There is a trend to use artificial neural networks (ANNs) as approximation models to implement explicit model-predictive control (EMPC) on hardware. However, the vanilla ANN-based EMPC scheme requires an overredundant ANN structure for achieving better fitting performance but at the expense of increasing the online implementation resources, such as computation time, memory cost, etc. This article proposes a light implementation scheme for ANN-based EMPC (LISABE). It shows much superior control performance than the vanilla scheme with reduced online computation time and memory resources under a combination of an optimized data generation process and an improved ANN structure. On the one hand, attention-based tree-search sampling is proposed to help enhance the ANN's fitting performance by optimizing the distribution of the offline laws of EMPC. On the other hand, by taking advantage of the bilaterally bounded property of the offline law distribution in power converter applications, a dual-rectified-linear-unit ANN is proposed as the approximation model for EMPC. It significantly improves the fitting performance with a reduced ANN structure. Simulations and experiments verify that the LISABE addresses the challenge of performing online computation of EMPC with a long prediction horizon using low-cost microprogrammed control units and can further save around 84% of online computation and memory for the current-mode boost converter and around 50% of online computation and memory for the voltage-mode buck converter compared with the vanilla ANN-based EMPC.

In DC microgrid, the uncontrollable DC link short-circuit current seriously shortens the life of the electrolytic capacitors in conventional AC/DC converters. To solve the issue, a new AC/DC converter with controllable DC link short-circuit current is proposed. Simulation results have verified the performance of the proposed converter.

In order to mitigate the negative effect of multi-frequency ripple power, a non-isolated micro-inverter with active power decoupling has been proposed in recent years, which shows some potential advantages, in terms of low cost, fewer switches, high efficiency, and a brief system control strategy. However, it has two independent PV panel input dc sources, which may be asymmetrical in realistic PV systems, resulting in a decrease in the power generation capacity of the whole system. To address this problem, an improved non-isolated micro-inverter topology with active power decoupling is proposed in this paper. Similar to the conventional micro-inverter, it can effectively suppress the common-mode leakage current. Different from the conventional micro-inverter topology, it uses a single PV panel input dc source and achieves positive and negative half-line cycle equivalent dc sources through two split-bus capacitors. The simulation has validated the effectiveness of the proposed non-isolated micro-inverter.

In recent years, the deadbeat-control-based MPC algorithm (DBC-MPC) has been widely studied because it can effectively reduce the number of candidate vectors. However, for the LCL-type grid-connected inverter, when calculating the reference value of the inverter output voltage, the control algorithm only addresses the influence of the current tracking error on the reference value of the output voltage of the inverter, and ignores the influence of the filter capacitor voltage, resulting in poor control performance. Therefore, in this paper an improved DBC-MPC is proposed for the LCL-type grid-connected inverter, where both effects of current and voltage tracking errors on the control performance are fully considered. Compared with the traditional DBC-MPC algorithm, the control performance of the system is greatly improved, while still effectively reducing the number of candidate voltage vectors. Finally, a three-phase simulation model is built to verify the control performance of the proposed algorithm.

Ultra-high-frequency radio-frequency-identification (UHF RFID)-based sensing technology with antenna-integrated sensor tags offers a cost-effective solution for Internet of Things (IoT) applications. A multidimensional differential measurement technique for eliminating the effect of the measurement setup, such as unknown mutual distance and orientation between the read/write device (RWD) and sensor tags, and line-of-sight obstruction, on acquiring information from multiple sensor tags simultaneously is proposed. The concept is based on 1) using a channel hopping mechanism to acquire multidimensional data sets and 2) conducting a dissimilarity analysis between offline and online data sets to extract sensor information. The differential measurement, which is activated by the channel hopping mechanism, mitigates the impact of the measurement setup, making the pattern of the multidimensional data sets tightly relate to the sensing state. A sensor tag for measuring the water-filling level of polyvinyl chloride (PVC) pipes is designed and fabricated. Experiments show that the proposed method can accurately acquire information from three sensor tags simultaneously under different measurement setups. The performances using data sets of different dimensions are compared. Results reveal that using low-dimensional data sets can still be capable of getting accurate information from the sensor tags, implying that the acquisition time can be reduced.

This paper presents an IGBT open-circuit fault diagnosis method based on Kalman filter model and reconfiguration algorithm for H6 grid-tied inverter. This method can detect open-circuit fault by only sampling the inductor current through Kalman filter model. Then, through the reconfiguration algorithm, the H6 grid-tied inverter is reconfigured as a Boost-type converter to identify the faulty IGBT device. The Kalman filter model can also predict the grid voltage provided to the control loop, thus saving the ac voltage sensor for the system. This method does not require extra sensors and diagnostic circuits, so it can be easily embedded in the DC/AC inverter system.

