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This paper proposes a novel adaptive range composite differential evolution (ARCoDE) algorithm to efficiently and accurately solve optimal reactive power dispatch (ORPD) problem. Because of a novel adaptive range strategy for control parameters, the proposed ARCoDE possesses superior exploration and exploitation capabilities that can efficiently handle the ORPD problem involving complicated constraints and discrete and continuous variables. This has been demonstrated in case studies using the IEEE optimal power flow testbeds considering complex wind and demand scenarios. The superior performance of ARCoDE has been further validated through comparisons with several award-winning algorithms in 2014 IEEE Competition on “Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems”, given limited iterations of in evolutionary optimization process. Energy Market Authority (EMA) Published version This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1505400, and inpart by the Energy Market Authority and National Science Foundation Singapore under Grant NRF2017EWT-EP003-038.

In artificial neural networks, activation functions play a significant role in the learning process. Choosing the proper activation function is a major factor in achieving a successful learning performance. Many activation functions are sufficient universal approximators, but their performance is lacking. Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks. However, the learning process involves many challenges, such as saturation, dying, and exploding/vanishing the gradient problems. The contribution of this work resides in several axes. First, we introduce two novel activation functions: absolute linear units and inverse polynomial linear units. Both activation functions are augmented by an adjustable parameter that controls the slope of the gradient. Second, we present a comprehensive study and a taxonomy of various types of activation functions. Third, we conduct a broad range of experiments on several deep neural architecture models with consideration of network type and depth. Fourth, we evaluate the proposed activation functions’ performance in image and text classification tasks. For this purpose, several public benchmark datasets are utilized to evaluate and compare the performance of the proposed functions with that of a group of common activation functions. Finally, we deeply analyze the impact of several common activation functions on deep network architectures. Results reveal that the proposed functions outperform most of the popular activation functions in several benchmarks. The statistical study of the overall experiments on both classification categories indicates that the proposed activation functions are robust and superior among all the competitive activation functions in terms of average accuracy.

This paper presents a first study on solution representation learning for inducing greater alignment and hence positive transfers between distinct multi-objective optimization tasks that bear discrepancies in their original search spaces. We first establish a novel probabilistic model-based multi-objective transfer evolutionary optimization (TrEO) framework with solution representation learning, capable of activating positive transfers while simultaneously curbing the threat of negative transfers. In particular, well-aligned solution representations are learned via spatial transformations to handle mismatches in search space dimensionalities between distinct multi-objective problems, as well as to increase the overlap between their optimized search distributions. We then showcase different algorithmic instantiations and case studies of the proposed framework in applications spanning continuous as well as discrete optimization; illustrative examples include multi-objective engineering design and route planning of unmanned aerial vehicles. The experimental results show that our framework helps induce positive transfers by unveiling useful but hidden inter-task relationships, thus bringing about faster search convergence to solutions of high quality in multi-objective TrEO.

This paper addresses the synchronization problem of coupled harmonic oscillators via event-triggered impulsive control. Different from the existing results where both position and velocity measurements are required to achieve the synchronization, two kinds of event-triggered impulsive mechanisms, that is, the position-only and velocity-only approaches are proposed respectively, which effectively extends the event-triggered impulsive control strategy to the synchronization problem with limited information. Sufficient conditions are proposed to exhibit the convergence behavior of the coupled harmonic oscillators. Meanwhile, it is derived that Zeno behavior can be excluded by using the designed controller. Two simulation examples are illustrated to substantiate the performance of the proposed synchronization protocols.

Decomposition-based evolutionary multi-objective algorithms (MOEAs) and many-objective algorithms (MaOEAs) divide a multi-objective problem (MOP) or a many-objective problem (MaOP) into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto Fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector guided evolutionary algorithm (RVEA) by designing an adaptive reference vectors adjustment strategy and strengthening the poor selection pressure. By adding the adaptive strategy, the predefined reference vectors will be dynamically adjusted according to the distribution of promising solutions with good overall performance and the subspaces where the PF lies may be further divided so as to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increasing of objective size. Motivated by that, an improved angle-penalized distance (APD) method based on a newly proposed fractional dominance relation is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, 10- objective MaF1-MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( 6.83×10−4 , 2.06×10−4 , and 4.48×10−6 ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach., This work was supported in part by the European Commission H2020 TWINNING Joint Universal activities for Mediterranean PV integration Excellence (JUMP2Excel) Project under Grant 810809., peer-reviewed

Time synchronization is an essential problem for energy-harvesting wireless sensor networks (EH-WSNs), which is closely related to efficient resource schedules, energy harvesting, data fusion, location, etc. With the advantage of being more robust than master controlling synchronization, distributed time synchronization algorithms are usually used to EH-WSNs for cooperating sleeping nodes. This paper proposes a novel accelerated time co-synchronization algorithm based on the storage-and-prediction method to improve the convergence rate. In this algorithm, each node in the network first predicts the estimated current time state value according to previous time state values stored in the local node, and then adjusts the time state value according to the estimated time state value deviations between all its adjacent nodes. Theoretical analysis in a more general case shows that the proposed algorithm can improve the convergence rate of distributed time synchronization when selecting the appropriate parameter, and the closed-form solution of the optimal parameter is also given. Finally, the simulation of comparing the classical algorithm with the proposed algorithm based on different scenarios is completed.

