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This paper proposes a predictive control algorithm for the speed and displacement tracking of urban rail trains. Firstly, the train dynamics model is constructed considering the resistance existing in the actual operational scenarios. Secondly, based on the model predictive control (MPC) framework, a control objective function for tracking the desired speed and displacement and the stable change of control quantities is designed. Finally, combined with constraints of the train operation, the proposed MPC algorithm for the train tracking control problem is transformed into quadratic programming with inequality constraints, thereby facilitating a solution with the commonly-used solvers. Experimental results demonstrate that the proposed algorithm can effectively enhance the speed and displacement control performance while improving energy efficiency, ensuring the safety, stability, and riding comfort of the train. [ABSTRACT FROM AUTHOR]

The progressive iterative approximation (PIA) is a versatile method for interpolating or fitting a given data set. The convergence behavior of PIA plays a pivotal role in determining the computational efficiency of data interpolation or fitting. This paper introduces an accelerated iterative approach, GS-PIA, derived from the Gauss-Seidel splitting, specifically designed for interpolating data points. The convergence and computational cost of the proposed iterative method are discussed. Numerical results underscore the superiority of GS-PIA in data interpolation when compared to other existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making approach grounded in rough set theory and evidential reasoning to address the demand for expert decision-making in greenhouse environmental control systems. Furthermore, a decision-making model is developed by integrating the D-S evidence theory with an expert knowledge table for greenhouse environmental control systems. The model's reasoning process encompasses continuous attribute discretization, expert decision table formation, attribute reduction, and evidence combination reasoning. Firstly, the fuzzy C-means clustering algorithm is employed to discretize the original environmental data and cluster it. Subsequently, an attribute reduction algorithm based on information entropy is utilized to optimize the decision table by eliminating unnecessary conditional attributes in expert knowledge. The reduced indicators are then combined using evidential theory. Finally, suitable greenhouse control methods are determined by the confidence decision proposed by the D-S evidence theory. To assess the efficacy of this intelligent decision-making algorithm based on rough set and D-S evidence theory, its performance is compared with traditional SVM algorithms and small-shot learning algorithms. The results indicate that this proposed method significantly enhances the credibility of control decision-making processes, with an average running time of 0.002378s for the fusion decision algorithm and 0.017939s for the support vector machine (SVM) algorithm, respectively. The SVM accuracy rate after testing and training stands at 90.34%. Moreover, retraining based on information entropy attribute reduction leads to a correct decision rate increase of up to 100%. This method notably improves confidence levels in decision-making processes while reducing uncertainty and demonstrates reliability when applied in making decisions regarding greenhouse environments. [ABSTRACT FROM AUTHOR]

Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance in real-world environments such as criminal investigation and statistical analysis of commercial foot traffic and has received extensive attention from the academic community. However, traditional methods such as manual extraction cannot adapt to largescale data volumes, and deep learning-based methods at this stage suffer from interference in complex environments such as similar costumes, perspective changes, and occlusion. Therefore, in this paper, we investigate the above problems. Firstly, we expand the dataset by introducing random erasure-based preprocessing of pedestrian images to enhancing the robustness and generalization capability of neural networks. Secondly, a composite attention mechanism is introduced after the network residual layer to enhance the spatial information capability and feature expression. Finally, the union loss composed of Circle Loss, Ternary Loss, and Cross Entropy Loss was chosen for network training in the loss optimization phase. Findings from the experiments reveal that the improved method proposed in this experiment achieves 96.0%Rank-1 and 88.3%mAP in Market1501, which reflects the validity of the approach proposed in this manuscript, and provides valuable reference suggestions for Person Re-Identification related research. [ABSTRACT FROM AUTHOR]

This paper intends to develop a new integral filled function method with four properties. First, the proposed filled function is non-exponential and non-logarithmic. This property is aimed at avoiding the overflow effect during the computational stage. Second, our method does not contain parameters, so the possibility of non-converging iterations can be prevented. Third, the filled function proposed in this paper is continuously differentiable. By this property, any local minimization procedures can be applied. Finally, the suggested filled function is dependent on the objective function, and thus it can provide any information for locating a minimum of the filled function. To show that the proposed method is competitive, a comparison has been made with one of the deterministic approaches, namely the DIRECT method. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

