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Drones and drone swarms, characterized by their low price and ease of deployment, are being utilized to launch assaults, and presenting a great threat to the public and homeland security in recent years. Countering drones or drone swarms is thus of great significance. Some effective counter approaches for a small group of drones have been studied. However, these approaches may be insufficient to counter a large-scale drone swarm due to the lack of efficient time-saving detecting technologies. Towards this end, this paper proposes a fast counter approach to deprive the drone swarm of its coordination and clustering capabilities in a short time by splitting the drone swarm into several unconnected components. To achieve efficient splitting, two efficient algorithms for searching critical nodes are proposed, namely, the genetic algorithm and the particle swarm optimization algorithm. Extensive simulation results are presented to validate the superior performances of the proposed two algorithms for splitting the drone swarms in different formations. The results show that the drone swarms lose their coordination ability as they are forced to split into multiple components with a constraint of group size. The high accuracy and efficiency of the proposed algorithms are also verified by a series of comparative experiments. [ABSTRACT FROM AUTHOR]

This paper investigates a wireless federated learning (FL) network with limited communication bandwidth, where multiple mobile clients train their individual models with the help of one central server. We consider the practical communication scenarios, where the clients should complete the local computation and model upload within a defined latency. By jointly exploiting the dynamic characteristics of wireless channels and computational capability at the clients, we optimize the federated learning network by maximizing the number of active clients under the constraints of both latency and bandwidth. Specifically, we propose two bandwidth allocation (BA) schemes, where scheme I is based on the instantaneous channel state information (CSI), while scheme II employs the particle swarm optimization (PSO) method, based on the statistical CSI. Simulation results on the test accuracy and convergence rate are finally provided to demonstrate the advantages of the proposed optimization schemes for the considered FL network. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) control has been widely used in grid-connected inverter to effectively increase the equivalent inertia of power system. However, the active power oscillation similar to electromechanical transient characteristics of conventional synchronous generator (SG) is introduced to power grid, especially under the condition of multi-virtual synchronizer. In this paper, the dynamic stability of the multiple grid-connected voltage source converters (VSC) controlled by VSG is studied. Firstly, the system linearization model is established to analyze the oscillation risk. In order to improve the damping characteristics of multi-VSG grid-connected systems, an additional power system stabilizer (PSS) method is proposed. Then considering the influence of controller installation location and control parameters on the dynamic stability of the system, the design methods of multi-VSG damping controller based on residue index and hybrid particle swarm optimization algorithm are proposed respectively. Finally, the effectiveness of the damping controller design method is verified through the simulation and test cases of the multi-VSG grid-connected system. [ABSTRACT FROM AUTHOR]

Optimal control system has been considered as a valuable technique to enhance the reliability and security of wastewater treatment process (WWTP). However, due to the complex environments, it is difficult to realize the optimal control with changeable operational conditions in WWTP. To overcome this problem, an intelligent optimal control system with flexible objective functions (IOCS-FOF) was proposed in this paper. The main merits of this proposed IOCS-FOF were shown as follows. First, an optimal control system scheme was developed, where different time-scale operational objectives were detailed to describe the operational features. Second, a flexible objective strategy was designed to determine suitable objective functions, which could adapt to complex environments of WWTP. Third, a novel dynamic multiobjective optimization algorithm for flexible objectives (FO-DMOA) was developed to obtain the optimal solutions of IOCS-FOF. Finally, the proposed IOCS-FOF was applied in the benchmark simulation model No. 1 (BSM1) to confirm its technique capability. The results demonstrate that this proposed method can improve the operation performance of WWTP. [ABSTRACT FROM AUTHOR]

This paper proposes a new reentry trajectory generation method, which mainly comprises two parts: reentry profile design and trajectory optimization. Different from conventional methods that design angle-of-attack (AOA) and bank-angle profiles, a height-range profile is proposed in this paper. Using this profile, the AOA can be directly derived and the quasi-equilibrium glide condition is easily satisfied. In addition, the bank angle is analytically derived with the heading error, with only one reversal. Finally, the original problem is transformed into a parameter optimization problem and solved by an improved particle swarm optimization (PSO) method. In PSO, a comparison principle is proposed to tackle constraints and three modifications are applied to the swarm evolution to enhance the searching capacity. To conquer disturbances, an online profile updating strategy is developed based on the nominal profile, so complicated work of designing a trajectory tracker is avoided. Simulation validates the effectiveness of the proposed method and demonstrates that the improved PSO is comparable to the pseudospectral method and is better than the basic PSO. [ABSTRACT FROM AUTHOR]

