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Abstract: In this paper, we focus on the proposed algorithms to solve a linear programming problem with the convex combination of the max–min and the max–average composition and the max–star composition, respectively. They have been proposed by Ghodousian and Khorram [A. Ghodousian, E. Khorram, Solving a linear programming problem with the convex combination of the max–min and the max–average fuzzy relation equations, Appl. Math. Comput. 180 (2006) 411–418] and Khorram et al. [E. Khorram, A. Ghodousian, A. Abbasi Molai, Solving linear optimization problems with max–star composition equation constraints, Appl. Math. Comput. 179 (2006) 654–661], respectively. Firstly, we show that the “Tabular method algorithm” in the first paper and the “First procedure” in the second paper may not lead to the optimal solutions of the two models in some cases. Secondly, we generalize the proposed algorithm by Abbasi Molai and Khorram [A. Abbasi Molai, E. Khorram, A modified algorithm for solving the proposed models by Ghodousian and Khorram and Khorram and Ghodousian, Appl. Math. Comput. 190 (2007) 1161–1167] to solve the two models. In fact, it modifies the presented algorithms in the two papers. Finally, some numerical examples are given to illustrate the purposes. [Copyright &y& Elsevier]

• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this note is to show that the optimization algorithm proposed in may not lead to the optimal solution in some cases. In fact, the optimization problem remains open if we do not apply the branch-and-bound method to solve it or compute all the objective values of minimal solutions of the feasible domain. [Copyright &y& Elsevier]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

• A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. • Optimal network reconfiguration allow a better penetration of renewable energy in the system. • Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. • DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement. This study is an extension of the earlier published conference paper. [ABSTRACT FROM AUTHOR]

Nonuniform irradiance on the Photovoltaic (PV) array would cause mismatch problem and decrease the generated power. To relieve the negative effects of partial shading, reconfiguring the interconnection of the PV array is a favorable solution. However, the current reconfiguration techniques are usually limited by different corresponding factors, e.g., not suit for various instantaneous shade cases, the shortage of experts' knowledge or large amounts of data, easy coverage to local optima, and the strict assumptions for application. Therefore, in this paper a unsupervised adaptive PV array reconfiguration technique is proposed with a Divide and Conquer Q-Learning (DCQL) based array reconfiguration scheme designed to decrease the influence of partial shades. To testify the performance of the proposed PV array reconfiguration scheme, mathematic theoretical evaluation and electric characteristic analyses are made and compared with those obtained by the conventional Total-Cross-Tied (TCT) arrangement, SuDoKu connection, Genetic Algorithm (GA) based scheme, as well as some lately proposed Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimizer (HHO), and Lo Shu (LS) technique based reconfiguration methods. Besides, additional estimation indices, including mismatch loss, fill factor, percentage power loss and percentage power enhancement are calculated with substantial analyses and comparisons. Subsequently, the application of the proposed scheme in real-time conditions is evaluated, with energy saving and income analysis estimated in comparison with other eminent challenger reconfiguration schemes. At last, a comprehensive qualitative comparison assessment, with respect to dispersion ability under partial shading conditions (PSC), sensor requirement, wiring complexity and capital investment is carried out and proves the versatility of proposed methodology. • States, actions and rewards are designed in the Q-Learning based reconfiguration. • The combination of divide and conquer principle fastens the decision making process. • The proposed reconfiguration is estimated better performance through variety contrasts. [ABSTRACT FROM AUTHOR]

