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To automatic detect and characterize paper impurities with computer vision, we present a novel two parts evaluation procedure with feature representations using Alternating Direction Method of Multipliers (ADMM) sparse codes. The method is based on an offline training step to obtain sparse coefficients and codebooks via learning extracted features with ADMM optimization, followed by an online detection step to use linear SVM classifier to assess defective paper samples from no-defective ones. Our approach bridges the gap between paper impurities evaluation and sparse feature representations, taking advantages of existing ADMM algorithms to handle sparse codes problem. We compare different feature descriptors and sparse code methods to implement the procedure and experimentally validate it on a dataset of 11 paper classes. Experiment results show that the proposed method is competitive and effective in terms of evaluation accuracy and speed. [ABSTRACT FROM AUTHOR]

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network. [ABSTRACT FROM AUTHOR]

Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts' linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts' evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs' specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed. [ABSTRACT FROM AUTHOR]

Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. [ABSTRACT FROM AUTHOR]

Motivated by Hema Freshs new-retail case, we noticed that an effective recommender system is a common way to attract the consumers' purchasing behaviors and thus enlarge the profit of platform as well as retailers. With the aim of increasing the benefits of all parties in the platform, this paper focusing on not only increasing the effectiveness of the recommender platform but also the evaluation system of measuring the interests of consumer, retailers and platform. In this paper, the interests of the third-party platform are added into the evaluation system, the profit of the third-party platform as an evaluation index is taken and a 0–1 integer programming model is established which sets the profit of the platform as the objective function. The result of the proposed model and algorithm indicate that: (1) The relevance of products has a significant impact on platform recommendation when the consumers are selecting products. When the correlations of the products are high, the algorithms of selecting the products will have a lower capacity of 1% compared with the algorithm without products correlations. (2) The evaluation of the target products from the target consumers is quite different from the heterogeneity assumptions. When the consumer presentation is taken into consideration, it is hard to evaluate the consumer presence because of the strictly requirement of data for the platform recommendation system. (3) The proposed two-stage solution for the platform recommendation system is optimized in time and space complexity. Total optimization of the proposed method is 30% higher than the greedy algorithms. The two stages are combined together to obtain the approximate solution, and finally provide a reasonable and feasible recommendation for the third-party platform. [ABSTRACT FROM AUTHOR]

With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm's optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work. [ABSTRACT FROM AUTHOR]

Filter Bank Multicarrier(FBMC)is considered as one of the most standardized waveform for fifth generation (5G) mobile communication system application FBMC endures lot of nonlinear effects which occurs because of high Peak Average Power Ratio (PAPR). High value of PAPR due to the large dynamic range of multicarrier signal is one of the most significant issues in FBMC multicarrier based modulation technique. This paper presents one investigated PAPR reduction technique named as Selected Mapping (SLM) to minimize high PAPR by utilizing the complex signal divide into real and imaginary parts and then select minimum PAPR signal based on Modified Forest Optimization Algorithm (MFOA)to achieve good PAPR which can maintain the FBMC based system performance with a required Bit Error Rate (BER). The associated method was produced with the aim of optimize the phase factors so that the phase rotation operation is accomplished to minimize PAPR by fixing the MFOA into the conventional SLM. The simulation results demonstrate that the proposed technique gives better performance in terms of BER and PAPR compared to other SLM based optimization techniques. [ABSTRACT FROM AUTHOR]

Due to the importance of the multi-level fully rough interval linear programming (MLFRILP) problem to address a wide range of management and optimization challenges in practical applications, such as policymaking, supply chain management, energy management, and so on, few researchers have specifically discussed this point. This paper presents an easy and systematic roadmap of studies of the currently available literature on rough multi-level programming problems and improvements related to group procedures in seven basic categories for future researchers and also introduces the concept of multi-level fully rough interval optimization. We start remodeling the problem into its sixteen crisp linear programming LP problems using the interval method and slice sum method. All crisp LPs can be reduced to four crisp LPs. In addition, three different optimization techniques were used to solve the complex multi-level linear programming issues. A numerical example is also provided to further clarify each strategy. Finally, we have a comparison of the methods used for solving the MLFRILP problem. [ABSTRACT FROM AUTHOR]

In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process. [ABSTRACT FROM AUTHOR]