Traditional $\ell _{1}$ -regularized compressed sensing magnetic resonance imaging (CS-MRI) model tends to underestimate the fine textures and edges of the MR image, which play important roles in clinical diagnosis. In contrast, the convex nonconvex (CNC) strategy allows the use of nonconvex regularization while maintaining the convexity of the total objective function. Plug-and-play (PnP) algorithm is a powerful framework for sparse regularization problems, which plug any advanced denoiser into traditional proximal algorithms. In this paper, we propose a PnP-ADMM algorithm for CS-MRI reconstruction with CNC sparse regularization. We first obtain the proximal operator for CNC sparse regularization. Then we present PnP-ADMM algorithm by replacing the proximal operator of ADMM with properly pre-trained denoisers. Furthermore, we conduct comparison experiments using various denoisers under different sampling templates for different images. The experimental results verify the effectiveness of the proposed algorithm with both numerical criteria and visual effects.

Regression loss function in object detection model plays an important factor during training procedure. The IoU based loss functions, such as CIOU loss, achieve remarkable performance, but still have some inherent shortages that may cause slow convergence speed. The paper proposes a Scale-Sensitive IOU(SIOU) loss for the object detection in multi-scale targets, especially the remote sensing images to solve the problem where the gradients of current loss functions tend to be smooth and cannot distinguish some special bounding boxes during training procedure in multi-scale object detection, which may cause unreasonable loss value calculation and impact the convergence speed. A new geometric factor affecting the loss value calculation, namely area difference, is introduced to extend the existing three factors in CIOU loss; By introducing an area regulatory factor $\gamma $ to the loss function, it could adjust the loss values of the bounding boxes and distinguish different boxes quantitatively. Furthermore, we also apply our SIOU loss to the oriented bounding box detection and get better optimization. Through extensive experiments, the detection accuracies of YOLOv4, Faster R-CNN and SSD with SIOU loss improve much more than the previous loss functions on two horizontal bounding box datasets, i.e, NWPU VHR-10 and DIOR, and on the oriented bounding box dataset, DOTA, which are all remote sensing datasets. Therefore, the proposed loss function has the state-of-the-art performance on multi-scale object detection.

Given a network, local community detection aims at finding the community that contains a set of query nodes (seed nodes). Random walk (RW) based algorithms have shown great success in various local community detection scenarios. Starting from the seed nodes, RW based algorithms continuously sample random walk paths to get the clustering result. However, current RW based algorithms for local clustering are faced with the following two problems. The random walker is insensitive to the community boundary and might have an unbalanced walk. These problems would have a negative effect on the clustering result of RW based algorithms. In this paper, we propose a density sensitive random walk algorithms (DSRW) for local community detection. By integrating the graph density information into the random walk process, the problems are resolved and the clustering result is improved. We provide the convergence proof of DSRW algorithm and perform extensive experiments on both real and synthetic datasets. Results show that our DSRW algorithm has achieved the state-of-the-art result in most scenarios.

This paper investigates the opinion dynamics on the adaptive networks with the community structure. A coevolution model of opinions and networks is proposed, in which the influence weights among agents can not be precisely obtained, and there exist the confidence heterogeneities and the frequency difference of exchanging opinion between the inner-community and the inter-community. A two-dimensional fuzzy inference system is designed to obtain the influence weights among agents’ opinions, which deals with the difficulties that the influence weights can not be precisely obtained. The simulation results show that the imbalance levels between the inner-community confidence and the inter-community confidence not only affect strongly the average relative frequency of survival opinions, but also dominate the distribution of survival opinions. The polarization state of opinions easily appears in the population with the higher inner-community confidence, whereas when the inter-community confidence is higher, the system tends to exhibit a large number of small-size clusters and a plural state is achieved. Moreover, the simulation results also show that the convergence time to the stable state varies non-monotonically with the increase of the inner-community confidence or the inter-community confidence.

In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

The switched reluctance machine has been an attractive candidate for many applications owing to its simple design and low construction costs, without the use of permanent magnets. However, the double saliency of its stator and rotor poles results in noise-causing torque ripples. And although advanced torque ripple minimization control techniques exist, they rely on modeling the machine, which in turn requires specialized offline experimental setups or online (during operation) parameter identification techniques. To date, existing online techniques are iterative without proof of convergence, do not provide all model parameters, and/or rely on a priori information that can change after the machine is commissioned. In this work, an online parameter identification method is developed with a new empirical model of its flux linkage and electromagnetic torque, to provide a complete nonlinear model of the machine. With two seconds of data collected online, all electrical and mechanical parameters are identified using a non-iterative algorithm, and so it does not pose a risk of divergence. Therefore, parameter identification can be reliably and frequently carried out at different operating conditions as the machine ages for diagnostics. Also, the resulting model is designed to be used by advanced torque ripple minimization control techniques. The implementation procedure is detailed along with simulation results to demonstrate its efficacy.