In this paper, an improved Reinforcement Learning (RL) based approach for adaptive Virtual Synchronous Generator (VSG) control of grid-forming inverters is proposed. High penetration of inverter-based resources causes recurrent frequency stabilization issues. For this matter, a novel model-free Actor–Critic policy optimization technique is proposed to improve frequency transients metrics such as frequency nadir and Rate of Change of Frequency (RoCoF). Stability analysis of the VSG controlled system is conducted through small signal modeling to determine the stability margins of both the virtual inertia parameter J and damping coefficient D p. The control problem is formulated in RL terms using a new technique for state, action and reward construction that improves the learning process of the agents. The proposed approach's efficiency is tested for three RL algorithms, namely, Deep Deterministic Policy Gradient (DDPG), Soft Actor–Critic (SAC), and Twin-Delayed DDPG (TD3) with special attention given to the latter. Various performance metrics such as training time and maximum cumulative episodic reward are used to compare the three algorithms. The resulting agent based VSG adaptive controllers are simulated and then compared for power quality and frequency stabilization capabilities. Finally, a comparison between non-adaptive VSG and TD3 based VSG is presented to conclude on the efficiency improvements achieved by the proposed approach. • Conventional VSG Controllers necessitate recurrent parameter adjustments. • Adaptive VSG parameter adjustment is mandatory during power disturbance events. • Model-free RL-based Adaptive VSG Controllers provide more frequency stabilization. • The TD3 Algorithm is better than DDPG and SAC for Virtual Inertia Optimization. • RL Based Adaptive VSG Methods can be improved by using a novel state formulation. [ABSTRACT FROM AUTHOR]

• Conversion time and failure rate are crucial factors for online optimization problems. • Control parameters of the swarm optimization techniques influence the performance of optimization. • The novel musical chairs algorithm (MCA) descriptions. • The simulation of the MCA compared to nine optimization algorithms. • Single-diode and double-diode models of the PV solar cells. • Parameters estimation for different operating conditions. Solar photovoltaic (PV) energy systems are representing the most attractive source of energy, especially with the continuous reduction of manufacturing costs. The new systems' sizing and cost estimation depend mainly on the accurate modeling of the PV cells. The modeling of the PV cells is based on one-diode modeling with five unknown parameters or the more accurate modeling based on two-diode modeling that have seven unknown parameters, or with a higher number of shunt diodes modeling with a higher number of unknown parameters. An accurate and fast estimation of the PV cell parameters will help designers to build their decision based on accurate results, where any small variation of these parameters can spoil the results obtained from the modeling and can produce an unwise decision. A high number of studies are introduced in the literature to estimate these parameters. These studies are different in the accuracy of results and the time consumed to get these results. Some of these studies used metaheuristic algorithms which are characterized by slow convergence and may cause inaccurate results. For this reason, a recent optimization algorithm called the musical chairs algorithm (MCA) is introduced in this paper to estimate these parameters faster and more accurately than many metaheuristic algorithms. The idea behind the use of MCA is to have a high number of search agents in the beginning to enhance the exploration and continuously reduce this number to enhance exploitation at the end of optimization and reduce the convergence time. The results obtained from using 10 optimization algorithms showed that the error associated with MCA is 20% of the average error of the other optimization algorithms. Moreover, the MCA never misses the global minimum of error which was not the case in other optimization algorithms. The MCA obtained these results in 40% of the time consumed by other optimization algorithms. These results of the MCA showed its superiority compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

The prediction of short term power load owns a great influence on performance of the whole electric system. Raising the result of power load forecasting is always a research spot. This paper proposes a method combined Elman neural network (ENN) and the particle swarm optimization (PSO) for the short-term power load forecasting. Firstly, this paper introduces the principle of ENN and PSO algorithm respectively and analyzes performance of network influenced by parameters of ENN. Then the particle swarm optimization algorithm is applied to searching the optimal learning rate of ENN. To study the capability of the method proposed in this paper, the method is utilized for the short-term power load forecasting, which is suitable to be solved by ENN. Besides, a comparison experiment on this method (PSO–ENN) with general regression neural network (GRNN), the original ENN and the traditional back-propagation neural network (BPNN) is given out to illustrate effectiveness of PSO–ENN. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this short note is to show on a numerical example that one of the two optimization algorithms proposed in [E. Khorram, A. Ghodousian, Linear objective function optimization with fuzzy relation equation constraints regarding max-av composition, Appl. Math. Comput. 173 (2006) 872–886] may not lead to the optimal solution in some cases. Besides it will be pointed out that the other algorithm (correct, but unfortunately ineffective for larger problems) proposed in [E. Khorram, A. Ghodousian, Linear objective function optimization with fuzzy relation equation constraints regarding max-av composition, Appl. Math. Comput. 173 (2006) 872–886] can be extended to a wider class of non-convex optimization problems. [Copyright &y& Elsevier]

