Maximum Number Of Iterations Research Articles

Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures

This article proposes a novel reinforcement learning algorithm using an improved Monte Carlo tree search (IMCTS) formulation for the discrete optimum design of truss structures. IMCTS with multiple root nodes includes the update process, best reward, accelerating technique and terminal condition. The update process means that once a final solution is found, it is used as the initial solution for the next search tree. The best reward is used in the backpropagation step. The accelerating technique is introduced by decreasing the width of the search tree and reducing the maximum number of iterations. The agent is trained to minimize the total structural weight under various constraints until the terminal condition is satisfied. The optimal solution is the minimum value of all solutions found by the search trees. Numerical examples show that the agent can find the optimal solution with low computational cost, stably produces an optimal design, and is suitable for multi-objective structural optimization and large-scale structures.

Chaotic Sea-Horse Optimizer for Profit-Based Unit Commitment to Maximize the Profit of GENCOs in Restructured Power System Considering Reserve Allocation

Objectives: The primary objective of this research work is to solve the Profit Based Unit Commitment (PBUC) problem in Power Generation Companies (GENCOs) in the competitive Electricity market. The energy and spinning reserve allocation are considered to maximize the profit of power generation companies. Methods: A new and recently developed optimization technique of Chaotic Sea-Horse Optimizer (CSHO) is planned for an optimal solution PBUC problem. The control parameters of CSHO are the number of Population is 50, the Chaotic map parameter is 4, the dimensions are 10, and the maximum number of iterations is 100 are considered to achieve the global optimal solutions. The competitive study with standard golden approaches of the LR method and other soft computing techniques such as GA, PSO, etc. are considered for performance evaluations of CSHO. The proposed CSHO is mainly based on the natural behavior of Sea-Horse in the ocean. The MATLAB 15.0 platform is utilized for the expected problem. Findings: The CSHO was tested on a standard IEEE-39 bus system (10 generators and 24 hours) with reserve allocation. The CSHO technique improves the total profit of GENCOs with various test loads and reserve demand. The simulation results like Optimal UC schedule, power generation of thermal plants, fuel cost, startup cost, revenue, and profit are displayed. The percentage of total profit of 29.76% is improved when compared with the mathematical approach of the LR method with 102 second time period. Novelty: The CSHO has currently developed an effective algorithm that easily reaches the global optimal solutions. The CSHO mimics the movement, hunting, and breeding behavior of sea-horses in nature. Implementation of chaotic maps helps to improve the convergence speed of the proposed method. Keywords: Deregulation, Profit Based Unit Commitment, Energy and Reserve Allocation, Cost Minimization, Profit of GENCOs, Chaotic Sea-Horse Optimizer

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learning

Iterative ensemble smoothers (IES) are among the popular reservoir data assimilation (RDA) algorithms for reservoir characterization. The actual deployment of an IES algorithm requires implementing certain stopping criteria, normally adopted for runtime control (e.g., by stopping the IES when it reaches the maximum number of iterations) and/or safeguarding the RDA performance (e.g., by preventing the simulated data from overfitting the actual observations). In practice, for various reasons, it is often challenging for existing stopping criteria to simultaneously achieve both purposes. One noticeable issue, as illustrated in this work, is that in many situations, the qualities of the estimated reservoir models may already start to deteriorate before a conventional stopping criterion activates to terminate the iteration process. Following this observation, one practically important question arises: Is it possible to further improve the efficacy of the IES algorithm by designing a different stopping criterion so that the IES can stop earlier, saving computational costs while achieving better RDA performance?As one of the rare attempts in the community, this work aims to investigate the use of a new IES stopping criterion that has the potential to provide an affirmative answer to the above question. In this regard, our main idea is based on the concept of cross validation (CV), routinely adopted in supervised machine learning (SML) problems for early stopping to prevent SML models from overfitting the training data. Despite the noticed similarities between RDA and SML problems, some fundamental differences exist, making it fail to work well if one directly extends a vanilla CV procedure from SML to RDA. To tackle this identified challenge, we design an efficient CV procedure tailored for RDA problems, and inspect the performance of an IES algorithm equipped with this CV procedure (IES-CV) in both synthetic and real field case studies. Our numerical investigation indicates that the IES-CV algorithm achieves promising RDA performance in all case studies, confirming the possibility that with the aid of a proper stopping criterion, an IES algorithm can terminate at an appropriate iteration step with near-optimal RDA performance. Beyond these numerical findings, it is also our hope that the current work may help improve the best practices of applying IES to RDA problems, taking advantage of the effective, CV-based stopping criterion.