This study proposes a unit module-based acceleration method for 2-D topology optimization. For the purpose, the first-stage topology optimization is performed until the predefined iteration. After a whole design domain is divided into a set of unit modules, information on the spatiotemporal characteristics of intermediate designs and a filtering radius is used to separately predict a near-optimal design of each unit module through a trained long short-term memory (convLSTM) network. Then, in the second-stage topology optimization, a combined near-optimal design of a whole design domain is used as an initial design to determine the optimized design in a more efficient way. To train a convLSTM network, a history of intermediate designs is obtained under a randomly generated boundary condition of a unit module. The filtering radius is also used as the training data to reflect the geometric features affected by a filtering process. For four examples with different design domains and boundary conditions, the proposed method successfully provides the accelerated convergence up to 6.09 with a negligible loss of accuracy less than 1.12% error. These numerical results also demonstrate that the proposed unit module-based approach achieves a scalable convergence acceleration at a design domain of an arbitrary size (or resolution).

Defect inspection in pipes at the early stage is of crucial importance to maintain the ongoing safety and suitability of the equipment before it presents an unacceptable risk. Due to the nature of detection methods being costly or complex, the efficiency and accuracy of results obtained hardly meet the requirements from industries. To explore a rapid and accurate technique for surface defects detection, a novel approach QDFT (Quantitative Detection of Fourier Transform) has been recently proposed by authors to efficiently reconstruct defects. However, the accuracy of this approach needs to be further improved. In this paper, a modified QDFT method with integration of an integral coefficient updating strategy, called QDFTU (quantitative detection of Fourier transform of updating), is developed to reconstruct the defect profile with a high level of accuracy throughout iterative calculations of integral coefficients from the reference model updated by a termination criteria (RMSE, root mean square error). Moreover, dispersion equations of circumferential guided waves in pipes are derived in the helical coordinate to accommodate the stress and displacement calculations in the scattered field using hybrid FEM. To demonstrate the superiority of the developed QDFTU in terms of accuracy and efficiency, four types of defect profiles, i.e., a rectangular flaw, a multi-step flaw, a double-rectangular flaw, and a triple-rectangular flaw, are examined. Results show the fast convergence of QDFTU can be identified by no more than three updates for each case and its high accuracy is observed by a smallest difference between the predicted defect profile and the real one in terms of mean absolute percentage error (MSPE) value, which is 6.69% in the rectangular-flaw detection example.

When individuals interact with one another to accomplish specific goals, they learn from others’ experiences to achieve the tasks at hand. The same holds for learning in virtual environments, such as video games. Deep multiagent reinforcement learning shows promising results in terms of completing many challenging tasks. To demonstrate its viability, most algorithms use value decomposition for multiple agents. To guide each agent, behavior value decomposition is utilized to decompose the combined Q-value of the agents into individual agent Q-values. A different mixing method can be utilized, using a monotonicity assumption based on value decomposition algorithms such as QMIX and QVMix. However, this method selects individual agent actions through a greedy policy. The agents, which require large numbers of training trials, are not addressed. In this paper, we propose a novel hybrid policy for the action selection of an individual agent known as Q-value Selection using Optimization and DRL (QSOD). A grey wolf optimizer (GWO) is used to determine the choice of individuals’ actions. As in GWO, there is proper attention among the agents facilitated through the agents’ coordination with one another. We used the StarCraft 2 Learning Environment to compare our proposed algorithm with the state-of-the-art algorithms QMIX and QVMix. Experimental results demonstrate that our algorithm outperforms QMIX and QVMix in all scenarios and requires fewer training trials.

Differential evolution (DE) algorithm has some excellent attributes including strong exploration capability. However, it cannot balance the exploitation with exploration ability in the search process. To enhance the performance of the DE algorithm, this paper proposes a new algorithm named hybrid harmony differential evolution algorithm (HHSDE). The key features of HHSDE algorithm are as follows. First, a new mutation operation is developed for improving the efficiency of mutation, in which the New Harmony generation mechanics of the harmony algorithm (HS) is employed. Second, the harmony memory size is updated with the iteration. Third, a self-adaptive parameter adjustment strategy is presented to control scaling factor. Fourth, a new evaluation method is proposed to effectively assess the algorithm convergence performance. Two classical DE algorithms, HS algorithm, improvement Differential evolution algorithm(ISDE) and Hybrid Artificial Bee Colony algorithm with Differential Evolution(HABCDE) have been tested against HHSDE based on 25 benchmark functions of CEC2005 and the results reveal that the proposed algorithm is better than the other algorithms under consideration.

In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is decomposed into infinite number of singular systems based on the separation principle. Meanwhile, the slow-fast subsystems are introduced via singular value decomposition method. Then, a novel mixed PD-type ILC algorithm with finite dimension is designed for the low dimensional slow part and the corresponding convergence conditions are manifested. With the proposed controller, the output error of high dimensional fast complement can satisfy the given value instead of neglecting the effect of high dimensional modes. Furthermore, under the aforesaid ILC law and the appropriate number of the low dimensional slow part, the resulting tracking error of parabolic singular distributed parameter systems can converge to any small tracking accuracy. Finally, simulation results on the distributed building automatic temperature system verify the convergence and effectiveness of the mixed PD-type ILC algorithm.