We improve the approximated solution for the inventory model with a linear trend in demand such that there is no necessary to adjust the last or the first time interval. Our formula is easier to compute. We estimate our approximated result to the optimal solution of the equal time interval constraint, then our result is measured with an error of less than two-thirds. For the second inventory system examined in this paper, we consider an inventory system with two retailers and a manufacturer. Through the algebraic process, we obtain the optimal profit that is missing in the previously published paper. We show the elegant character of the algebraic process. [ABSTRACT FROM AUTHOR]

This paper proposes a robust identification and control-based on a neural network method for a Twin Rotor Multivariable System (TRMS) using a recursive adaptive training algorithm. The algorithm is based on a recursive least squares approach with an additional steepest descent stage. An Adaline neural network is used for modeling the system, and a robust structure is selected based on a linear auto-regressive structure with exogenous inputs (ARX) related to the estimation error. The identification is performed online and the system is controlled under a polynomial structure by pole placement with a dead-beat strategy. The method is evaluated in terms of estimation and tracking error in the presence of external additive disturbances, parametric disturbances, and sinusoidal reference signals. The Root-Mean Square Error (RMSE) is used to evaluate the estimation performance and the Integral-Time Absolute Error (ITAE) is used to evaluate the tracking performance. As a result, a novel robust controller based on a neural network is designed where the best results are obtained for a training recursive least squares algorithm with an additional steepest descent stage. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is evolving rapidly and has made many theoretical breakthroughs while widely applied in various fields. ML allows systems the ability to access data and use it to enable computers to execute cognitive processes such as learning and improving from previous experiences and solving complicated issues. Many first-order stochastic optimization methods have been used to solve the optimization model of ML. These algorithms adopt Barzilai-Borwein (BB) step size instead of fixed or diminishing step size to improve performance. However, the BB step size format involves fractional calculation, which inevitably leads to a zero denominator, especially when the objective function is non-convex. The BB technique will be violated if the denominator is near 0 or even negative. To improve the computation of the step size, a Positive Defined Stabilized Barzilai-Borwein (PDSBB) approach is introduced in this paper. Integrating PDSBB with the stochastic variance reduced gradient (SVRG) approach, a new method SVRG-PDSBB is proposed. Numerical experiments have shown that the new algorithm has stabilized the performance of the new step size, which successfully avoiding zero denominators and effectively solving the common problems in machine learning. The convergence of SVRG-PDSBB is theoretically and numerically proven, and the effectiveness of the new algorithm is shown by comparison with other algorithms. [ABSTRACT FROM AUTHOR]

The Lotka-Volterra competition model consisting of two equations is studied. The existence and uniqueness of solutions on an infinite interval are proved by using the Schauder fixed point theorem, Gronwall’s inequality and some special analytical techniques. Some conditions of existence for positive solutions are obtained. Iterative algorithms and error estimations for solving this model are established. The results of this paper can be generalized to the cases consisting of more than two equations. [ABSTRACT FROM AUTHOR]

Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approach in this paper. The proposed algorithm is referred to as AP-MODE, which improves the search efficiency by utilizing the affinity propagation approach to find out the population distribution structure for guiding search. In addition, mating restriction probability is used to select parent individuals for recombination from the neighborhoods or the whole population. Meanwhile, the mating restriction probability is updated according to the non-dominated count approach at each generation. This proposed algorithm is verified by comparing it with some state-of-the-art multi-objective evolutionary algorithms, and the simulation results on DTLZ test problems indicate that AP-MODE can efficiently achieve two goals of multi-objective optimization, i.e., the convergence to actual Pareto front and uniform spread of individuals along Pareto front. [ABSTRACT FROM AUTHOR]

The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales. [ABSTRACT FROM AUTHOR]