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]

In recent years, the need for high security with reliability in the wireless network has tremendously been increased. To provide high security in reliable networks, mobile ad hoc networks (MANETs) play a top role, like open network boundary, distributed network, and fast and quick implementation. By expanding the technology, the MANET faces a number of security challenges due to self-configuration and maintenance capabilities. Besides, traditional security solutions for wired networks are ineffective and inefficient because of the nature of highly dynamic and resource-constrained MANETs. In this paper, the researchers focus on improving reliable data transmission with high security in the MANET using an optimization technique. In the proposed MANET system, the nodes are clustered by utilizing an energy-efficient routing protocol. Then, the modified discrete particle swarm optimization is used to select the optimal cluster head. A secured routing protocol and a signcryption model can be used to improve the transmission security of the reliable MANET. The signcryption algorithm encrypts the digital signature, which can enhance the overall efficiency and confidentiality. The security-based analysis is performed on the basis of packet delivery ratio, energy consumption, network lifetime, and throughput. Finally, the result demonstrates that the MANET with optimization techniques achieves a high transmission rate and improves the reliable data security. [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]

The proportional-integral-derivative (PID) controller is a widely used controller in automation industries. Several advanced PID tuning/design methods, such as response-based design, internal model control, and controller optimization by stochastic algorithms, have been proposed in the literature. However, regardless of the advantages and accuracy of developed tuning methods, due to the process modeling error and parametric uncertainties, the experimental response always differs from the theoretical response, which required online fine-tuning of the PID parameter. Manual adjustment of three PID parameters disturbs the robustness of the controller from its desired value; also, it does not guarantee the stability of the process. Therefore, this paper focuses on online PID controller tuning with the guaranteed robustness of the controller. A new single variable tuning method is developed for the online robustness and performance adjustment. Moreover, the proposed rules only depend upon the previously optimized PID parameters. The proposed method is an additional feature to all existing PID tuning methods, including optimal PID controller with stochastic optimization algorithms. The proposed method is validated with the help of optimal PID controller design by existing optimal tuning rules, optimized PID with particle swarm optimization and experimental validation on the canonical tank system. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity. [ABSTRACT FROM AUTHOR]

This paper describes a novel method to improve the target location accuracy through imaging it from an aircraft. This method focuses on improving estimation accuracy of heading angle bias and then to improve geolocation performance. A particle swarm optimization algorithm is employed to derive an expression of optimal trajectory, which can be a guide for trajectory planning. Thanks to the maneuverability of quad-rotor unmanned aerial vehicles, the aircraft is commanded to follow path generated by trajectory planning to acquire multiple bearing measurements of the ground object. The main result is that the aircraft's heading angle bias can be more accurately estimated using trajectory planning. Hence, the target is more accurately geolocated. The efficacy of this technique is verified and demonstrated by simulation results and flight test. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) plays an important role in the quantitative analysis of hyperspectral images, as the main step in the decomposition of mixed pixels. At present, scholars have proposed many EE algorithms based on the linear spectral mixture model and the convex geometry principle, such as the pixel purity index (PPI) and the vertex component analysis (VCA). At the same time, many intelligent optimization algorithms, such as the particle swarm optimization (PSO) and the discrete PSO (DPSO), have been applied to EE, which can get promising results for real images. However, PSO and DPSO have theoretical limitations and cannot guarantee the global convergence. The problem of premature convergence will reduce the accuracy of the EE result. The quantum-behaved PSO (QPSO) can theoretically guarantee the convergence of the algorithm by combining the quantum mechanics into the PSO. In order to increase the accuracy of the algorithm, this paper proposes an improved QPSO (IQPSO) algorithm for EE. IQPSO has made innovations in population coding and initialization methods. Besides, the collaborative approach for updating the optimal positions of particles can help to solve the difficulties caused by high dimensions. The experimental results show that IQPSO can extract endmembers efficiently and effectively. [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]