[Display omitted] • A distributed algorithm is proposed for the generalized Nash equilibrium seeking. • The proposed algorithm is available for a more general Nash game. • The proposed algorithm can achieve an exponential convergence rate. In this paper, a distributed optimization algorithm is proposed for aggregative game with coupled constraints. Based on the singular perturbation system, the generalized Nash equilibrium is sought by a group of agents. By employing the average consensus method in the fast manifold, the aggregates in the object function can be estimated via simple information exchanges, as well as the aggregate of dual variables, which provides necessary information for the fully distributed algorithm design. Moreover, the exponential convergence of the proposed algorithm is explored based on the Lyapunov method, the properties of the variational inequality and the characteristic of the singular perturbation system. Application to the resource competition problem in smart grid verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Thermal error compensation is a simple and efficient method to reduce thermal errors of machine tools, and the compensation effect largely depends on the temperature-sensitive points screened for the thermal error modeling. In this paper, 5 spindle heating experiments are carried out, and a new temperature-sensitive point screening method based on the Improved Binary Grasshopper Optimization Algorithm (IBGOA) feature selection is proposed. Firstly, an optimal approximation criterion is added to the Binary Grasshopper Optimization Algorithm (BGOA) for ensuring the convergence of the algorithm. And the temperature measuring points are regarded as the feature of the thermal error. Then these feature temperature point subsets are generated by IBGOA. Next, the stepwise regression analysis is carried out to remove non-significant temperature points from these subsets. And each subset is evaluated to search the temperature-sensitive points based on the cross-validation result of the multiple linear regression (MLR). Finally, for further testing the applicability of the proposed temperature-sensitive points screening method, 3 common thermal error models are established with MLR , support vector regression, and back propagation neural network respectively. Compared with the traditional fuzzy C-means clustering (FCM) temperature-sensitive points screening method, the RMSE of these models could decrease by 30–50% in the thermal drift error of X-direction, 10%–30% in the thermal tilt error of Y-direction, generally 40%–60% in the thermal elongation of Z-direction and the thermal drift error of Y-direction. The results show the superiority of the proposed IBGOA-feature selection method. • An optimal approximation criterion is designed to improve the BGOA. • The IBGOA-feature selection correlates the temperature-sensitive point screening with modeling results. • The non-significant temperature points will be eliminated in the screening process. • The applicability of the proposed selection method is verified by kinds of models. • The prediction accuracy and robustness of the thermal error model are enhanced with IBGOA-feature selection. [ABSTRACT FROM AUTHOR]

The shading and blocking factor plays a significant role in the design of a heliostat field. It is the most computationally expensive task during the optimization process of a heliostat field. Over time, different methods have been developed to calculate the extent of the optical losses associated with shading and blocking. This paper focuses on the famous center point projection method and its implementation using Matlab. This work provides detailed procedures from the perspective of programming and simulation of central receiver systems. The underlying assumptions, the algorithm's strengths, and weaknesses are highlighted. The procedure and simulation parameters will help in developing optimization algorithms for designing high-efficiency heliostat fields. A dense radial staggered heliostat field is used for testing the performance of the algorithm. The field efficiency is calculated at different design points. The results show that the shading loss of the field changes with time, whereas there is negligible variation in the blocking loss. The instantaneous shading loss of a dense radial staggered field on December 21, noontime is 2.12%, whereas the blocking loss is 1.52%. [ABSTRACT FROM AUTHOR]