G(V, E) represents the basic chart without circle, if existing a one-to-one mapping f:E(G) → {1,2,...,|E|}, for any two vertices in the diagram, in the event that d(u)= d(v). S(u)=S(v), where S(u)= Σuw∈E(G)f(uw), d(u) represents the degree of the vertex u, then call the mapping f: Adjacent Vertex Reducible Edge Labeling (alluded as AVREL). In graph theory, graph coloring and graph labeling are two research directions of graph theory, and there is little correlation between the two in previous research results. In the process of researching the concept of Adjacent Reducible Edge Coloring proposed by Professor Zhang Zhongfu, we found that there are several graph classes whose coloring number reaches the sum of the number of vertices and edges, so we propose a new concept of Adjacent Reducible Edge Labeling. In the transportation network, the edge weight represents the transportation capacity, and the node transportation capacity is represented by the sum of its associated edges. Two nodes with the same degree of adjacency require the transportation capacity to be as equal as possible, which can be described by the Adjacent Vertex Reducible Edge Coloring model. when the road diversity reaches the extreme value, it can be described by the Adjacent Vertex Reducible Edge Labeling model. In this paper, designing and using Adjacent Vertex Reducible Edge Labeling algorithm (abbreviation: AVREL algorithm). The algorithm recursively looks through the arrangement space of the Adjacent Reducible Edge Label through the underlying label of the edge, lastly sifts through the graph book fulfilling the edge label and results as a label matrix. In the wake of examining the algorithm results, some special graphs such as Petersen-pyramid graphs, Möbius ladder graphs, bicyclic graphs, and some joint graphs in various situations are summed up, the proofs and conjectures are given. [ABSTRACT FROM AUTHOR]

In this paper, a modified and hybrid flower pollination algorithms (MHFPA) is proposed for dealing with the multi-objective optimal power flow (MOOPF) problem with conflictive objectives. The algorithm combines the mutation and crossover process in the differential evolution (DE) algorithm, introduces the sinusoidal nonlinear dynamic switching probability (SNDSP) and the elite strategy of elder generation (ESEG), which can improve the shortcomings of the original pollen algorithm that it is easy to fall into the local optimum and the diversity is insufficient. A screening approach with Pareto-dominant rule (SAPR) is proposed to ensure that the state variable can satisfy the inequality constraints of the power system. A uniformly distributed Pareto optimal set (POS) is obtained by the non-dominant sorting with elite strategy (NSES) based on Rank and Density estimation, and the best trade-off solution (BTS) is determined from the POS obtained by the fuzzy affiliation theory. For practicality, the total fuel cost, active power loss, emissions and voltage deviation are selected as objective functions. Due to the limitations of the actual power system, the valve point effect is also considered. The IEEE30-, 57- and IEEE118-bus test systems are used to verify the performance of the proposed MHFPA. In addition, two performance indicators, Hypervolume (HV) and Spacing (SP), quantitatively evaluate the diversity and uniformity of the POS obtained by MHFPA. The simulation results show that, compared with the classic MOPSO and NSGA-II algorithms, the method proposed in this paper shows a greater competitive advantage in dealing with different scales and non-convex optimization problems. [ABSTRACT FROM AUTHOR]

Multi-objective reactive power optimization (MORPO) is a high-dimensional, nonlinear, multi-constraint problem. To solve this problem, an improved multi-objective honey badger algorithm (MOIHBA) is proposed. To address the shortcomings of the original algorithm, such as easy falling into local optimum and insufficient population diversity, the improved algorithm introduces a sine chaotic mapping strategy to expand the population diversity, a backward learning mechanism to narrow the range of high-quality solution sets, and a cross-learning mechanism to improve the precision of the algorithm optimization process. In addition, in order to obtain the pareto optimal set (POS), a method based on calculating individual rank and crowding distance is proposed to sort the non-inferior solution, and the best compromise solution (BCS) is obtained by using a fuzzy theory strategy. By introducing three objective functions of active power loss, voltage stability index, and voltage deviation, the multi-objective reactive power optimization is established. To investigate the robustness of the introduced improved algorithm and its ability to solve the MORPO problem, this paper uses IEEE30, IEEE57, and IEEE118 as test systems that optimize the dual objective and triple objective simultaneously. In order to study the comprehensive performance of the improved algorithm, the algorithm time complexity, GD index, and HV index are adopted for evaluation. The simulation results and performance index results show that compared with other algorithms, MOIHBA has better BCS and pareto fronts (PFs) with the best uniformity and convergence. Therefore, the MOIHBA algorithm has a greater competitive advantage in solving the MORPO problem. [ABSTRACT FROM AUTHOR]