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed. Moreover, we design a novel tracking differentiator to avoid the “differential expansion” problem well caused by the calculation of derivative. Significantly, the function approximators (e.g. neural networks) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. Finally, the numerical simulation verifies the convergence and robustness of the system, and the results show that the control strategy can obtain better transient and steady-state performance.

In this study, an adaptive linear active disturbance rejection control is proposed to achieve steady levitation of the magnetic levitation ball. The proposed algorithm was designed and its convergence was proven via derivation. It can address the difficulty in parameter tuning of the controller and realize the real-time self-adaptive optimization of parameters. Besides, to verify the effectiveness of the proposed parameter tuning strategy, we analysed the anti-interference ability and tracking effects of SMC, LADRC and A-LADRC in different cases. Finally, the results showed that the anti-interference ability of A-LADRC is stronger than SMC and LADRC. In addition, the anti-interference effect intensifies when the interference increases. The designed controller also performs the best tracking performance among these three controllers. Therefore, A-LADRC exhibits better robustness and dynamic performance.

In this paper, an unknown system dynamics estimator (USDE)-based sliding mode control (SMC) is presented for quadrotor attitude tracking with the aims of conquering control oscillation problem and achieving great robustness. To enhance the anti-disturbance ability of quadrotors, the invariant manifold-based USDE, with features of simple structure and exponential error convergence, as well as low computational consumption, is introduced to recover the unknown disturbances by basic filter operation and algebraic calculation. Aiming at accelerating sluggish transient convergency in conventional disturbance compensation-based SMC, a novel quadrotor attitude SMC strategy containing a continuous double hyperbolical reaching law (DHRL) is designed, which greatly alleviates control oscillations without sacrificing dynamic response. The dramatical advantage of proposed scheme is that the chattering-free property and fast dynamic response with precise static tracking performance are simultaneously achieved. Meanwhile, all the error signals involved in the closed-sloop system are proved to be bounded by Lyapunov analysis. Eventually, the effectiveness of explored control strategy is illustrated through simulations and experiments.

Both non-equal trail lengths and non-zero initial errors are practical challenges to learning control of robotic and mechatronic systems. Iterative learning to update input is still desired, because of the repetitive motion nature of the controlled objects. This paper concerns with the adaptive iterative learning control method for performing non-identical tasks. The time scaling technique is applied to normalize non-equal trail lengths, while the error-tracking approach is adopted for coping with initial errors. Theoretical results for performance analysis are presented in detail. The uniform convergence of the tracking error is examined, while boundedness of the variables in the closed-loop is characterized. It is shown that the fully-saturated learning algorithm plays an important role in assuring uniform boundedness of the control input. The proposed control design method does not require the magnitude transformation, and removes the assumption of identical initial conditions. The time scaling technique is verified to be effective in assuring the expected performance, for tracking tasks with non-equal trail lengths and initial errors.

Aiming at the shortcoming that the basic beetle antennae search algorithm fails to consider differences between individuals and the dynamic information in the searching process, this paper proposed a new beetle antennae search algorithm based on the elite selection mechanism and the neighbor mobility strategy. The elite selection mechanism will be used to weaken beetles having bad performances and generate new beetles to ensure diversities and abilities in the whole population. The neighbor mobility strategy will guide the algorithm to open up a wider searching area to ensure that individuals having good positions own a chance to infect individuals with poor performances. To verify the searching ability and the optimization speed of the proposed algorithm in this paper, different testing functions were selected for numerical testing experiments, and the iteration figures, box plots, and searching path figures were given. The experimental results showed that the proposed algorithm in this paper was superior to the original algorithm in the solving accuracy, the convergence speed, and the stability.

This paper mainly addresses the issue of fast finite-time consensus (FTC) algorithms for a group of high-order uncertain nonlinear non-strict-feedback Multi-agent Systems (MASs) with time-varying asymmetric full state constraints. In order to guarantee the state constraints are satisfied, an appropriate asymmetric nonlinear mapping (NM) is employed to transform the original full state constrained system into a corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory and backstepping recursive design scheme, the uncertainties and non-strict-feedback form are considered to propose a distributed adaptive FTC protocol which can guarantee tracking error reaching a region in finite time. Finally, an example is given to show that the designed control law can perform effective control.

