, 67 million USD. Nevertheless, the voltage of the system reaches values lower than 0.95 p.u. for certain nodes during night-time as the PV are not able to provide power. Finally, the DGs + PV + batteries system yields the lowest cost, 64 million USD, and is able to keep the values of the voltage within the desired range, as batteries can provide power when PV are not operating. [ABSTRACT FROM PUBLISHER]

The race to increase the efficiency and reduce the power losses in transmission systems has resulted in the substantial growth of high-voltage direct current (HVDC) transmission systems. Moreover, the interconnection of these transmission systems significantly increases their reliability. However, the control of these meshed grids is a key problem that usually is managed through the control of the VSCs in those grids, but the control of the VSC can be complemented with a reconfiguration algorithm. This paper proposes the use of the particle swarm optimization algorithm, in order to reconfigure meshed HVDC transmission systems and reduce losses. The proposed algorithm has been tested in the CIGRE benchmark grid, which comprises of several offshore wind farms that generate energy sent to the grid through several HVDC transmission lines. The results show that as the energy generation changes due to wind changes, the grid topology must be reconfigured in order to achieve the maximum efficiency. Doing this reconfiguration, power savings around 18–19% could be achieved. [ABSTRACT FROM PUBLISHER]

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode–forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs. [ABSTRACT FROM AUTHOR]

Convex formulations of low-rank matrix factorization problems have received considerable attention in machine learning. However, such formulations often require solving for a matrix of the size of the data matrix, making it challenging to apply them to large scale datasets. Moreover, in many applications the data can display structures beyond simply being low-rank, e.g., images and videos present complex spatio-temporal structures that are largely ignored by standard low-rank methods. In this paper we study a matrix factorization technique that is suitable for large datasets and captures additional structure in the factors by using a particular form of regularization that includes well-known regularizers such as total variation and the nuclear norm as particular cases. Although the resulting optimization problem is non-convex, we show that if the size of the factors is large enough, under certain conditions, any local minimizer for the factors yields a global minimizer. A few practical algorithms are also provided to solve the matrix factorization problem, and bounds on the distance from a given approximate solution of the optimization problem to the global optimum are derived. Examples in neural calcium imaging video segmentation and hyperspectral compressed recovery show the advantages of our approach on high-dimensional datasets. [ABSTRACT FROM AUTHOR]

Synthesis of thinned linear array geometry with low redundancy is an important problem for one-dimensional aperture synthesis radiometers (ASRs) with high spatial resolution and satisfactory sensitivity. In this paper, a synthesis method of low-redundancy linear array (LRLA) with large numbers of antennas is proposed. Two LRLA patterns are suggested to significantly reduce the tremendous solution space of large LRLA search problem and a strategy is proposed to speed up exploration. Further, particle swarm optimization (PSO) algorithm exploiting the suggested patterns and strategy is applied to the array synthesis. The advantages of the proposed method, in terms of both generating more new large LRLA configurations and reducing computational cost for the large LRLA configuration synthesis problem, are demonstrated by comparing its performance with that of the previously proposed methods. Finally, a number of newly found large LRLA configurations for 28–37 elements are also presented. [ABSTRACT FROM PUBLISHER]

This paper proposes an improved maximum power point tracker (MPPT) for photovoltaic (PV) system. The scheme is a hybrid between the adaptive perturb and observe and particle swarm optimization (PSO). The algorithm incorporates the search-skip-judge (SSJ) mechanism to minimize the region within the P−V curve to be searched by the PSO. Furthermore, the PSO performance is enhanced by ensuring that the regions that have been previously explored (by other particle) will not be searched again by (another particle). Thus, the unnecessary movement of particles is minimized—leading to faster convergence. The proposed method is evaluated against four well-known MPPT techniques, namely the modified incremental conductance, the original version of SSJ, the modified cuckoo search, and the hybrid PSO. In addition, an experimental prototype, which is based on PV array simulator is used to verify the simulation. The competing algorithms are tested with a buck-boost converter, driven by the TMS320F240 DSP on the dSPACE DS1104 platform. It was found that the proposed scheme converges to the global maximum power point (GMPP) most rapidly and the GMPP tracking is guaranteed even under complex partial shading conditions. [ABSTRACT FROM AUTHOR]