Radial basis function neural network (RBFNN) has been widely used in industrial process modeling because of its strong approximation ability. However, many existing modeling methods aim at accuracy, but ignore the stability of mode. Therefore, this paper proposes a parameter optimization method of RBF neural network based on modified Levenberg-Marquardt (MLM-RBFNN) to ensure the stability of the network. Firstly, a typical sample mechanism with variance reduction is proposed, which can reduce the error of gradient estimation and use accurate gradient information to guide learning. Secondly, a modified LM optimization algorithm is proposed to optimize the parameters, which not only improve the convergence speed of the network, but also ensure the stability of the model. Finally, a multi-step updating rule based on a typical sample and a single sample is designed, which effectively reduces the sample bias introduced by a single sample. In order to prove the advantages of the MLM-RBFNN method proposed in this paper, experiments are carried out on three benchmark data sets and a practical wastewater treatment process application problem and compared with several existing methods. The results show that the proposed MLM-RBFNN method has good performance in both learning speed and stability. [ABSTRACT FROM AUTHOR]

This paper proposes a collaborative optimization method for transportation planning and land use. This method combines system dynamics models, structural equations, DEA evaluation and BP neural network technology to construct a complete transportation planning and land use collaborative optimization system. In this system, system unit determination is a key problem, which has a different relationship with each stage. This paper uses the DEMATEL model to construct a causality diagram and an incidence matrix for the system units, to identify the central influencing unit and causal influencing unit of the method of collaborative optimization of transportation planning and land use. While simplifying the model through the DEMATEL model, the macro interaction mechanism between urban traffic and land use will be clarified. Further, the extracted key influence units can be micro-decomposed to construct subsystems of micro-elements, which makes preliminary preparation for the analysis of static and dynamic integration model. [ABSTRACT FROM AUTHOR]

The automatic generation of game mechanics is nowadays one of the most complex challenges within procedural content generation (PCG), even being considered by itself as automatic game generation in the literature. Previous works have contributed with research papers related to PCG in general, and, in particular, the generation of game mechanics from a specification written in a game description language (GDL). One primary reference is the Ludii general game system that allows to generate new mechanics for two-player combinatorial (board) games. Ludii manages game specifications written in a GDL that can be directly represented in a tree structure. These structures are then evolved by means of genetic programming (GP), a well-known bio-inspired optimization technique, to produce new game rules. Now, this paper extends the approach centered on 2-player games and proposed in the Ludii general game system to n-player videogames. The paper describes a system to automatically generate videogame mechanics. The starting point is a videogame specification written in the XML-based videogame description language (XVGDL). Similarly to Ludii, this videogame specification can be directly translated to rule tree structures that can be evolved by GP. The viability of this approach is demonstrated by experimentation with practical examples. The experiments show how it is possible to automatically produce and evaluate a number of distinct versions (that differ in their mechanics) of a videogame that was originally specified in XVGDL. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Takagi–Sugeno fuzzy model identification method by combining fuzzy c-regression model clustering (FCRM), least squares support vector machine (LSSVM) and intelligent optimization algorithm. Firstly, in order to improve the performance of FCRM for the complex nonlinear dataset, in this paper the method of FCRM based on LSSVM (FCRM-LSSVM) is proposed to discover the data structure and obtain the antecedent parameters. And then, a newly developed intelligent optimization algorithm by hybridizing Harris hawks optimization and moth-flame optimization algorithm (IHHOMFO) is proposed to further optimize the antecedent membership function parameters obtained by the FCRM-LSSVM. Finally, the proposed novel T-S fuzzy model identification combines FCRM, LSSVM and IHHOMFO for solving actual model identification problems. Experiments on five different datasets demonstrate that the proposed method is more efficient than conventional methods, such as T-S model identification based on fuzzy c-means (FCM), FCRM and FCRM-LSSVM, in standard measurement indexes. This study thus demonstrates that the proposed method is a credible and competitive fuzzy model identification method. The novel method contributes not only to the theoretical aspects of fuzzy model, but is also widely applicable in data mining, image recognition and prediction problems. [ABSTRACT FROM AUTHOR]

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions. [ABSTRACT FROM AUTHOR]

In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm's convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm's global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems. [ABSTRACT FROM AUTHOR]

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). [ABSTRACT FROM AUTHOR]

Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. [ABSTRACT FROM AUTHOR]

The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). [ABSTRACT FROM AUTHOR]