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm’s convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

Leader follower formation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has found numerous applications such as surveillance of critical infrastructure, industrial automation and disaster management emergency. For completion of high precision group tasks, the choice of appropriate control mechanism is of utmost importance. In presence of environmental effects,external disturbances and parametric uncertainties in the UAVs and UGV models, the controller design process is a challenging task. In order to address the aforementioned problems and to ensure minimum tracking errors and fast convergence of the states, this article proposes an adaptive robust formation and trajectory tracking control scheme for a leader follower formation of UAVs and UGV using Non-Singular Terminal Super Twisting Sliding Mode Control Method.Adaptive compensators are derived based on Lyapunov function method and stability of the proposed controllers is guaranteed. Two variants of the control schemes namely Adaptive Super Twisting SMC (AST-SMC) and Adaptive Non-Singular Terminal Super Twisting SMC (ANSTS-SMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, and with the proposed ANSTS-SMC control scheme, the measured $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0~0.01}]m$ , $[{0.01~0.02}]m$ and $[{0.01,~0.02}]m$ respectively, While with the AST-SMC method the peak $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0.05~0.05}]m$ , $[{0.1~0.2}]m$ and $[{0.05~0.1}]m$ respectively. The proposed leader follower formation can be effectively used to monitor solar/PV panels and cables in large solar parks.

The task of root-finding of the non-linear equations is perhaps, one of the most complicated problems in applied mathematics especially in a diverse range of engineering applications. The characteristics of the root-finding methods such as convergence rate, performance, efficiency, etc., are directly relied upon the initial guess of the solution to execute the process in most of the systems of non-linear equations. Keeping these facts into mind, based on Taylor’s series expansion, we present some new modifications of Halley, Househölder and Golbabai and Javidi’s methods and then making them second derivative free by applying Taylor’s series. The convergence analysis of the suggested methods is discussed. It is established that the proposed methods possess convergence of orders five and six. Several numerical problems have been tested to demonstrate the validity and applicability of the proposed methods. These test examples also include some real-life problems associated with chemical and civil engineering such as open channel flow problem, the adiabatic flame temperature equation, conversion of nitrogen-hydrogen feed to ammonia and the van der Wall’s equation whose numerical results prove the better performance of the suggested methods as compared to other well-known existing methods of the same kind in the literature. Finally, the dynamics of the presented algorithms in the form of polynomiographs have been shown with the aid of computer program by considering some complex polynomials and compared them with the other well-known iterative algorithms that revealed the convergence speed and other dynamical aspects of the presented methods.

The convergence of the iterative harmonic analysis (IHA) of networks with nonlinear loads can be improved if the Gauss algorithm is replaced by the modular decoupled Newton solution (M-DNS). The core of the M-DNS is the harmonically coupled admittance matrix (HCAM) of each nonlinear load. HCAM of high-power converters has received special attention. However, the HCAM of single-phase uncontrolled rectifiers (SPUR) with capacitive smoothing has only been developed under certain simplifications for the SPUR. A reference model for the SPUR is extensively accepted and a detailed analytical procedure (DAP) is the fastest way to obtain the steady state of the SPUR. The main object of this paper is to present a HCAM for the reference model of SPUR with capacitive smoothing. This HCAM is directly derived from a DAP for the converter. Convergence properties of the IHA with this HCAM are studied.

This paper proposes a novel individual variable step-size subband adaptive filter algorithm robust to impulsive noises. A fixed step-size subband adaptive filter algorithm that is robust against impulsive noises is newly derived by obtaining the optimal solution from a constrained optimization problem through the Lagrange multiplier. In addition, in order to further improve the convergence performance of the proposed algorithm, the weight update formula with a single fixed step size is modified to have multiple individual step sizes. By analyzing its mean-square-deviation (MSD), the optimal individual step size is designed. Simulation results show that the proposed algorithm outperforms the algorithms robust to impulsive noises in the literature.

In recent years, power companies have shown increasing interest in making strategic decisions to maintain profitable energy systems. Economic Load Dispatch (ELD) is a complex decision-making process where the output power of the entire power generating units must be set in a way that results in the overall economic operation of the power system. Moreover, it is a constrained multi-objective optimization problem. Now a days, there is a tendency to use metaheuristic methods to deal with the complexity of the ELD problem. Particle swarm optimization (PSO) is a subclass of metaheuristic methods inspired by fish schooling and bird flocking behaviors. However, the optimization performance of the PSO is highly dependent on fitness landscapes and can lead to local optima stagnation and premature convergence. Therefore, in the proposed study, two new variants of the PSO called global particle swarm optimizer with inertial weights (GPSO-w) and quasi-oppositional population based global particle swarm optimizer with inertial weights (QPGPSO-w) are proposed to address the complexity of the ELD problem. The ELD problem is formulated as an optimization problem and validation of the proposed methods is performed on IEEE standards (3, 6, 13, 15, 40 & 140) unit Korean grid ELD test systems under numerous constraints and the obtained results are compared with the several recent techniques presented in the literature. The results obtained with convex systems showed excellent cost-effectiveness, while for non-convex systems sequential quadratic programming (SQP) optimizer was added to discover global minima even more efficiently. The proposed techniques were successful in solving the ELD problem and yielded better results compared to the reported results in the selected cases. It is further inferred that the proposed techniques with less algorithmic parameters reflected improved exploration and convergence characteristics.

In this paper, we propose an inertial derivative-free projection method for solving convex constrained nonlinear monotone operator equations (CNME). The method incorporates the inertial step with an existing method called derivative-free projection (DFPI) method for solving CNME. The reason is to improve the convergence speed of DFPI as it has been shown and reported in several works that indeed the inertial step can speed up convergence. The global convergence of the proposed method is proved under some mild assumptions. Finally, numerical results reported clearly show that the proposed method is more efficient than the DFPI.