Temperature rise of an induction machine increases losses and decrease in the efficiency. The temperature rise depends on the machine design. It takes several hours after starting a machine to reach to thermal stability. In most of the in situ efficiency estimation methods, it is required to obtain the operating data of the machine at a thermally stable condition, which needs a long running time of the machine. In this paper, a method based on a particle swarm optimization (PSO) algorithm is proposed, which can estimate the machine efficiency at different loads with thermal stability. The machine operation data at the first 30 min after the start rather than data at a thermal stability condition are used in the method. The proposed algorithm utilizes two approaches to predict a full-load temperature at a thermally stable condition. The first approach is based on the insulation class of the machine and uses the equivalent circuit. The second approach is based on the trend of the temperature rise in the first 30 min of running the machine after the start. Furthermore, a method is proposed to narrow the parameters range, which helps the PSO to converge to the right answer. All results are validated by the experimental results. [ABSTRACT FROM AUTHOR]

Magnetic tracking technology is emerging to provide an occlusion-free tracking scheme for the estimation of full pose (position and orientation) of various instruments. This brings substantial benefits for intracorporeal applications, such as for tracking of flexible or wireless endoscopic devices, and thus is significant for further computer-assisted diagnosis, interventions, and surgeries. Toward efficient magnetic tracking, a 6-D magnetic localization and orientation method is proposed in this paper. An annular permanent magnet is mounted on the exterior surface of a capsule. With a magnetic sensor array, the magnetic field can be measured and the capsule’s 3-D location and 3-D orientation information can be estimated based on proposed closed-form analytical model of annular magnet and particle swarm optimization algorithm. Magnetic dipole model and Levenberg–Marquardt algorithm are used to improve the speed and accuracy of estimation. Extensive simulation experiments show that the localization and orientation method works well with good position and orientation accuracy. [ABSTRACT FROM AUTHOR]

Over the past few decades, a plethora of computational intelligence algorithms designed to solve multiobjective problems have been proposed in the literature. Unfortunately, it has been shown that a large majority of these optimizers experience performance degradation when tasked with solving problems possessing more than three objectives, referred to as many-objective problems (MaOPs). The downfall of these optimizers is that simultaneously maintaining a uniformly-spread set of solutions along with appropriate selection pressure to converge toward the Pareto-optimal front becomes significantly difficult as the number of objectives increases. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs with a large number of decision variables. In this paper, insight is given into the current state of many-objective research by investigating scalability of state-of-the-art algorithms using 3–15 objectives and 30–1000 decision variables. Results indicate that evolutionary optimizers are generally the best performers when the number of decision variables is low, but are outperformed by the swarm intelligence optimizers in several large-scale MaOP instances. However, a recently proposed evolutionary algorithm which combines dominance and subregion-based decomposition is shown to be promising for handling the immense search spaces encountered in large-scale MaOPs. [ABSTRACT FROM PUBLISHER]

Considering the advantages of contactless fault diagnosis, a novel sound based fault diagnosis method for railway point machines (RPMs) is proposed. Firstly, a denoising method based on empirical mode decomposition (EMD) is proposed. The useful intrinsic mode functions (IMFs) are selected using kurtosis and energy criteria to reconstruct the denoised signal. Then, multi-scale fractional permutation entropy (MFPE) is proposed inspired by fractional calculus, which is more powerful than the classical multi-scale permutation entropy (MPE). And a two-scale algorithm is developed to avoid neglecting the information contained at the end of the signals in the coarse-graining process. Finally, the feasibility and superiority of the proposed method (D-FMPE-T) based on denoising method and two-scale algorithm are verified using support vector machine optimized by particle swarm optimization (PSO-SVM) through comparing with some commonly used feature extraction and classification methods. Besides, CWRU is also utilized for verification of the superiority of the proposed method. Experiment results show that the proposed method performs best. The identification accuracies on normal-reverse and reverse-normal switching processes, and CWRU data sets reach 99.3%, 99%, and 99.17% respectively, demonstrating the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel magnetically levitated coreless planar motor with three-layer orthogonal overlapping windings is shown to have higher power density and higher space utilization compared with other coreless planar motors. In order to achieve maximum forces with minimum cost and minimum space, a multiobjective optimization of the novel planar motor is carried out. In order to reduce the computational resources required for finite-element (FE) analyses, a fast but accurate analytical tool is developed, based on expressions of the flux density of the permanent-magnet array, which are derived from the scalar magnetic potential method. The validity and accuracy is verified by 3-D FE results. Based on the force formulas and the multiobjective function derived from the analytical models, a particle swarm optimization (PSO) algorithm is applied to optimize the dimensions of the planar motor. The design and optimization of the planar motor is validated with experimental results, measured on a built prototype, thus proving the validity of the analytical tools. [ABSTRACT FROM PUBLISHER]