Ultrahigh frequency radio-frequency identification (UHF RFID) technology using transducer-integrated antennas provides a cost-effective option for sensing. A novel setup-independent UHF RFID sensing system that can overcome challenges caused by the measurement setup with a priori unknown mutual distance and orientation between the tag and the read/write device (RWD) is proposed. The concept is based on using off-the-shelf radio-frequency identification (RFID) chips having self-tuning capability, which maximizes power transfer coefficient between RFID chip and RFID antenna. An ON-OFF differential (OOD) measurement is implemented to provide setup-independent sensing. The OOD parameters form a 3-D vector that can support more sensing states than prior art using single-dimensional information. Power transfer efficiency (PTE) and tag sensor efficiency (TSE) are defined, where PTE is the minimal power transfer coefficient in all sensing states and TSE is the minimal pairwise Euclidean distance of the OOD parameters. The RFID antenna layout is optimized by optimizing PTE and TSE simultaneously so as to offer sensing robustness without sacrificing communication performance in all sensing states. A water filling-level sensor has been designed and evaluated. Theoretical predications are favorably compared with experimental results under different measurement setups, having different relative distances and orientations between the tag and RWD.

In grid-tied DC/AC inverter applications, the equivalent grid impedance often varies widely, which may limit the bandwidth of the inverter control system. This paper introduces a new method to perform the online estimation of the equivalent grid impedance with a Kalman filter by observing the grid voltage and grid current, as well as the voltage at the point of common coupling (PCC). With the estimated grid impedance, a high control bandwidth can be achieved for the grid-tied inverter through online regulation of the proportional coefficient of a current controller. A MATLAB/Simulink model of a grid-tied single-phase inverter has been setup to demonstrate the effectiveness of the proposed method. The simulation results show that the online estimated impedance is accurate enough and the inverter system can continuously maintain a high bandwidth, even under weak grid operating conditions.

Virtual oscillator control (VOC) has been proposed for Microgrids, since compared to the droop control method, VOC has a faster transient response. However, the output voltage of the conventional VOC always contains the third-harmonic. Thus, in the grid-connected mode, the third-harmonic voltage causes the generation of significant third-harmonic current which is injected into the power grid. In this paper, by analyzing the nonlinear oscillator and simplifying the nonlinear current source in the oscillator, a novel VOC for three-phase DC/AC inverter is proposed, where the third-harmonic of the oscillator output voltage can be successfully eliminated, whether in the islanded or grid-connected mode of operation. In addition, compared with the traditional VOC, the dynamic response of the proposed VOC-based inverter can be significantly improved, especially in the islanded mode. Experimental device designed on the DSPACE DS1202 is developed to verify the feasibility of the proposed strategy.

The Passivity-Based Control (PBC) has been adopted in LCL-filtered grid-tied inverter (GTI). However, the conventional PBC method depends much on accurate mathematical model, where zero steady-state error can't be realized when the accurate model parameters are not available or parameters drift occur. Furthermore, due to the utilizing of three state variables in the conventional PBC controller for LCL-filtered GTI, twelve sensors (voltage and current) must be used in a three phase system, which increase the costs and the failure rate of hardware. In order to handle the problems, a novel state observer based modified PBC (SOMPBC) controller for LCL-filtered GTI is proposed in this paper. Six sensors can be saved by the state observer and zero steady-state error can be easily realized by two modified control methods, where an integral regulator is insert into the conventional PBC controller with two different ways. Simulation platform is built in MATLAB/Simulink and a 3-kW experimental device is carried out with DS1202 to verify the correctness and effectiveness of proposed control method.

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

This paper proposes a novel method for detecting the Parkinson's diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinson's diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinson's diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinson's diseases, our proposed method allows to use a flexible number of features for detecting the Parkinson's diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.

With the rapidly development of smart grid, communication network will play more and more fundamental role in many smart grid applications and services. The interaction between power system and communication network will appear almost everywhere in the new services of smart grid, the investigation of mixture system combined power system and communication network reveals the mutual influence of each other, and will give accuracy and quantitative data for the planning of the future smart grid. This paper presents a novel cosimulation platform combined power system and communication network to meet the decision-making need in smart grid environment. The platform connects power system simulator and communication system simulator together via a middleware with interfaces, a synchronization method is proposed for the correct time and sequence of data exchange, a time step adjustment algorithm is proposed as well to balance the requirement of accuracy and efficiency.

The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy.

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept “aging” in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.

Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on the quality of service provided by the transport system. This paper explores the periodic railway timetable scheduling (PRTS) problem, with the objective to minimize the average waiting time of the transfer passengers. Unlike traditional PRTS models that only involve service lines with fixed cycles, this paper presents a more flexible model by allowing the cycle of service lines and the number of transfer passengers to vary with the time period. An enhanced differential evolution (DE) algorithm with dual populations, termed “dual-population DE” (DP-DE), was developed to solve the PRTS problem, yielding high-quality solutions. In the DP-DE, two populations cooperate during the evolution; the first focuses on global search by adopting parameter settings and operators that help maintain population diversity, while the second one focuses on speeding up convergence by adopting parameter settings and operators that are good for local fine tuning. A novel bidirectional migration operator is proposed to share the search experience between the two populations. The proposed DP-DE has been applied to optimize the timetable of the Guangzhou Metro system in Mainland China and six artificial periodic railway systems. Two conventional deterministic algorithms and seven highly regarded evolutionary algorithms are used for comparison. The comparison results reveal that the performance of DP-PE is very promising.

Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.

Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs.

Particle swarm optimization (PSO) is predominately used to find solutions for continuous optimization problems. As the operators of PSO are originally designed in ann-dimensional continuous space, the advancement of using PSO to find solutions in a discrete space is at a slow pace. In this paper, a novel set-based PSO (S-PSO) method for the solutions of some combinatorial optimization problems (COPs) in discrete space is presented. The proposed S-PSO features the following characteristics. First, it is based on using a set-based representation scheme that enables S-PSO to characterize the discrete search space of COPs. Second, the candidate solution and velocity are defined as a crisp set, and a set with possibilities, respectively. All arithmetic operators in the velocity and position updating rules used in the original PSO are replaced by the operators and procedures defined on crisp sets, and sets with possibilities in S-PSO. The S-PSO method can thus follow a similar structure to the original PSO for searching in a discrete space. Based on the proposed S-PSO method, most of the existing PSO variants, such as the global version PSO, the local version PSO with different topologies, and the comprehensive learning PSO (CLPSO), can be extended to their corresponding discrete versions. These discrete PSO versions based on S-PSO are tested on two famous COPs: the traveling salesman problem and the multidimensional knapsack problem. Experimental results show that the discrete version of the CLPSO algorithm based on S-PSO is promising.

Maximizing the lifetime of a sensor network by scheduling operations of sensors is an effective way to construct energy efficient wireless sensor networks. After the random deployment of sensors in the target area, the problem of finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target area, is nondeterministic polynomial-complete. This paper proposes a hybrid approach of combining a genetic algorithm with schedule transition operations, termed STHGA, to address this problem. Different from other methods in the literature, STHGA adopts a forward encoding scheme for chromosomes in the population and uses some effective genetic and sensor schedule transition operations. The novelty of the forward encoding scheme is that the maximum gene value of each chromosome is increased consistently with the solution quality, which relates to the number of disjoint complete cover sets. By exerting the restriction on chromosomes, the forward encoding scheme reflects the structural features of feasible schedules of sensors and provides guidance for further advancement. Complying with the encoding requirements, genetic operations and schedule transition operations in STHGA cooperate to change the incomplete cover set into a complete one, while the other sets still maintain complete coverage through the schedule of redundant sensors in the sets. Applications for sensing a number of target points, termed point-coverage, and for the whole area, termed area-coverage, have been used for evaluating the effectiveness of STHGA. Besides the number of sensors and sensors' sensing ranges, the influence of sensors' redundancy on the performance of STHGA has also been analyzed. Results show that the proposed algorithm is promising and outperforms the other existing approaches by both optimization speed and solution quality.

The multimode resource-constrained project-scheduling problem with discounted cash flows (MRCPSPDCF) is important and challenging for project management. As the problem is strongly nondeterministic polynomial-time hard, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for the MRCPSPDCF, this paper proposes an ant colony optimization (ACO) approach. ACO is promising for the MRCPSPDCF due to the following three reasons. First, MRCPSPDCF can be formulated as a graph-based search problem, which ACO has been found to be good at solving. Second, the mechanism of ACO enables the use of domain-based heuristics to accelerate the search. Furthermore, ACO has found good results for the classical single-mode scheduling problems. But the utility of ACO for the much more difficult MRCPSPDCF is still unexplored. In this paper, we first convert the precedence network of the MRCPSPDCF into a mode-on-node (MoN) graph, which becomes the construction graph for ACO. Eight domain-based heuristics are designed to consider the factors of time, cost, resources, and precedence relations. Among these heuristics, the hybrid heuristic that combines different factors together performs well. The proposed algorithm is compared with two different genetic algorithms (GAs), a simulated annealing (SA) algorithm, and a tabu search (TS) algorithm on 55 random instances with at least 13 and up to 98 activities. Experimental results show that the proposed ACO algorithm outperforms the GA, SA, and TS approaches on most cases.