Biological pest control has a strong advantage in its non-chemical effects on the environment. In this study, a fixed-time synergetic control scheme for the biological pest control problems represented by the $n$ -dimensional Lotka-Volterra model was proposed. The proof of stability shows that the proposed controller can regulate the biological pest control systems with the characteristic of fixed-time convergence. The performance of the proposed scheme was demonstrated through simulation studies. The simulation results show that the pre-specified bound of the settling time can be satisfied regardless of the initial conditions, confirming a desired fixed-time convergence characteristic. Moreover, unlike the existing control policy based on the sliding mode control, the control inputs of the proposed policy are free from chattering.

The effectiveness of user-oriented traffic routing applications to mitigate traffic congestion in Intelligent Transportation Systems depends on their degree of adoption, which usually evolves depending on subjective and exogenous factors. This paper proposes a user experience and social dynamics model to analyze and evaluate traffic routing methods, based on fuzzy rules and discrete choice theory. The model has been applied to the optimal Traffic-Weighted Multi-Maps (TWM) routing method to evaluate the adoption dynamics and analyze convergence towards the system optimum. Route unfairness and resistance to change are also considered in the model. Experimental results are obtained simulating the evolution of the drivers’ population behavior. Simulation is carried over synthetic and real networks, using optimized TWM maps. The experimental results show how the TWM system evolves to a stationary System Optimum, improving overall traffic congestion and showing how User Equilibrium variability is bounded as it depends on user routing choices influenced by behavioral patterns.

With the rising number of applications for sensor networks comes a need for more accurate cooperative fusion algorithms. In this paper, a distributed and optimal state estimator is presented for implementation through a dynamically switching, yet strongly connected, directed communication network to cooperatively estimate the state of a dynamic system. The Kalman-Consensus filter approach is used to incorporate a consensus protocol of neighboring state estimates into the traditional Kalman filter. It has been known that the main difficulty associated with implementing such an optimal solution is its fully coupled covariance matrix. Presented is a distributed computation of the covariance matrix at every node achieved by taking advantage of its independence from state estimates. Reductions to the distributed covariance computations are achieved through shared processing made available by the strongly connected digraph. Should the digraph change over time, a distributed topology estimation algorithm is included to facilitate the implementation of the proposed Kalman-Consensus filters. Together, these advances render a distributed and optimal solution to the consensus-based cooperative Kalman filter design problem. Convergence and stability of the proposed algorithms are analyzed and analytically concluded with performance verified through simulation of an illustrative example.

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm., Comment: 22 pages, 13 figures; complements and expands arXiv:2010.09321

This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.

This paper investigates command filter backstepping fault-tolerant control for a class of nonlinear systems with nonlinear faults. In particular, the system nonlinear faults are non-affine, which are handled by the filtered signal and fuzzy logic systems approximate Lemma. Command filter is used in this paper, which eliminates the “explosion of complexity” of the classical backstepping and compensates the output of the filter to the derivative of the virtual control. In addition, the controller design procedure has been shown in detail, while the stability of the closed-loop system and the convergence of the tracking error are proved via Lyapunov Theorem. Simulation examples are given to illustrate the feasibility and effectiveness of the proposed scheme.

In order to improve the performance of the sparrow search algorithm (SSA), in this paper, a novel series of SSA variants is proposed by combining SSA with improved Tent chaos mutation (IT), Lévy flights mutation (LF), elite opposition-based learning mutation (EOBL), variable radius mutation (VR) and the combination of IT, LF, EOBL, and VR, namely, ITSSA, LFSSA, EOBLSSA, VRSSA, and CMSSA, respectively. Initially, the performance of these variants is evaluated on a comprehensive set of 31 benchmark test functions. Moreover, the performance of the best algorithm among these variants is compared with 19 state-of-the-art optimization algorithms to validate its performance on 31 benchmark test functions. The convergence and computational complexity of the best variant are also analyzed to test exploration, exploitation, and local optima avoidance. It is then employed on eight real-world constrained engineering problems to further verify its robustness. The experimental results reveal that the best algorithm of SSA variants outperforms other competitors and is highly effective in solving real-life cases.

Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR’s different stages effectively. This paper focuses on classifying the DR’s different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer’s virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.

This paper investigates an adaptive coordinated control problem of spacecraft formation flying (SFF) system subject to uncertain mass and external perturbations. First, a general unified structure is proposed to represent the adjustable performance functions like the exponentially and appointed-time convergent ones. Then, an adaptive coordinated controller is developed based on a two-layer performance envelope to characterize the transient and steady-state formation maneuvering behaviors quantitatively of SFF system. Compared with the existing coordinated control approaches, the prominent advantage of the proposed one is that the preassigned position tracking and consensus tracking performance is guaranteed simultaneously while maintaining the scheduled formation configuration. Meanwhile, the users can configure and adjust the convergence rate (like the exponential or finite-time convergence) of the formation tracking errors by choosing different parameters arbitrarily in the unified structure for the performance functions. Finally, a group of numerical examples are organized to validated the effectiveness of the proposed coordinated control approach.