Along with the increasing capability of consumer electronic devices, it becomes the new trend that the home cloud computing takes household consumer electronic devices as a sharing resource pool, which can bring great convenience to consumers. However, how to find the most suitable resources or services in the home cloud is a challenge for the consumers. When offloading a complicated application to the home cloud, each task of the application can be executed individually with its own computation, storage and bandwidth requirements. The heterogeneous consumer electronic devices that contain different performance metrics affect the destination choice. This paper proposes a distributed home-to-home cloud infrastructure where volunteer users allow the resources of their consumer electronic devices to be shared with the neighboring houses. By extending the home gateways, a decentralized controlling is responsible for resource monitoring, orchestration, task offload and migration. With consideration of load balance and volunteers' opinion, the resource orchestration is formulated as an optimization problem with one objective and multi-constraints. Finally, a Particle Swarm Optimization (PSO)-based algorithm is used to obtain the approximate optimal solution. Simulation results show that the solution for all cases studied almost achieves 90% objective value in acceptable time1. [ABSTRACT FROM PUBLISHER]

Partial discharge (PD) localization is an effective way to detect the insulation problem of power equipment. Considering the environmental adaptability and low hardware cost, a novel ultrahigh frequency (UHF) PD localization algorithm based on received signal strength indication (RSSI) fingerprint is proposed. The methodology includes two stages: in the offline, the RSSI fingerprint map is established by filed test and the BP neural network is trained. Particle swarm optimization (PSO) algorithm is also adopted to choose the initial weight of the BP neural network. In the online stage, the PD sources localization which consists of two steps is deployed. Firstly, a preliminary localization achieved by trained BP neural network. Secondly, more accurate localization result is obtained using compressive sensing (CS) algorithm. Furthermore, since the UHF signal is sensitive to the layouts change of detection area, it is necessary to update the fingerprint map timely from practical application point of view, which is also a time-consuming process. To reduce the workload of fingerprint map updating, a reconstruction algorithm based on CS theory is proposed, by which the fingerprint map can be rebuilt by only a subset of original fingerprint data. A filed experiment in the substation is performed and the results show that, the average localization error of the CS algorithm is 0.89 meter and 90.4% localization errors are less than 2 meters. The results proved the accuracy and effectiveness of our proposed localization algorithm. [ABSTRACT FROM AUTHOR]

Auditing the energy consumption in urban rail is vital for energy consumption evaluation and system parameter design. There are multiple ways to audit energy consumption, but a universal and global approach is missing. The system-level traction energy consumption (STEC) is proposed. Compared to the main substation energy consumption (MSEC), STEC is more accurate by eliminating the influence of step-down loads based on field test studies. An optimization parameter designing model is built, which takes the system cost as the optimal object considering the life span of energy feedback system (EFS)s. The modified salp swarm algorithm (MSSA) is proposed as the optimization algorithm. The numerical tests show that MSSA has better converge performance than salp swarm algorithm (SSA) and particle swarm optimization (PSO). The impact factors of STEC are analyzed. Compared with SSA and PSO, the initial value of the MSSA is improved and it evolves faster. Compared with the case that does not take the no-load voltage of rectifiers and start voltage of EFSs as optimal parameters, the composite cost of the case that takes the abovementioned parameters as optimal parameters is 3.49% less. Compared to the system without EFSs, the optimal system with EFSs can save costs by 29.47%. [ABSTRACT FROM AUTHOR]

An optimal transposition of power transformer windings is proposed to minimize the circulating current loss by incorporating co-Kriging into a binary version of particle swarm optimization. The co-Kriging takes most samplings from less accurate but cheap data, while taking only some from accurate but expensive data to increase its fitting accuracy and to reduce the computing cost at the same time. The effectiveness of the proposed algorithm is demonstrated through an analytical example, and applied to an optimal transposition design of a power transformer to minimize the circulating current loss. [ABSTRACT FROM AUTHOR]