A Novel Pelican Bird Optimization Algorithm based Optimal Allocation of Combined DGs and EVCSs for Improving Voltage Stability of RDS

Objectives: The main objective of the proposed investigation is to enhance the voltage stability of Radial Distribution System (RDS). It is achieved by proper allocation between combined Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) in RDS. Here, three objective functions such as Real Power loss, Average Voltage Devotion Index (AVDI) and Voltage Stability Index (VSI) are considered. Methods: The multi-objective optimization problem is implemented using a novel and an effective Pelican Optimization Algorithm (POA) to attain the best compromised solutions. POA is a nature inspired parameter less optimization tool and it is well suited for solution of complex, non-linear voltage stability problem. It determines the most excellent site and size of the EVCSs and DG units. The control parameter of POA considered are population size as 40, the number of variables as 8 and the maximum number of iterations as 100. MATLAB 14.0 platform is utilized for the projected problem. Findings: The bench mark test system of IEEE-33 node system is considered to authenticate the presentation of planned POA methodology. The outcomes such as minimized power loss and AVDI, improved voltage at each bus and VSI are presented. The percentage of loss reduction 39.30% is enhanced, when compared with the base case approach and 5.42 % more improved than the other existing methods like TLBO and HHO approaches. Similarly, the VSI 24.96 % is improved, when compared with the base case method and 1.58 % better than the other obtainable methods. Novelty: The POA is a newly urbanized and well-designed optimization algorithm. It efficiently reaches the global optimal solutions with less time duration. The comparative study with the existing algorithm of TLBO and HHO is considered to prove the performance of Projected POA approach. Keywords: Radial distribution system, Distributed Generators, Electric Vehicles, Charging Stations, Voltage stability, Network loss reduction, Pelican Optimization Algorithm

Optimizing Wireless Sensor Network Localization: Hybrid Natural Inspired with Bat Swarm Algorithm

In wireless sensor networks, the data received from sensor nodes is processed and communicated to the next node or cluster head. Sensed data are meaningful only when location information is embedded with the data. Therefore, the sensor should be able to estimate its location information and embed the location information with the sensed data. This research work proposes three hybrid nature-inspired localization optimization algorithms for location estimation of sensor nodes, namely the Hybrid genetic –Bat Localization Algorithm (GA-BAT LA), Hybrid Bat- Particle Swarm Optimization Localization Algorithm (BAT-PSO LA), and Hybrid genetic -Bat- Particle Swarm Optimization Localization Algorithm (GA-BAT-PSO LA). The localization algorithms are developed, implemented, and compared to ensure the accuracy of the estimation of location information. A fixed number of location-known anchor nodes with a variable location-unknown sensor node are considered. The algorithms are compared for a minimum number of iterations, average number of iterations, standard deviation, maximum number of iterations, time complexity, and accuracy to estimate the location of the sensor nodes.

Reference-free calibration method for asynchronous rotation in robotic CT.

Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators. We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model. We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached. In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality. We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.

RBF Neural Network for Chaotic Motion Control of Collision Vibration System

Aiming at the control problem of chaotic motion of a kind of collision vibration system with gap, a chaotic motion control of collision vibration system based on RBF neural network is proposed. Firstly, the system mechanical model and chaotic motion are introduced, and then a chaotic controller based on RBFNN is designed to control and simulate the chaotic attractor. The model information of the system is not used in the control method. In this paper, the model of the system is used only to generate the input / output data of the system, and it is not used for the design of the controller. The parameters of AHGSA algorithm are set as follows: the population size is 30, the maximum number of iterations is 100,G0, a = 18, and the proportional coefficient P = 0.96. Small disturbances are applied to the controllable parameter ωof the system to suppress the chaotic motion of the system and make the system tend to stable periodic motion. In order to more clearly show the control effect of chaotic motion, the chaotic motion is controlled when the system iterates 400 times. The results show that the chaotic motion can be quickly controlled to periodic 1-1 motion, the phase diagram is a closed curve, and there is a peak in the spectrum diagram. Chaotic motion can be quickly controlled as periodic 2-2 motion, the phase diagram is two closed curves, and two obvious peaks appear in the spectrum diagram. The proposed method can effectively control the chaotic motion of the system, and the expected target can be not only the fixed point of period 1, but also other periodic orbits.