This paper proposes a fast and accurate electromagnetic optimization method for magnetic levitation planar motors (MLPMs). With the purpose of achieving the maximum force with the minimum power dissipation and cost, a multi-objective optimization is adopted for the MLPM. Furthermore, in order to improve the precision and reduce the computational burden, the levitation force expression in the objective function is analyzed through the numerical model. This model is built by the magnetic charge method and the Gaussian quadrature and implemented in parallel computation. According to the optimal objective, an intelligent optimization algorithm—particle swarm optimization (PSO)—is used to undertake the optimization variables of the planar motor for a global optimal solution. The precision and effectiveness of the proposed method are validated by both the finite-element method (FEM) commercial software COMSOL and a boundary element software program named Radia. The comparative results with other optimization methods are also presented to highlight the performance of the proposed optimization method. [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 this paper, we propose an improved sub-synchronous resonance (SSR) damping controller (SSRDC) and a related control strategy to mitigate the SSR in a capacitive series-compensated double-fed induction generator based wind farms, based on the damping torque analysis. The proposed strategy is utilized for enhancing the system stability. Compared with the traditional damping controller, the proposed SSRDC is simultaneously embedded into the $d$ -axis and $q$ -axis control channels in the inner current loop of the rotor-side converter. On this basis, combined with the eigenvalue analysis and improved particle swarm optimization algorithm, the optimum gain coefficients of the SSRDC can be determined for maximizing the mitigation effect under different operation conditions of the series compensation levels and wind speeds. Moreover, the effect of the weight coefficients in the objective function on the SSR suppression is also investigated. Finally, the time-domain simulations are carried out to demonstrate the effectiveness of the proposed control strategy for mitigating the SSR and suppressing the connected power system oscillations. [ABSTRACT FROM AUTHOR]

In the photolithography process of wafer fabrication, a mask pattern is transferred to a wafer in a layer-by-layer fashion, and the pattern alignment of adjacent layers is critical to the wafer yield. To enhance the alignment precision, an overlay metrology system measures the overlay error at some markers on the wafer, and the error information is used for constructing an overlay correction model. During the overlay alignment, the layout of the markers has a significant impact on the correction of the overlay error. After the maximum number of available markers has been determined based on the quality and turn-around time of the target device, the positions of those markers should be determined in such a way that the overlay error correction model shows robust performance for future wafers. In this paper, we propose a sparse particle swarm optimization algorithm to find an optimal marker layout in terms of robust performance characterized by the overlay error prediction and irregularity of marker positions. In the experiment, the performance of the marker layouts suggested by several search algorithms was tested on three different layers, and the proposed algorithm demonstrated superiority over the other algorithms. [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]

Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters. [ABSTRACT FROM AUTHOR]

The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. [ABSTRACT FROM PUBLISHER]

In this paper, new metaheuristic technique cuckoo search optimization (CSO) is explored for an optimal and robust power system stabilizer (PSS) designing for multimachine power systems and performance is evaluated by comparing with well-established techniques, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and new harmony search optimization (HSO). An eigenvalue-based multiobjective function is implemented for simultaneous control of damping factor and damping ratio to damp out low-frequency local and interarea modes of oscillations. The PSS parameters are so intended that unstable and/or lightly damped mode eigenvalues are shifted to a specified D-shape zone in the left half of the s-plane. The selected metaheuristic techniques GA, PSO, HSO, and CSO are used to obtain PSS parameters for 10-machines, 39-bus New England Power System and the controllers designed are named as GAPSS, PSOPSS, HSOPSS, and CSOPSS, respectively. The performance of all designed PSSs controllers is evaluated by eigenvalues analysis, nonlinear simulations, and performance indices for various operating conditions under different scenarios of severe disturbances and their comparative analysis is observed. The robustness of all designed PSSs controllers is observed by testing them on unseen operating conditions and results are compared for the same. The performance of CSOPSS is found to be better than other controllers designed. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a methodology for shape optimization of the starting cage of a line start permanent magnet synchronous motor motor with the aim to improve its synchronization performance. The parameters of the machine are established from a field-circuit model, where the magnetic field is simulated using a finite element method (FEM). A strategy for evaluating machine parameters exploiting parallel computing is proposed. To facilitate the use of FEM package, bespoke procedures have been developed and model parameterization applied with the aid of the scripting language Visual Basic. A particle swarm algorithm has been adapted for design optimization purposes. The proposed strategy has been verified via test simulations. [ABSTRACT FROM AUTHOR]