An optimization-based confocal algorithm for microwave imaging aimed at medical applications is presented. Due to complexity of human body tissues, microwave signals that enter a human organ from different angles and are reflected at different depths inside that organ face different effective dielectric constants. An accurate estimation of those dielectric constants is vital for an accurate estimation of signal speed within the imaged object and thus accurate resultant images. The traditional confocal algorithm uses one preassumed effective dielectric constant and thus its resultant image is sensitive to that assumption. In the proposed approach, position-dependent dielectric constants are used to generate a highly focused image that accurately maps scatterers within an imaged object. To that end, an objective equation based on the focusing degree of the obtained initial image is created. Then, particle swarm optimization (PSO) uses that equation to find the effective dielectric constants facing signals penetrating the object according to their entrance point. Those optimized effective dielectric constants are used to produce a highly focused image without the risk of creating false-positive targets irrespective of the initial values of the effective dielectric constants. The proposed algorithm is evaluated in head imaging via full-wave electromagnetic simulations and experiments. The simulations are based on using an accurate numerical head model, whereas the experiments are conducted using realistic artificial head phantoms. The obtained images indicate that the quality of the images is significantly improved with more accurate localization of targets in unhealthy cases and less probability of positive false alarms in healthy cases compared with the traditional approach. [ABSTRACT FROM PUBLISHER]

Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability. [ABSTRACT FROM AUTHOR]

This article is concerned with the design of a super-twisting algorithm (STA) based sliding mode controller for permanent magnet synchronous motor (PMSM) speed regulation system under the self-triggered mechanism. By using the strict Lyapunov function approach, it is shown that the tracking error converges to an ultimate domain within the finite-time sense under the proposed self-triggered STA. A feasible self-triggered strategy is designed for both cases with and without external perturbation. Moreover, a nonlinear optimization problem is formulated in terms of the tradeoff between the ultimate domain and the communication burden. The optimized STA gains are obtained by solving the above-formulated optimization problem via a particle swarm optimization algorithm. Finally, the applicability of the proposed self-triggered STA for PMSM is verified by simulation and experiment results. [ABSTRACT FROM AUTHOR]

Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters. [ABSTRACT FROM AUTHOR]

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel $N$ -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions. [ABSTRACT FROM AUTHOR]

In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. Aiming at maximizing the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to make decisions on content placement, content delivery, and UAV trajectory simultaneously. As the decisions interact with each other and the UAV energy is limited, the formulated JCTO problem is intractable directly and timely. To this end, we propose a deep supervised learning scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. In specific, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make fast decisions online. The network density and content request distribution with spatio-temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function which maps the input network information to the output decision can be intelligently learnt to make timely inference and facilitate online decisions. We conduct extensive trace-driven experiments, and our results demonstrate both the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. [ABSTRACT FROM AUTHOR]

The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme’s classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is a newly developed swarm intelligence-based optimization technique that mimics the social hierarchy and group hunting behavior of grey wolves in nature. Here, a detailed introduction of the GWO algorithm is given, after which, three sets of examples are investigated: first, numerical experiments on four benchmark functions are conducted; second, the GWO is applied to the synthesis of linear arrays with the aim of reducing the peak sidelobe level under various constraints; and finally, the performance of the GWO is further verified on the optimization design of two representative antennas, namely, a dual-band E-shaped patch antenna and a wideband magneto-electric dipole antenna. The results show that the GWO is capable of outperforming or providing very competitive results compared with some well-known metaheuristics such as the genetic algorithm, particle swarm optimization, and differential evolution. Thus, it may serve as a promising candidate for handling electromagnetic problems. [ABSTRACT FROM AUTHOR]

This article proposes a modified grey wolf optimization (MGWO) which has improved rate of convergence and avoids local optima stagnation. This is achieved by improving the exploration–exploitation balance in conventional grey wolf optimization (GWO). Moreover, the use of weighted-distance strategy allows quick identification of the global optima in the proposed MGWO. This optimization is then applied for the control of a three-phase, 11-level hybrid cascaded multilevel inverter (HC-MLI). Selective harmonic elimination pulsewidth modulation technique is implemented through MGWO which generates optimal switching angles for the HC-MLI so as to eliminate lower order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In MGWO optimized HC-MLI, balancing of capacitor voltage is made possible at higher modulation index by making use of the available redundant switching states. The performance of the proposed work is validated through simulation and experimentation on a 1.5 kW prototype. The results obtained through simulation show that the MGWO algorithm is more efficient and accurate than other reported algorithms such as GWO, genetic algorithm, and particle swarm optimization in terms of performance, harmonic reduction, and convergence rate. [ABSTRACT FROM AUTHOR]