Impedance mismatching between different phases of a multiphase transformer is generally observed e.g., in a three-phase to seven-phase transformer, due to an unequal number of turns in different coils. This mismatching introduces error in the study of per phase equivalent circuit diagrams as well as induces an imbalance in output voltages and currents. Therefore, it is a challenging task to develop a per-phase equivalent circuit for the secondary and primary sides (In some cases) too. This paper proposes an artificial intelligence optimization technique like PSO based modeling of the per-phase equivalent circuit of the secondary side. This paper deals with the modeling and simulation of a three-phase to seven-phase power transformer using Artificial Intelligence technique like particle swarm optimization (PSO) and Genetic Algorithm (GA). The proposed model is optimized through PSO and GA algorithms and tested for minimum voltage error in each phase. The proposed model is designed and the objective function is optimized by PSO & GA in MATLAB environment. It is found that the optimized model can be effectively implemented as a per-phase equivalent circuit for the secondary side. [ABSTRACT FROM AUTHOR]

Planning a collision-free path while preserving processing time and minimizing cost function has been considered a significant challenge in developing an Autonomous Mobile Robot (AMR). Various optimization techniques for avoiding obstacles and path planning problems have been proposed recently. But, the computation time for executing these techniques is comparatively higher and has lesser accuracy. In this paper, the State Estimation Obstacle Avoidance (SEOA) algorithm has been proposed for estimating the position and velocity of both of the wheels of the AMR. Moreover, this algorithm has been also applied in path planning for reaching the destination point in minimum computational time. Five different positions of static obstacle are demonstrated in a real time static environment where the proposed SEOA algorithm has been compared with state-of-the-art path planning algorithms such as A* and VFH. The simulation results demonstrate that the proposed algorithm takes lesser computational time to generate the collision free path when compared to other mentioned algorithms. [ABSTRACT FROM AUTHOR]

Glaucoma is an irreversible blindness that affects the people over the age of 40 years. Many approaches are proposed to detect glaucoma in image by dealing with its complex data. Redundancy is the major problem in medical image which could lead to increased false positive and false negative rates. This paper proposed a three-structure CNN optimized with Hybrid optimization approach for glaucoma detection and severity differentiation. The CNN structure is designed with three sub-groups to do attention prediction, segmentation and classification. The mathematical equation for Loss function is derived for the CNN structure with three hyper-parameters which is optimized with Hybrid approach. Hybrid optimization approach consist of Muddy Electric fish Optimization and Grass hopper optimization algorithm for exploration and exploitation processes. The proposed method is designed in a Matlab and validated with LAG and Rim-One database. The proposed method achieved accuracy greater than 95% and other metrics like F2 and AUC has reached 98%. [ABSTRACT FROM AUTHOR]

The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect. [ABSTRACT FROM AUTHOR]

Twin proximal support vector regression is a new regression machine designed by using twin support vector machine and proximal support vector regression. In this paper, we use the above models framework to build a new regression model, called the twin proximal least squares support vector regression model based on heteroscedastic Gaussian noise (TPLSSVR-HGN). The least square method is introduced and the regularization terms b 1 2 and b 2 2 are added respectively. It transforms an inequality constraint problem into two simpler equality constraint problems, which not only improves the training speed and generalization ability, but also effectively improves the forecasting accuracy. In order to solve the parameter selection problem of model TPLSSVR-HGN, the particle swarm optimization algorithm with fast convergence speed and good robustness is selected to optimize its parameters. In order to verify the forecasting performance of TPLSSVR-HGN, it is compared with the classical regression models on the artificial data set, UCI data set and wind-speed data set. The experimental results show that TPLSSVR-HGN has better forecasting effect than the classical regression models. [ABSTRACT FROM AUTHOR]

Through scientific theoretical methods, we take the internal control optimization of the Financial Sharing Center of H company as the research object. Firstly, we introduce the Financial Sharing Center and the development background and research significance of internal control under this mode, sort out the existing international research and related concepts, analyze the problems existing in the internal control stage of the Financial Sharing Center, and analyze the problems one by one from the five elements of internal control. What is more innovative is that we use the quality function deployment theory in the field of system science, combined with the intuitionistic fuzzy set theory, G1 method and entropy method in fuzzy mathematics to evaluate the five elements affecting the internal control optimization of the Financial Sharing Center of H company, and give the priority of optimization in theory. Finally, according to the implementation conditions of the Financial Sharing Center, this paper puts forward relevant countermeasures and suggestions to optimize the internal control of the financial sharing mode of H company, which can also provide experience for other enterprises that are building the Financial Sharing Center. [ABSTRACT FROM AUTHOR]