Multilinear model predictive control (MLMPC) can regulate a nonlinear process with wide operating regions based on a set of linear models. Although online computational cost is reduced compare to nonlinear MPC (NMPC), it is difficult to obtain a reliable full nonlinear model or set of linear models in practice. In this paper, we propose a combination of MLMPC with differential dynamic programming (DDP), so that the system can be controlled offset-free in the absence of a full nonlinear model. DDP is a 'trajectory-centric' optimization technique that solves nonlinear optimal control problems. The trajectory can be optimized even if the full model for the system is unknown, because DDP uses only the gradients around the visited trajectory, which is easily obtained by input excitations. Moreover, the gradient information can provide linear models in the subsequent MLMPC step. In the proposed scheme, a novel model selection based on gap metric and weighting method are employed for MLMPC. We prove the offset tracking property of DDP assisted MLMPC. A continuous stirred tank reactor (CSTR) process is studied to demonstrate the effectiveness of the proposed algorithms. Simulation studies show that CDDP designed by the proposed algorithm improves the trajectory over iterations, and the resulting MLMPC achieves offset-free tracking property regardless of an initial point and a set-point in the operating region. • Data-driven DDP is proposed to find the optimal trajectory without models of nonlinear systems. • LTV model is obtained from Data-driven DDP and exploited for the model of MLMPC. • A novel clustering method based on gap metric is proposed to choose linear models for MLMPC. • MLMPC with a novel prediction-based weighting method is proposed and its stability is discussed. • The proposed MLMPC strategy achieves offset-free control over large operating ranges. [ABSTRACT FROM AUTHOR]

In this paper, a fast iterative algorithm based on J -order homotopy perturbation method is proposed for the nonlinear ill-posed problem whose forward operator is not Gâteaux differentiable. The Bouligand subderivative of the forward operator is utilized to replace the Fréchet derivative in iteration system. The sequential subspace optimization technique is introduced to accelerate the convergence speed, which regards the correction term of homotopy perturbation as multiple search directions to update the new iterate. To this end, the current iteration is sequentially projected to the stripes whose width is determined by search directions, the nonlinearity of the forward operator and noise level. We present the convergence analysis based on the asymptotic stability estimates and a generalized tangential cone condition. Numerical experiments are performed to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization , the performance indices were significantly improved. • A mapping modeling between objective functions and design parameters based on Gaussian process regression is proposed. • A multi-objective optimization design method combined with the mapping model and particle swarm algorithm is proposed. • The proposed method can significantly reduce the computational cost for the high computation-intensive design problems. • A study on the comparison of the robustness and accuracy among the three mapping models is carried out. • The three mapping models are multivariate regression, BP neural network, and Gaussian process regression models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]

• Unmanned Aerial Systems provide an efficient means to collect CSP heliostat image data in-situ without interrupting plant operation. • The UAS can be programmed to collect data autonomously, satisfying constraints and user parameters to achieve images used for heliostat error characterization. • The images can be used to characterize 3 main sources of heliostat errors: slope, canting, and tracking. • The UAS is a novel approach to rapidly acquiring a large volume of this image data which can be used to benefit CSP power generation efficiency. A newly developed in situ non-intrusive optical (NIO) approach has been developed to survey various types of heliostat optical errors for a concentrating solar power (CSP) tower plant. To measure mirror surface slope error, facet canting error, and heliostat tracking error at a sub-milliradian accuracy, NIO requires several reflection images scanned over each individual heliostat. For a utility-scale plant that typically includes more than 10,000 heliostats, an unmanned aerial system (UAS) is crucial for efficient implementation of the NIO method. In this paper, we develop a flight path generation/optimization algorithm to plan more efficient UAS paths to collect NIO data over a utility-scale heliostat field. The algorithm considers NIO data requirements, all potential constraints, optimization within each subfield, and operational flexibility. Case studies are presented to illustrate the feasibility and robustness of the developed flight path algorithm. The path planning algorithm may also find applications elsewhere, such as drone-driven imaging under extreme conditions. [ABSTRACT FROM AUTHOR]