A proximal-gradient method for problems with overlapping group-sparse regularization: support identification complexity

We consider the proximal-gradient method for minimizing the sum of a smooth function and a convex non-smooth overlapping group- ℓ 1 regularizer, which is known to promote sparse solutions with respect to its groups. A feature that distinguishes our work from most in the literature is that the proximal operator for the overlapping group- ℓ 1 function does not admit a closed-form solution, which introduces challenges when proving convergence of the iterates and especially when proving that the iterates correctly identify the group-sparse structure of the optimal solution. To address these challenges, we present an implementable termination condition for the proximal-gradient algorithm, and a specialized primal-dual subproblem solver that is designed to ensure that a group-sparse structure identification property holds. In particular, we give an upper bound on the maximum number of iterations before the correct support (i.e. group-sparse structure) of an optimal solution is identified.

Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

A stochastic data-driven Bayesian optimization approach for intensified ethanol–water separation systems

The optimization of advanced intensified distillation operation systems is crucial for achieving sustainability in the chemical industry. In this paper, we present a stochastic data-driven Bayesian optimization approach for the design and operation of ethanol distillation separation systems. The proposed optimal design methodology does not require a first-principles mathematical model to draw optimal operating conditions. The approach involves an initial sampling of input/output system measurements, followed by the construction of an approximate input/output Gaussian process model. The next best sampling point is then drawn using an acquisition function, and the algorithm converges when the optimization function value does not change or when the maximum number of iterations is reached. We address the optimal parametric design of advanced ethanol–water intensified distillation separation systems, assuming a mixture of the ethanol–water system is available, and almost anhydrous ethanol is required for mobility applications. We also consider the impact of noisy measurements on the optimality operating region. The proposed approach can be extended to the optimization of merged discrete and continuous systems. Bayesian optimization techniques represent black-box optimization strategies that can be employed for the continuous online optimization of intricate separation systems. These techniques aid in mitigating sustainability issues by reducing mass and energy requirements.

Simulation Analysis and Experimental Study on Airfoil Optimization of Low-Velocity Turbine

By combining computational fluid dynamics (CFD) and surrogate model method (SMM), the relationship between turbine performance and airfoil shape and flow characteristics at low flow rate is revealed. In this paper, the flow velocity tidal energy airfoil model is designed based on the Kriging model, and the original airfoil with a relative thickness of 12% and a relative curvature of 2.5% is obtained. The parameter optimization is carried out by setting the 4th CST equations through the surrogate model; the maximum lift-drag ratio is the optimization goal, the optimization design variable is 10, the maximum number of iterations is 100, and the maximum number of sub-optimization iterations is 200. The results show that the hydrodynamic performance of the airfoil with thinner thickness and more curvature is better, the maximum thickness part is shifted forward by 4.58%, and the lift-drag ratio is improved by 4.03%. The flow field and the efficiency are more stable, which provides an engineering reference for the optimal design of hydraulic turbine airfoils under low flow velocity. It supplements the research on the performance of turbine blades in low velocity.

Optimizing generalized linear fractional program using the image space branch-reduction-bound scheme

This paper addresses solving the generalized linear fractional program problem. For this purpose, we first convert the original problem into an equivalent problem by introducing some new variables and equivalent variation. Next, by using the two-part approximation and bilinear relaxation, we construct the linear relaxation of the objective function and the constraint function, respectively. Based on the characteristics of the objective functions of the equivalence problem and the relaxation problem and the branch-and-bound structure of the algorithm, we construct some image space region reduction techniques to enhance the convergence speed of the algorithm. By combining the linear relaxation program problem and the image space region reduction techniques, we construct an image space branch-reduction-bound algorithm, prove its global convergence, and estimate its maximum number of iterations in the worst case by analysing the complexity. Finally, numerical results indicate high computational efficiency of the algorithm.