To maximize solar photovoltaic (PV) output under dynamic weather conditions, maximum power point tracking (MPPT) controllers are incorporated in solar PV systems. However, the occurrence of multiple peaks due to partial shading adds complexity to the tracking process. Even though conventional and soft computing techniques are widely used to solve MPPT problem, conventional methods exhibit limited performance due to fixed step size, whereas soft computing techniques are restricted by insufficient randomness after reaching the vicinity of maximum power. Hence, in this paper, a new flower pollination algorithm (FPA) with the ability to reach global peak is proposed. Optimization process in FPA method performs global and local search in single stage and it is a key tool for its success in MPPT application. The ruggedness of the algorithm is tested with zero, weak, and strong shade pattern. Further, comprehensive performance estimation via simulation and hardware are carried out for FPA method and are quantified with conventional perturb and observe and particle swarm optimization (PSO) methods. Results obtained with FPA method show superiority in energy saving and proved to be economical. [ABSTRACT FROM AUTHOR]

In this paper, a parallel computing implementation of a multiagent coordination optimization (MCO) algorithm is introduced using \mathtt parfor , a built-in MATLAB function. As a novel variation of particle swarm optimization (PSO), the MCO algorithm has demonstrated significant performance improvement, in terms of both accuracy and efficiency, in solving real-time optimization problems when compared with PSO. However, the ability to handle large-scale optimization problems or use more particles in the algorithm is limited by the sequential implementation of the original MCO algorithm. A numerical evaluation of the parallel MCO algorithm was conducted using the supercomputers in the High Performance Computing Center at Texas Tech University. Based on the results of this evaluation, it was determined that the performance of the parallel MCO is not only superior to that of PSO but is highly efficient as it reduces the computational time. The parallel binary MCO (BMCO) algorithm is presented here as well. By combining the parallel MCO with parallel BMCO algorithms, a new parallel mixed-binary nonlinear programming MCO solver is proposed. Finally, a load balancing coordination problem, a multiagent formation control problem, and a power system vulnerability analysis problem are solved using the corresponding parallel MCO algorithm. [ABSTRACT FROM AUTHOR]

This paper describes a multiobjective optimization design technique for a six-phase copper rotor induction motor mounted with a scroll compressor to achieve minimum manufacturing cost and starting current, and maximum efficiency and power factor. The proposed algorithm uses the modified particle swarm optimization and the Taguchi method with finite-element analysis. The experimental results confirm the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a simulated annealing (SA)-based global maximum power point tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common perturb and observe MPPT technique and the particle swarm optimization method for GMPPT. The performance is assessed by considering the time taken to converge and the number of sample cases where the technique converges to the GMPP. Simulation results indicate the improved performance of the SA-based GMPPT algorithm, with arbitrarily selected parameters, in tracking to the global maxima in a multiple module PV system which experiences PSC. Experimental validation of the technique is presented based on PV modules that experience nonuniform environmental conditions. Additionally, studies regarding the influence of the key parameters of the SA-based algorithm are described. Simulation and experimental results verify the effectiveness of the proposed GMPPT method. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs' change of states, and relay stations (RSs)' change of states. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves better performance at linear complexity, compared with the existing algorithms under different dynamic environments, while guaranteeing fairness to a proper level. [ABSTRACT FROM AUTHOR]