Recently, energy-saving scheduling issues have attracted more and more attention in the manufacturing field. Meanwhile, in practical production, maintenance planning is viewed as a vital task in the workshop. However, the existing literature about energy-saving scheduling problems rarely consider the effect of preventive maintenance. Therefore, this paper investigates an energy-saving flexible job shop scheduling problem with preventive maintenance. A mathematical model is proposed considering the minimization of total energy consumption. To solve the problem, a novel discrete elephant herding optimization algorithm (NDEHO) is proposed according to the problem's characteristics. To test the NDEHO's performance, the Taguchi design of experiment approach is adopted to get the best combination of parameters in the algorithm. Numerical experiments are conducted based on twenty-four instances, including four benchmark instances and twenty randomly generated instances. Computational data indicate that NDEHO outperforms other compared algorithms for solving the considered problem. [ABSTRACT FROM AUTHOR]

Chaotic systems are dynamic systems with aperiodic and pseudo-random properties, and systems in many fields exhibit chaotic time-series properties. Aiming at the fuzzy modeling problem of chaotic time series, this paper proposes a new fuzzy identification method considering the selection of important input variables. The purpose is to achieve higher model modeling and prediction accuracy by constructing a model with a simple structure. The relevant input variable was swiftly chosen in accordance with the input variable index after the Two Stage Fuzzy Curves method was used to determine the weight of the correlation between each input variable and the output from a large number of selectable input variables. The center and width of the irregular Gaussian membership function were then optimized using the fuzzy C-means clustering algorithm and the particle swarm optimization technique, which led to the determination of the fuzzy model's underlying premise parameters. Finally, the fuzzy model's conclusion parameters were determined using the recursive least squares method. This model is used to simulate three chaotic time series, and the outcomes of the simulation are contrasted and examined. The outcomes demonstrate that the fuzzy identification system has higher prediction accuracy based on a simpler structure, demonstrating its valid [ABSTRACT FROM AUTHOR]

Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model. [ABSTRACT FROM AUTHOR]

Today optimization algorithms are widely used in every application to increase quality, quantity and efficiency of making products as well as to minimize the production cost. Most of the techniques applied on different applications try to satisfy more than one parameter of interest in the design problem. In doing so, an objective function based on weighted aggregation has been designed to fulfill multi-objective optimization (MOO). A lot of computational time and energy is wasted in tuning the value of weighting factor in terms of number of trials each having hundreds of iterations to achieve the optimum solution. To reduce such tedious practice of adjustment of weighting factor with multiple iterations, Fuzzy technique is proposed for auto-tuning of weighting factor in this paper that will benefit the researchers who are working upon optimization of their designed objectives using artificial intelligence techniques. This paper proposes MOO settlement method that does not require complex mathematical equations in order to simplify the weight finding problem of weighted aggregation objective function (WAOF). The results have been compared in terms of time and space efficiency to show the importance of Fuzzy-WAOF (F-WAOF). Further the results taken on Automatic Voltage Regulator (AVR) system for set point tracking, load disturbance, controller effort and modelling errors, prove the superior performance of the proposed method as compared to state of the art techniques. [ABSTRACT FROM AUTHOR]

The power battery is a key component of the electric vehicle, and its State of health (SOH) parameters directly affect the safety and reliability of the electric vehicle. Considering the problem of the reduced SOH estimation accuracy of Li-ion battery, this paper proposes a joint algorithm of the firefly algorithm-back propagation neural network K-means (FA-BPNN-K-means) for SOH estimation to alleviate the wide voltage platform and severe polarization. In particular, the BPNN model of the battery is first established. The ohmic resistance, polarization resistance, and polarization capacitance of the battery are used as the input parameters of the model, and SOH was used as the output parameters. Secondly, the firefly algorithm (FA) is used to optimize BPNN for SOH estimation of Li-ion battery, solving the problem that BPNN is easy to fall into the local minimum and the convergence rate is slow. Finally, the predicted output of the FA-BPNN model is substituted into the K-means algorithm for clustering, and the data points for evaluation are obtained to reduce the cumulative error caused by the battery model. Compared with the BPNN algorithm, FA-BPNN-K-means joint optimization algorithm, obtaining lower error in SOH estimation, and it has good convergence. Besides, it is accompanied by higher prediction accuracy, which can guarantee the stable operation of the battery management system. [ABSTRACT FROM AUTHOR]