A study on rocker arm defect inspection based on vision system

The rocker arm plays a crucial role in an internal combustion engine’s valve train system. Issues often arise when there are errors in the production of the rocker arm, such as the following: (1) misalignment of the rocker arm spindle hole and thread hole; and (2) bevel errors in the rocker arm spindle hole. This paper focuses on the development of a rocker arm chamfering and coaxial defect inspection system, which utilizes the RANSAC algorithm. The algorithm consists of five steps: (i) representing a 2D point cloud as a matrix with X and Y coordinates; (ii) detecting the number of circles present in the input point cloud; (iii) setting a tolerance threshold for the distance between the selected circle and other points, crucial for achieving high accuracy; (iv) specifying the maximum number of iterations to find the best model from the input points; (v) defining the maximum number of points that can belong to the same circle, considering point density and the anticipated circle’s surface. The real-time inspection system is designed with a camera mounted on top of the box, and two LED arrays positioned around the box at suitable distances. Test results using a collected data set demonstrate the effectiveness and accuracy of the proposed method in detecting each type of error. In the experiments, the proposed method successfully detected all coaxial defect samples and achieved a detection rate of 95.1% for chamfer defect samples.

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks.

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

Research on Security Governance of Data Elements in the Context of Digitization

Abstract This paper presents a multi-subject collaborative data security governance system architecture that caters to various aspects of data security governance. It also introduces an attribute-based encryption technology that utilizes multi-subject data, specifically a multi-subject access control policy fusion algorithm scheme from a technical perspective. The neural network is chosen to build the prediction model. The particle speed is changed using the linear decreasing weight strategy. The improved particle swarm algorithm is used to set the initial weight coefficients and bias variables of the BP neural network. This creates the shared information security risk prediction model. In order to derive the maximum number of iterations of the data security risk prediction model, analyze the encryption and decryption overhead of the multi-owner access control strategy. Additionally, analyze the fluctuation amplitude of MAE and MRE that meets the error threshold range, selecting the virus attack industry for analysis. The probability of occurrence of information data risk points is assessed by analyzing the frequency of coding structure for data technology risk, data management risk, and data legal risk, which is combined with the interview data. The statistics show that the data management risk category, with a standard deviation of 32.64, is more volatile than the two core categories of data technology risk and data legal risk. The lack of stability exposes the data security of e-commerce platforms to risky turbulence.

Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9

A localized subdomain smoothing MMALE particle method for efficient modeling FSI problems

Fluid–structure-interaction (FSI) phenomena with multi-phase flow dynamics and structural damage commonly exist in engineering practice, which however bring great challenges to nowadays numerical FSI algorithms. A novel localized subdomain smoothing MMALE particle method (ls-ALEPM) is proposed in this paper for efficient and accurate simulations of large scale FSI problems. The MMALE method and the MPM are strongly coupled by immersing the MPM particles into the MMALE grid. In order to avoid the spurious strain induced by the mixed FSI velocity field, a decoupled stress updating scheme is proposed to update the stress of solid particles by introducing a virtual velocity field in the vicinity of FSI interface. And specifically, the highly accurate polyhedron intersection based method is employed for its remapping phase, which however is time-consuming. Thus, the localized subdomain smoothing method (LSSM) is put forward to accelerate the remapping phase which only involves the distorted regions of computational grid. The LSSM is composed of a distorted subdomain determination step and a combinated mesh smoothing step. Each iteration of the combinated mesh smoothing step consists of the modified GETMe and the weighted average method, and the transfinite interpolation method is adopted if the quality criteria is still not satisfied after prescribed maximum number of iterations. The validity and efficiency of ls-ALEPM is verified by several benchmark numerical examples and practical engineering simulations.

Research On the No-Wait Flow Shop Scheduling Problem Based on Simulated Annealing Algorithm.

This paper focuses on the No-Wait Flow Shop Scheduling Problem and conducts research based on an improved Simulated Annealing Algorithm. The No-Wait Flow Shop Scheduling Problem is of great significance in practical production, but its solution complexity is high, requiring the search for appropriate optimization methods. In this study, a solution based on an improved Simulated Annealing Algorithm is proposed. Simulated Annealing Algorithm, as a global optimization method, introduces random perturbations to the current solution and accepts or rejects the new solution based on Metropolis criterion to escape local minima and search for better solutions. In this paper, the termination condition of the improved Simulated Annealing Algorithm is enhanced. Specifically, if no better solution is obtained after a certain number of consecutive temperature calculations or when the maximum number of outer loop iterations is reached, the algorithm terminates and outputs the result. This improvement aims to increase efficiency and reduce unnecessary computations.By comparing the results with other heuristic algorithms, it is found that the solution based on the improved Simulated Annealing Algorithm can effectively provide better scheduling solutions for the No-Wait Flow Shop Scheduling Problem, thereby improving production efficiency, reducing costs, and optimizing resource utilization.