Trapdoor functions contributed since their announcement in the evolvement cryptography as we know it, especially the lossy mode, by helping reduce the residual leakage for an optimal rate, but to make it more resilient cryptographically: generic constructions were made based on graph isomorphism, or other NP-hard problems defended by the zero-knowledge proof, such were used in Indistinguishability under Chosen-Plaintext Attack (IND-CPA), Computationnal Diffie-Hellman (CDH), or Decisional Diffie-Hellman (DDH). Once schemes like Indistinguishability under Chosen- Ciphertext Attack (IND-CCA) were adopted it became clear it cannot simulate a decryption using Lossy Trapdoor Functions (LTF); the problem with existing trapdoor functions in general is partial information leakage, lack of randomness and multiple messages insecurity. In the light of the following issues came the idea to present through this paper a simple but important fix, in the note of randomness a new Variate of the Engel expansion (VEE) is chosen, providing a pseudo-random bit sequence as an output, the reason being to recover the seed of the algorithm for an attacker, it is considered a hard number theory problem, and surely after the new construction in this paper, another NP-complete problem emerging from tensors the scheme is more secure. As for the strenghtening evidence of how it can be trusted, it seems more robust to supply a proof of its ergodicity as being done in this article, instead of semantic security analysis, to prove the efficiency of the new construction resolving the issues surrounding multi-challenge using a lossy trapdoor function. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) are recognized as an effective method for controlling the power loss, voltage stability, etc.. In this paper, a novel hybrid algorithm of beetle antennae search (BAS) and particle swarm optimization (PSO) is presented for optimal allocation of DGs in radial distribution network. The BAS describes the beetle's individual search by smell, PSO describes the group search of birds by location. The proposed algorithm combines their advantages, proceeds with individual optimization while conducting group optimization. Therefore, the proposed algorithm searches widely, and converges fast. In this paper, a series of improvement measures are proposed to deal with the shortcoming of PSO-BAS which is easy to fall into local optimum. These methods include equal interval initialization, cross mutation, and non-linear learning factor. This paper will show the comparison results of PSO-BAS and IPSO-BAS in the six confessed test functions to prove the necessity of the improved methods. Simultaneously, in order to verify the feasibility and effectiveness of this proposed algorithm in terms of practical application, it is tested on the standard IEEE 33-bus, IEEE 69-bus and IEEE 119-bus systems. The results of active power loss and voltage stability show that proposed algorithm is more effective and more suitable for the power distribution system than other algorithms. At the same time, this article also explores the impact of new energy sources on the annual network loss. Besides, a method for optimizing the annual network loss is proposed. Here, the IPSO-BAS algorithm is used to adjust the size of multiple biomass energy sources within 24 hours to optimize the network loss, and a comparison plan is designed to verify the feasibility of the proposed method. According to the final result, this proposed method can greatly reduce the annual network loss. [ABSTRACT FROM AUTHOR]

This paper proposes an improved novel bionic intelligent Moth-Flame Optimization (MFO) algorithm optimized linear active disturbance rejection control (LADRC) strategy for automatic generation control (AGC) problems. LADRC is a control strategy that combines linear extended state observer (LESO) and traditional proportional-derivative (PD) control, with the advantages of its resistance to disturbance and simplicity. The three-order LADRC controller is designed in load frequency control (LFC) system. The Improved MFO (IMFO) algorithm is proposed by Cauchy mutation strategy and self-adaptive weight to overcome the weaknesses in easily falling into local optima and poor optimization search accuracy, and the IMFO algorithm is employed for tuning the parameters of LADRC. Besides, an objective function takes account of the performance index integral-time-multiplied-AE (ITAE) with dynamic indicators, which is used to improve the effectiveness of LADRC. The proposed control strategy is tested for robustness in a two-area non-reheat thermal system and compared with recent control methods published in the literature. As a result, its superiority is demonstrated for the lowest cost function values of ITAE = 0.0036, ITSE = 1.24E-05, ISE = 6.81E-05 and IAE= 0.0086. To further explore the potential of the LADRC controller based on the IMFO algorithm, it is also extended to a two-area nonlinear system and an unequal three-area reheat system. The performance indicator functions and dynamic performance indicators are analyzed qualitatively in the meantime. [ABSTRACT FROM AUTHOR]