Enhancing the monitoring capabilities of wastewater treatment plant (WWTP) key features can accomplish accurate prediction to help WWTPs develop a plan, which is of great significance to control regional water environmental pollution. Chemical oxygen demand (COD) is one of the key features of wastewater treatment. Traditional monitoring methods are time consuming and have high costs making it difficult to meet the needs of rapid monitoring in practical applications. To address this issue, a method for optimizing a long short term memory (LSTM) neural network model based on adaptive hybrid mutation particle swarm optimization (AHMPSO) and an attention mechanism (AM) is proposed. As the hyperparameters of the LSTM are difficult to select, AHMPSO is used to optimize the LSTM. A nonlinear variable inertia weight with random factors is introduced to balance the global search ability and the local search ability and to improve the convergence speed of the PSO algorithm. In addition, the hybrid mutation strategy is added in the search process to reduce the risk of particles falling into local optimal solutions. Finally, an AM is added to the LSTM model to mine local water quality features to improve the effluent COD prediction accuracy. Compared with other models (LSTM, LSTM-AM, and PSO-LSTM-AM), the RMSE(the root mean square error) of the optimized model decreased by 7.803%-19.499%, the MAE(the mean absolute error) of the optimized model decreased by 9.669%-27.551%, the MAPE(the mean absolute error) of the optimized model decreased by 8.993%-25.996%, and the R2 (the coefficient of determination) value of the optimized model increased by 3.313%-11.229%. The experimental results show that the optimized model has better performance and achieves a more accurate prediction of the effluent COD.

Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we study the problem in a deterministic network, taking the minimization of travel time from the origin to the destination as the optimization goal. For this purpose, we construct a mathematical model for the problem. This paper proposes a reverse labeling Dijkstra algorithm (RLDA) based on traditional Dijkstra algorithm to solve the problem, it is proved that the correctness of the RLDA algorithm theoretically, and analyze that the RLDA algorithm has a lower polynomial time complexity. Finally, we selected the actual road network as the simulation experiment object to verify the effectiveness of the algorithm searching for the optimal path. And select 10 groups of networks of different sizes and conduct extensive experiments to compare the convergence efficiency and calculation speed between RLDA and PSO, GA, ACO, NNA, OPABRL. The statistical results show that the convergence rate of the RLDA algorithm is better than that of ACO, NNA, and GA. When the number of network nodes is less than 350, the algorithm has the smallest running time.

Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, we present a co-evolutionary niching strategy (CoEN) to dynamically evolve appropriate niching methods and incorporate it into differential evolution (DE) to preserve the population diversity. The proposed CoEN strategy is achieved by optimizing a criterion, which involves both fitness improvement and population diversity resulting from employing the niching methods during evolution of DE. To verify the performance of proposed method, an extensive test on benchmark functions taken from CEC2019 and CEC2014 has been carried out. The results show the significance of proposed CoEN and, by incorporating CoEN, the resulting DE is able to achieve a better or competitive performance than related algorithms.

Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions, as well as its feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance the diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/many-objective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.

This paper presents a unified algorithmic framework for nonconvex stochastic optimization, which is needed to train deep neural networks. The unified algorithm includes the existing adaptive-learning-rate optimization algorithms, such as Adaptive Moment Estimation (Adam), Adaptive Mean Square Gradient (AMSGrad), Adam with weighted gradient and dynamic bound of learning rate (GWDC), AMSGrad with weighted gradient and dynamic bound of learning rate (AMSGWDC), and Adapting stepsizes by the belief in observed gradients (AdaBelief). The paper also gives convergence analyses of the unified algorithm for constant and diminishing learning rates. When using a constant learning rate, the algorithm can approximate a stationary point of a nonconvex stochastic optimization problem. When using a diminishing rate, it converges to a stationary point of the problem. Hence, the analyses lead to the finding that the existing adaptive-learning-rate optimization algorithms can be applied to nonconvex stochastic optimization in deep neural networks in theory. Additionally, this paper provides numerical results showing that the unified algorithm can train deep neural networks in practice. Moreover, it provides numerical comparisons for unconstrained minimization using benchmark functions of the unified algorithm with certain heuristic intelligent optimization algorithms. The numerical comparisons show that a teaching-learning-based optimization algorithm and the unified algorithm perform well.

In this paper, a method based on the successive convexification is proposed to solve the ascent trajectory optimization problem, the algorithm converges to the optimal solution quickly even if the initial guess is coarse. A three-dimensional motion is formulated with complex aerodynamics and terminal constraints. Based on the modified aerodynamic coefficients, the new auxiliary control variables are designed to deal with the complex aerodynamics and non-smooth of control variables in the discrete optimization problem. The inner nonconvex constraints between the new control are relaxed to be convex without loss. The artificial infeasibility and unboundedness caused by linearization are tackled by the virtual controls and soft constraint for trust region in the successive convexification. The good convergence of the proposed method is illustrated by the iterative solutions of the ascent trajectory optimization problem for a small guided rocket, the accuracy is verified by the comparison with the optimal solution given by the typical optimal control solvers, and the feasibility and stability are demonstrated by optimal solutions of the ascent trajectory optimization problems under different missions and dispersed conditions. These excellent performances validated by the adequate simulations indicate that the proposed algorithm can be implemented online.