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person's skill. This paper develops a novel intelligent testing system for both teachers and students. Based on the browser/server structure, the proposed testing system comprises a question bank and five modules, offering the features of self-adaptation, reliability, and flexibility for generating parallel tests with identical test ability. The core of the developed system is the ant-colony-optimization-based test composition (ACO-TC) method, which aims at generating high-quality tests for examinations and satisfying multiple requirements. As an advanced computational intelligence algorithm, the proposed ACO-TC method uses a colony of ants to select appropriate questions from a question bank to construct solutions. Pheromone and heuristic information is designed for facilitating the ants' selection. The system is analyzed by composing tests in different situations. The generated tests not only match the expected total completion time, the concept proportions, the average difficulty, and the score proportions of different question types, but also have high average discrimination degrees of questions. The experimental results also show that the system can always generate high-quality tests from question banks with various sizes.

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity.

This paper presents an investigation into the use of the time delay coordinate embedding technique in the multi-input-multi-output-adaptive-network-based fuzzy inference system (MANFIS) for chaotic time series prediction. The inputs of the MANFIS are embedded-phase-space (EPS) vectors preprocessed from the time series under test while the output time series is extracted from the EPS vectors. With such EPS preprocessing, the prediction accuracy of the MANFIS is found to be significantly improved. The proposed system will be tested with a periodic and the Mackey-Glass chaotic time series by comparing the prediction accuracy with and without EPS preprocessing. A moving root-mean-square error is used to monitor the error along the prediction horizon and to tune the membership functions in the MANFIS.

Research into adjusting the probabilities of crossover and mutation pmin genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. pxand pmgreatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of pxand pm, this paper presents the use of fuzzy logic to adaptively adjust the values of pxand pmin GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of pxand pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of pxand pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

This correspondence presents pseudocoevolutionary genetic algorithms (GAs) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel coadapted GA-based optimization processes for the power conversion stage (PCS) and feedback network (FN), respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as an untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.

Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed px and p.

Research on adjusting the probabilities of crossover p/sub x/ and mutation p/sub m/ in genetic algorithms (GA's) is one of the most significant and promising areas of investigation in evolutionary computation, since p/sub x/ and p/sub m/ greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of having fixed p/sub x/ and p/sub m/, This work presents the use of fuzzy logic to adaptively tune p/sub x/ and p/sub m/ for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of p/sub x/ and p/sub m/ are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed p/sub x/ and p/sub m/.

This paper presents pseudo-coevolutionary genetic algorithms (GA's) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel co-adapted GA-based optimization processes for power conversion stage and feedback network, respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.

This paper presents the generalized circuit structure of a switched-capacitor DC/DC converter that can offer features of voltage step-down, voltage step-up, and bi-directional power flow, Starting with the derivation of a single-capacitor bidirectional converter cell, a converter string is created by cascading a required number of cells, which is determined by the input and output conversion ratio for optimizing the conversion efficiency. The input current is made continuous by operating two strings in parallel and in anti-phase. State-space averaging technique is applied to study the static and dynamic behaviors.

This paper presents a decoupled optimization technique for design of switching regulators using genetic algorithms (GA). The optimization entails selection of the component values in the regulator to meet some static and dynamic requirements. During the optimization, a regulator is decoupled into two parts, including the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, while the FN is optimized with the required static and dynamic characteristics of the whole system during disturbances. The proposed technique is illustrated with the design of a buck regulator. Predicted results are compared to the available literature and are verified with experimental measurements.

A method for finding the equilibrium points of closed-loop switching converters subjected to large-signal disturbances is developed. The position of the equilibrium points in the state-plane, in conjunction with the state-plane portrait, allow for the study of the stability of large-signal transients. For this purpose, a discrete-time model is formulated, starting from a time-domain analysis algorithm based on solving a set of algebraical modified nodal equations at each integration step. Due to the complexity of the formulas, the study of the large-signal stability is carried out here only for PWM converters operating in continuous conduction mode. An application to a boost regulator allows for favorably comparing our results to those available in literature.

A discrete-time dynamic model of closed-loop switched mode electronic regulators is derived. No small-ripple approximations are required. The same model serves for both local and global stability study: by discarding the nonlinear terms (like products of small-signal perturbations in the converter state variables) and using the z-transform, a local stability criteria is formulated. Applying the condition that the eigenvalues of the z-domain characteristic matrix have to be situated inside the unit circle, a design constraint on the feedback gains is found. An example of a boost converter operating in continuous conduction mode with inductor current and output voltage feedback is presented.