Feature extraction and dimensionality reduction (DR) are necessary and helpful preprocessing steps for bearing defect classification. Linear local Fisher discriminant analysis (LFDA) has recently been developed as a popular method for feature extraction and DR. However, the linear method tends to give undesired results if the samples between classes are nonlinearly separated in the input space. To enhance the performance of LFDA in bearing defect classification, a new feature extraction and DR algorithm based on wavelet kernel LFDA (WKLFDA) is presented in this paper. Herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. To seek the optimal parameters for WKLFDA, particle swarm optimization (PSO) is used; as a result, a new PSO-WKLFDA algorithm is proposed. The experimental results for the synthetic data and measured vibration bearing data show that the proposed WKLFDA and PSO-WKLFDA outperform other state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the concepts taught in the course and motivate them to explore relevant issues of bio-inspired optimization algorithms through interactive and collaborative learning processes. BOLE differs from other similar tools in that it places greater emphasis on fundamental concepts than on complex mathematical equations. The learning tasks using BOLE can be classified into four steps: introduction, recognition, practice, and collaboration, according to task complexity. It complements traditional classroom teaching, enhancing learning efficiency and facilitating the assessment of student achievement, as verified by its practical application in an undergraduate course “Bio-Inspired Computing.” Both objective and subjective measures were evaluated to assess the learning effectiveness. [ABSTRACT FROM PUBLISHER]

In part II of this two-part paper, the improved particle swarm optimization (IPSO) algorithm for solving the microgrid (MG) day-ahead cooling and electricity coordinated scheduling is proposed. Significant improvements are made in comparison with the conventional PSO algorithm from two aspects. First, the mandatory correction is implemented to ensure the complex coupled constraints among the components of a particle are met after the particle's position is updated, which could enhance the algorithm performance when solving the problem including complex constraints. Second, it is assumed that a solution denoted by a particle occupies a neighboring area, the size of which decreases from a certain value to nearly zero as the iteration step increases to its limitation, which helps to avoid the pre-maturity of algorithm. For an MG composed of the combined cooling, heating and power (CCHP) units, PV panels, wind turbines, and storage batteries, a range of case studies under different MG operating modes are carried out through simulations. The simulation results demonstrate the proposed multi time-scale, multi energy-type coordinated MG scheduling solution can achieve the co-optimization of multi energy-type supply to meet customer's cooling and electricity demands, and make the MG be controllable as seen from the connected main grid. [ABSTRACT FROM AUTHOR]

This paper proposes a novel online multiobjective evolutionary approach for the navigation of humanoid robots. In the proposed approach, the humanoid robot navigation problem is decomposed into a series of small multiobjective optimization problems (MOPs) with corresponding local information. Using multiobjective evolutionary algorithms (MOEAs), the MOPs can be successively solved while the robot is walking. In addition, to achieve significant reductions in the processing time of the MOEAs for online implementation while maintaining robustness and scalability, a novel homogeneous parallel computing method is devised for the MOEAs. Multiobjective particle swarm optimization with preference-based sort (MOPSO-PS) is employed as the MOEA to reflect the user-defined preference for each objective during navigation. The effectiveness of the proposed online approach is demonstrated through well-known benchmark problems and a robot simulator. In both the simulation and the experiment, a humanoid robot successfully navigates to the goal, satisfying the preferences for various objectives, with local information in an environment without a global map. [ABSTRACT FROM PUBLISHER]

In design of a pulse transformer, there are several conflicting objective functions that should be optimized. However, due to the high complexity, the conventional design methods cannot consider all of them simultaneously. In this paper, multiobjective particle swarm optimization is used to design a pulse transformer. This method is based on Pareto optimality. The output consists of a set of optimal solutions that the best design is selected from it. To use this optimization method, a model is developed for a pulse transformer with toroidal wound core. The model considers magnetic, electric, and thermal characteristic of a transformer. Its simple calculations make it proper for using in an optimization process. A transformer is designed and implemented for minimum rise time, temperature rise, and volume using the presented method. This transformer is compared with a transformer that has been designed by conventional methods. Measurements and comparisons show that the results are more acceptable than the results of convectional designs besides simplicity and accuracy of the method. [ABSTRACT FROM PUBLISHER]

A new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization-expectation maximization (PSO-EM) algorithm, a “winner-take-all” version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods. [ABSTRACT FROM PUBLISHER]