Highlights • The mobility of nodes in depth is utilized to improve tracking performance. • A complete tracking scheme including sensor selection and depth adjustment. • The method has the superiority in tracking accuracy and computation efficiency. • The observation model with additive and multiplicative noise is considered. Abstract The effective node depth adjustment of distance-measuring sensors for target tracking in underwater wireless sensor networks (UWSNs) is investigated in this paper. Due to the limited energy and bandwidth in UWSNs, there is only a part of sensors participating in the tracking task at each time step. In this paper, the mobility of sensor nodes in depth is utilized to improve the tracking accuracy on the premise of employing the same number of sensors. Firstly, the relationship between the depth of sensor nodes and direct conditional posterior Cramer-Rao lower bounds (D-CPCRLB) is derived and taken as the performance bound for tracking accuracy. Secondly, the problem of optimal depth adjustment with the constraint on total moving distance is converted into an optimization problem and the objective function of which is D-CPCRLB. Finally, by combining the optimal depth adjustment and sensor selection algorithm, the best task sensors with optimal depth adjustment are selected for the purpose of the energy-efficiency. In particular, according to the sensor combination classification, the optimization problem is disassembled into that with and without an analytical solution, which improves computation efficiency. The simulation results illustrate the performance of the proposed scheme in improving the tracking accuracy and energy consumption. [ABSTRACT FROM AUTHOR]

Abstract In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN's further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration. [ABSTRACT FROM AUTHOR]

Abstract Capturing an uncontrolled space target is a tremendously challenging research topic. Target capture by a space robot can be well planned according to predicted motion of the target. In this paper, motion prediction of an uncontrolled space target is studied and a motion prediction algorithm is proposed. In the proposed algorithm, firstly a method for identifying the parameters of motion state and inertial property of the target is established; and then through substituting the identified parameters into the dynamic equations of the target, the motion of the target can be predicted as the solution of the equations. In the identification of the parameters, the unscented Kalman filter (UKF) is applied. In order to support the UKF, a method for estimating noise level of the observation data is developed, so our motion prediction algorithm is noise adaptive. A practical convergent criterion is also designed to determine the time when the estimated result of the UKF is accurate enough, such that the predicted motion is credible enough. After that, the accuracy of the prediction is further improved by an optimization method. In the end of this paper, numerical simulations are done to verify the validity of the proposed motion prediction algorithm. Simulation results indicate that the proposed algorithm is able to predict the motion of the target precisely. [ABSTRACT FROM AUTHOR]

The penetration ability of Light Detection and Ranging (LiDAR) pulses into vegetation cover makes it a valuable tool in forest inventory. Extraction of a Digital Terrain Model (DTM) using LiDAR data is a challenging topic, especially in steep and complex terrains with forest canopy. This paper presents some approaches for ground filtering and interpolating the point cloud to generate DTM in forested terrains. Interpolating the points in dense forests that have high attitude variation is very difficult and make the popular interpolation methods unsatisfactory. This paper proposed a modified Invasive Weed Optimization (IWO) method for finding the optimized coefficients of the polynomial interpolation method. This method had a good performance with 0.210 mm RMSE value in the forested terrains. The interpolated point cloud was used as the input of the proposed ground filtering method for detecting the non-ground pixels. The proposed ground filtering method was structured with two main sections including, iterative geodesic morphology and scan labeling. In the iterative geodesic morphology, some geometric and structural parameters were introduced to investigate the quality of extracted points in each iteration. The scan labeling searched the data pixel by pixel in four directions and labeled the pixels with the high slope value in all directions. The non-ground pixels were obtained by integrating the result of iterative geodesic and scan labeling. Assessment of the ground filtering results using the International Society for Photogrammetry and Remote Sensing (ISPRS) showed 3.92% total error compared to the other reported algorithms. This demonstrated the ability of the proposed approach in recognition of the non-ground pixels. The extracted pixels were removed and the DTM was generated by filling the gaps using the proposed IWO and polynomial interpolation. Some forested regions with various characteristics such as sparse and dense trees on the hills and steep slopes were utilized to evaluate the accuracy of the generated DTM. The computed RMSE in the test areas was 0.463 m, on average, which was acceptable for the complex and forested terrains. [ABSTRACT FROM AUTHOR]