In this paper, an improved marine predators algorithm (IMPA) is proposed to solve the short-term hydrothermal scheduling (STHS) problem. The marine predators algorithm (MPA) owns low diversity of the initial population and is easy to fall into local optima in the optimization process. Facing these challenges, three improvements are presented. Tent map is applied to initialize the population, which makes the population more uniformly distributed. An average fitness preferential strategy is adopted to improve the quality of population, which provides more possibility for MPA to find better solutions. By segmenting the probability factor in fish aggregating devices (FADs) effect on the optimization process, the premature convergence of MPA is improved. Moreover, a selective repair strategy and an economic priority strategy are proposed to handle dynamic water balance of reservoirs and the power balance, respectively. Three hydrothermal test cases are employed to verify the feasibility and effectiveness of the proposed method, and the results show that IMPA can obtain solutions of high quality. Compared with other methods, IMPA can get better results, which reflects its strong competitiveness in tackling the STHS problem. [ABSTRACT FROM AUTHOR]

A graph G(p, q) is said to have an edge-magic total labeling if there exists a bijective function f: V(G) ∪ E(G) → {1, 2, ...,p + q}, such that for any edge uv∊E(G) the condition f(u)+f(v)+f(uv)=k is satisfied, k is a constant. In this paper, a new algorithm, based on the graph generate algorithm, is designed to obtain the edge-magic total labeling of the unicyclic graphs. Some theorems about unicyclic graphs are also deduced from the algorithm's results. It's believed that the algorithm proposed is innovative and can be adopted by other researchers. [ABSTRACT FROM AUTHOR]

The discrete filled function method (DFFM) is a global optimization method for searching for the best solution amongst multiple local optima. This method consists of two phases: in the first phase, an ordinary descent method is used to find a local minimum; in the second phase, an auxiliary function, called a filled function, is introduced that has a maximizer at the current local minimum, so that minimizing the filled function leads to improved points. Once an improved point is found, it can serve as a starting point for the next local search. In this paper, we consider a standard discrete filled function algorithm in the literature and propose a modification to increase its efficiency. Three numerical examples are given to demonstrate the proposed modification's potential in solving large scale discrete optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a node-adaptive algorithm that aims to enhance the effectiveness of the dual reciprocity boundary element method (DRBEM) for numerically solving PDEs. The adaptation algorithm allows the internal nodes to automatically adapt accordingly to a pre-defined criterion during the computing process. The multiquadric radial basis function (MQ-RBF) is used to link the supports from internal nodes to the boundary ones. The proposed adaptation scheme is driven by the local change in velocity (in both x- and ydirections) using a form of normalized error indicator. The node-adaptation manner falls into the h-type of refinement where nodes are automatically added into (or removed from) the computational domain. It is found that the numerical solutions obtained from the proposed adaptation scheme are noticeably improved for all cases under the investigation. This promising aspect certainly provides a numerical tool for better applications of DRBEM toward more complex problems. [ABSTRACT FROM AUTHOR]

Recently, intuitionistic fuzzy sets and numbers are widely interesting in the literature, and many types of Intuitionistic Fuzzy Numbers (IFN's) are studied and applied to different mathematical and real life problems. In this paper, an algorithm to approximate general IFN's by the n-Intuitionistic Polygonal Fuzzy Number (n-IPFN) is introduced. The approximation facilitates the calculations due to its linearity, not to mention its realisticity and flexibility. After that, a new method to rank IFN's is introduced and applied to the n-IPFN. Based on that, convenient arithmetic operations for n-IPFN's that preserve the ranking values are proposed and shown to satisfy the most important properties. As a result, defining a ranking equivalence relation gave a strong algebraic structure that is isomorphic to the real numbers set. Finally, some definitions are proposed for dealing with matrices, functions, equalities and inequalities with n-IPFN's. [ABSTRACT FROM AUTHOR]