The development of automotive-information & communication technology (ICT) convergence has resulted in various vehicle-based electrical/electronic (E/E) systems. An automotive E/E system consists of one or more electronic control units (ECUs), sensors and actuators. With the commercialization of connected/autonomous cars, vehicle-based wireless communication systems have appeared, and are expected to grow in popularity. This increases the number of attack surfaces that can potentially threaten in-vehicle controller area network (CAN). To combat the vulnerabilities of the CAN protocol, the CAN data field should be encrypted and transmitted with authentication codes. Recently, a method of transmitting authentication codes by modifying the CAN protocol was proposed. However, changing the original CAN protocol can cause serious problems in CAN systems. In this paper, to enhance CAN security, a data compression algorithm is used to reduce the data frame length so that there is space for a message authentication code (MAC) to be contained inside the data field. The proposed algorithm guarantees that all CAN frames are authenticated by a MAC of at least four bytes without any change of the original CAN protocol. Simulations using CAN data from Kia Sorento, Kia Soul, and LS Mtron vehicles show that the proposed algorithm works successfully with only a slight increase in the peak load.

The generalized vector sampled pattern matching (GVSPM) algorithm is widely utilized in the EIT (electrical impedance tomography) reconstruction to solve the ill-posed inverse problem. An improved algorithm, which is called the generalized vector sampled pattern matching-fast (GVSPM-F), is proposed to improve the spatial resolution and reduce the iteration time based on the conventional GVSPM. The GVSPM merely applied the orthogonal projections to approximate the weights in the coordinate directions. The iteration of the proposed GVSPM-F algorithm is calculated in the projection direction of the space constructed by nonlinear correlated column vectors in the column space of the original sensitive matrix. Hence, the proposed GVSPM-F algorithm could achieve stable convergence without an empirical value to preserve the shape information and reduce the time consumption of GVSPM. In the experimental results, GVSPM-F is compared with the traditional GVSPM method in terms of voltage error, iteration time, and image error. The voltage error decreases by approximately 35%, and the number of iteration decreases from thousands to fewer than 100. The image error of GVSPM-F is 6% less than that of GVSPM. The proposed GVSPM-F algorithm is confirmed to be effective for the reconstruction of EIT images.

Multiple beam combination in optical interferometry with concurrent measurement of intricate visibilities, around each possible baseline, is a trending research area. In this work, a hybrid method is proposed for three different waveguide arrays and several waveguides are excited simultaneously in each array. Each waveguide array acts as a beam combiner, the output of which determines the field intensity of each waveguide mode. The output intensity depends on the waveguide selected for excitation. Thus, waveguide selection is the major factor that can affect the output intensity. The main goal of this research is to provide an effective solution for the selection of waveguides, to provide high visibility and intensity at the output of the multi-beam combiner. In addition to this, the use of metaheuristic optimization algorithms to solve the problem of waveguide selection is proposed. To accomplish this, firstly, an analytical study has been conducted to analyze the performance of optimization algorithms, including PSO, FA and GSA, and then the results of these algorithms have been compared with the conventional approaches. And finally, a model of Hybrid Local-Global Optimum Search Algorithm using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) has been developed for waveguide selection. The performance of the proposed hybrid model is examined in MATLAB simulation software. The simulated outcomes are determined for PSO, FA, and GSA-based models, as well as, for the proposed hybrid model, in terms of normalized intensity, visibility, and min-max 1/SNR values. The results obtained from simulation show that the PSO and GSA-based models are giving better results, followed by FA and conventional approaches. This worked as a motivation behind using PSO and GSA together in the proposed system, resulting in higher intensity and visibility values. Thus, the proposed hybrid model is concluded to be more efficient and convenient, for selecting optimum waveguides from the array, to attain an optimum output.

Particle swarm optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the natural behavior of birds flocking or fish schooling. The PSO’s main advantages are its ease of implementation and a small number of fine-tuning parameters. However, the major drawbacks of an existing PSO are its premature convergence and the lack of a balance of exploration and exploitation searches in the search space. To address the aforementioned problems, a new concept known as a smart particle swarm optimization (SPSO) process is introduced and implemented. The smart particle that leads the swarm in the proposed concept has eidetic memory behavior. The smart particle mainly works under the principles of a convergence factor (CF) technique, which integrates the memorization of particles positions, comparison, and leader declaration for the best optimal solution. Additionally, CF uses a particle position vector instead of a particle fitness or mutation to increase the exploration capability in the search space. The TEAM Workshop Problem 22, a super conducting magnetic energy storage (SMES) system; and some well-known benchmark optimization test functions are numerically solved to verify the efficacy of the proposed SPSO. The SPSO finds a better optimal solution than the other tested algorithms, particularly in the initial computational evaluation of the generation according to numerical experiments and case study analysis.