Highlights • This paper overviews the early, hidden results of topology optimization. • Optimal design of curved folded plates is presented. • Finite strip method is used for the analysis. • For the multiple loading cases two topology optimization algorithms are elaborated. • Minimization of the maximum strain energy with respect to a given volume is solved. Abstract There is an additional new fact that topology optimization has started its career more than hundred years ago by Maxwell and only a few years later by Michell. The classical solutions of the different type of plate or shell problems can be followed by the works of Mroz, Prager and Shield. This paper overviews these almost forgotten results. In addition to the conspectus of this hidden period, the optimal design of curved folded plates is presented. The finite strip method is used for the analysis. At first, a single load case is considered, but later multiple load cases are used for the design. The base formulation is a minimum volume design with displacement constraint, which is represented by the strain energy. For the multiple loading cases two topology optimization algorithms are elaborated: minimization of the maximum strain energy with respect to a given volume and the minimization of the weighted sum of the compliance of the connected load cases with respect to a given volume. The numerical procedures are based on iterative formulae, which are formed by the use of the first order optimality condition of the Lagrangian-functions. The application is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

One goal of the SPARTE code is to be used as a guide to select the experiments design parameters for future CABRI power transients. This paper focuses on methods for optimizing a specific CABRI power transient (FWHM (Full Width at Half Maximum) ≃30 ms, Deposited energy ≃130 MJ) using the target characteristics of the pulse. The selection of a method may help the experimentalists and the operation team to minimize the number of “white” power transients to perform before the final test with the fuel sample. The optimization can lead to different results, that can be ranked according to their projected uncertainties. Different optimization methods are tested and compared in this paper. The Subplex method based on reiterations of the Nelder-Mead algorithm (simplex method) was selected for its high precision. Indeed, the CABRI power transients are not completely reproducible and present some uncertainties linked to the test parameters. This article focuses on the uncertainties propagation in order to identify and select the parameters that minimize the output uncertainties. The results are very satisfactory and lead to several optimized scenarios that will be tested during the next qualification test campaign. [ABSTRACT FROM AUTHOR]

Abstract: -Means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the -means algorithm, the global -means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and uses each data point as a candidate for the -th cluster center. Results of numerical experiments show that the global -means algorithm considerably outperforms the -means algorithms. In this paper, a new version of the global -means algorithm is proposed. A starting point for the -th cluster center in this algorithm is computed by minimizing an auxiliary cluster function. Results of numerical experiments on 14 data sets demonstrate the superiority of the new algorithm, however, it requires more computational time than the global -means algorithm. [Copyright &y& Elsevier]

• Modelling and simulation of solar MPPT under partial shading conditions. • A hybrid bio-inspired LPSO algorithm is proposed. • LPSO is compared with PSO and LFO under steady-state and dynamic conditions. • LPSO algorithm has advantages in terms of zero oscillation, stable and fast tracking. In this paper, a hybrid of bio-inspired control algorithm to track the maximum power point of photovoltaic (PV) system under partial shading conditions is proposed. Particle swarm optimization (PSO) is a well-known method due to its simplicity and ease of implementation. Levy flight optimization (LFO) is a random walk distribution which is also simple and able to provide fast response. In the proposed algorithm, these two methods are integrated together, noted as a hybrid of Levy flight and particle swarm optimization (LPSO) to extract the global maximum power point (GMPP). The proposed LPSO is evaluated under three conditions: (1) under uniform irradiance (2) under non-uniform irradiance and (3) under step-change of irradiance. A prototype is built to verify the effectiveness of the proposed LPSO. Based on the results obtained, it clearly shows that the hybrid LPSO can track the local and global maximum power point effectively. Both simulation and experimental results show that the proposed LPSO is stable and efficient with zero steady-state oscillation. The efficiency of the proposed LPSO is approximately 99.50% for all tested conditions. [ABSTRACT FROM AUTHOR]

This paper studies the distributed resource allocation problem with nonsmooth local cost functions subject to the coupled equality and inequality constraints. In particular, each local cost function is expressed as the sum of a differentiable function and two nonsmooth functions. By using the operator splitting and primal–dual method, a continuous-time distributed proximal algorithm is developed, which can be applied to more general local cost functions that are convex but not necessarily smooth. In addition, the proposed algorithm is fully distributed in the sense that the gain parameter can be determined locally and does not require any global information of the network. By applying Lyapunov stability analysis and convex optimization theory, it is shown that the decision variables of all the agents converge to an optimal solution. Finally, a simulation example is carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]