Small and medium-sized enterprises (SMEs) have been suffering from the problem of "difficult and expensive financing." The few loans available are allocated unevenly to SMEs, resulting in a financing mismatch. To address this problem, this paper constructs a system dynamics model to simulate and calculate the theoretical loan amounts of SMEs. Compares the values between the theoretical and actual loan amount to find out the enterprises with extreme values of the difference. Then, it analyzes the characteristics of the index data of these enterprises to explore feasible solutions to solve the financing mismatch problem. The CRITIC and AHP weight determination methods will be used to calculate weights and screen indicators. The data will be quantified and calculated according to the standardized scoring table of enterprise borrowing factor indicators. The results show that enterprises with actual borrowing amounts higher than the theoretical borrowing amount perform better than other sample enterprises in three indicators: growth rate of primary business income, the upper limit of the loan amount, and subsidy amount. The actual loan amount can be increased by improving these indicators. This research can help solve the problem of financing mismatch to realize effective resource allocation and provide significant guidance for the research on enterprises to obtain more loans. [ABSTRACT FROM AUTHOR]

Jaya, a simple heuristic algorithm, has shown attractive features, especially parameter-free. However, the simple structure of Jaya algorithm may result in poor performances, to boost the performance, a multi-strategy Jaya (MJaya) algorithm based on multi-population has been proposed in this paper. Three strategies correspond to three groups of solutions. The first strategy based on the first population is to introduce an adaptive weight parameter to the position-updating equation to improve the local search. The second strategy is based on rank-based mutation to enhance the global search. The third strategy is to exploit around the best solution to reinforce the local search. Three strategies cooperate well during the evolution process. The experimental results based on CEC 2014 have proven that the proposed MJaya is superior compared with Jaya and its latest variants. Then, the proposed MJaya algorithm is used to solve three industrial problems and the results have shown that the proposed MJaya algorithm can also solve complex industrial applications effectively. [ABSTRACT FROM AUTHOR]

This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

In order to solve the premature convergence of multi-objective evolutionary algorithm, a two-stage diversity enhancement differential evolution algorithm for multi-objective optimization problem(TSDE) is proposed. The offspring with better performance needs the generation of high-quality parent generation. In this paper, an improved cell density method is used to screen for the high quality parents by estimating the global distribution of the objective space. Moreover, Principal Component Analysis operator is introduced to the external archive to perturb the non-dominated solution, which not only ensures the convergence but also improves the diversity. In order to verify the effectiveness of the algorithm, TSDE and other advanced methods are run on 19 test functions. The results show that TSDE performs better than other algorithms. [ABSTRACT FROM AUTHOR]

Cuckoo search algorithm (CS) is an excellent nature-inspired algorithm that has been widely introduced to solve complex, multi-dimensional global optimization problems. However, the traditional CS algorithm has a low convergence speed and a poor balance between exploration and exploitation. In other words, the single search strategy of CS may make it easier to trap into local optimum and end in premature convergence. In this paper, we proposed a new variant of CS called Novel Enhanced CS Algorithm (NECSA) to overcome these drawbacks mentioned above inspired by the cuckoos' behaviors in nature and other excellent search strategies employed in intelligent optimization algorithms. NECSA introduces several enhancement strategies, namely self-evaluation operation and modified greedy selection operation, to improve the searchability of the original CS algorithm. The former is proposed to enhance the exploration ability and ensure population diversity, and the latter is employed to enhance the exploitation ability and increase search efficiency. Besides, we introduced adaptive control parameter settings based on the fitness and iteration number to increase the convergence speed and the accuracy of the search process. The experimental results and analysis on the CEC2014 test have demonstrated the reliable performance of NECSA in comparison with the other five CS algorithm variants. [ABSTRACT FROM AUTHOR]

In today's world, Software-Defined Networking (SDN) plays a significant role in the advancement of next-generation network architecture that offers vast control to the network operators. However, the control layer is vulnerable to Distributed Denial of Service (DDoS) attacks where DDoS is one of the most powerful and devastating cyber-attacks. Thus, the development of a DDoS attack detection mechanism is very essential since these kinds of attacks have a direct impact on the overall performance of the SDN. In this paper, a new robust Tuned support vector machine-based DDoS attack detection methodology has been proposed to categorize the benign traffic from DDoS attack traffic on the SDN. Primarily, the network is created with controller and OpenFlow switch and the communication can be carried out through secure channels among different benign users and also attackers. Afterward, the multi-characteristic values are extracted by the effective extraction strategy which consists of the six-tuple characteristic values matrix. Finally, the tuned classifier has been implemented with the aid of optimization algorithm for differentiating the abnormal traffic and the normal traffic. The performance results manifest that the proposed detection framework achieves a higher accuracy of 98% and precision of 99% when compared with existing classifiers. [ABSTRACT FROM AUTHOR]

Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data. [ABSTRACT FROM AUTHOR]

From the recent study, it is observed that even though cloud computing grants the greatest performance in the case of storage, computing, and networking services, the Internet of Things (IoT) still suffers from high processing latency, awareness of location, and least mobility support. To address these issues, this paper integrates fog computing and Software-Defined Networking (SDN). Importantly, fog computing does the extension of computing and storing to the network edge that could minimize the latency along with mobility support. Further, this paper aims to incorporate a new optimization strategy to address the "Load balancing" problem in terms of latency minimization. A new Thresholded-Whale Optimization Algorithm (T-WOA) is introduced for the optimal selection of load distribution coefficient (time allocation for doing a task). Finally, the performance of the proposed model is compared with other conventional models concerning latency. The simulation results prove that the SDN based T-WOA algorithm could efficiently minimize the latency and improve the Quality of Service (QoS) in Software Defined Cloud/Fog architecture. [ABSTRACT FROM AUTHOR]

In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements. [ABSTRACT FROM AUTHOR]

The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs. [ABSTRACT FROM AUTHOR]

In this paper, we studied the resource constrained project scheduling problem, and the research object is extended to the multi-project environment. On the basis of multi-project priority evaluation, with the goal of minimizing the weighted duration of multi-project, a multi-project schedule planning model is constructed. Through reasonable scheduling of multiple parallel projects sharing resources under resource constraints, it provides a decision-making basis for project managers to allocate resources reasonably under resource constraints to meet the duration requirements of each project and to shorten the weighted total duration of multiple projects as much as possible. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist Retention (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of particle swarm optimization algorithm with dynamically changing inertia weight. Through the dynamic changing inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can be used to solve the RCMPSP model more effectively. [ABSTRACT FROM AUTHOR]

To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms. [ABSTRACT FROM AUTHOR]

Cardio vascular disease threatens human life with higher mortality rate. Therefore it is quite important to monitor. An arrhythmia is an abnormal heart beat and rhythm which causes the disease. The best tool to find the heart rhythm of heart is Electro Cardiogram (ECG) which provides information about the different types of arrhythmias. This paper aims at proposing an automatic framework by employing multi-domain features to classify ECG signals. Proposed work uses optimum method of feature selection to improvise the efficiency of the classification process. A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm Optimization (PSO) and Migration Modified Biogeography Based Optimization (MMBBO) algorithm. Algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4–5% over the comparison of other methods. [ABSTRACT FROM AUTHOR]

Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs' production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource's different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population's diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the efficiency improvement of permanent magnet synchronous motor using gravitational search algorithm as an optimization tool. The gravitational search algorithm (GSA) is a recently developed meta-heuristic optimization algorithm, which so far has proven to be quite suitable for solving power engineering optimization problems. The aim of this research work is to implement this novel optimization algorithm for the efficiency improvement of permanent magnet synchronous motor, where the objective function in the optimization process is the efficiency of the investigated motor. Comparative analysis of the initial and a number of optimized solutions of the motor model is performed. [ABSTRACT FROM AUTHOR]

The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems. [ABSTRACT FROM AUTHOR]

The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method's five steps. Finally a post-processing step is utilized. For recognizing the proposed method's effectiveness, the results are then contrasted with the existent methods. [ABSTRACT FROM AUTHOR]

Conventionally in many countries, electrical power industry is organized as vertically integrated system. Under this system, large utilities are authoritative for the generation, transmission and distribution of electrical power. Such utilities are governed by the rules and regulations of the government and are forced to operate within the prescribed guidelines with minimal profit. This confirmation causes an ineffective and sluggish perspective in power industry with a lack of technical innovation, competent management and customer satisfaction. To overcome these deficiencies, power sector around the globe is getting restructured. This paper addresses an inevitable technical disputes occurring in deregulated environment i.e., transmission congestion which has an adverse effect on system security, increase in electricity pricing and line losses. Flexible AC Transmission System (FACTS) is a boon to the power sector which helps in a better and reliable power flow through the transmission lines. The problem is articulated as a multi objective function satisfying all the operational and security limits. Three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS) and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem. To study the effectiveness of the proposed objective, IEEE 14 bus system was considered as the test system. In order to validate the proposed methodology three congestion cases i.e. bilateral transaction, multilateral transaction and overloading were imposed on the test bus system. Simulation was carried out in MATLAB. [ABSTRACT FROM AUTHOR]

In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea "ML to choose heuristics" (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems. [ABSTRACT FROM AUTHOR]