Reactive Power Planning Based on Fuzzy Clustering, Gray Code, and Simulated Annealing

Voltage deviation and stability constrained VAr planning or reactive power planning (RPP) is an important challenging issue in power systems. This paper presents a new hybrid technique for modeling and solving RPP problem taking into account the static voltage stability constraint. First, the uncertain fuzzy clustering theory is employed to select new candidate VAr source locations. Then, modified gray code is applied and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Based on the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability and new VAr capacity is derived and applied as static voltage stability constraint in RPP problem. Last, the RPP optimization problem is solved by a self adaptive fuzzy chaotic interactive honey bee mating optimization (FCIHBMO) technique taking advantage of the modified gray code. In the FCIHBMO algorithm, a modified definition of the updating factors on generation solution is proposed. In the case study, uncertain fuzzy clustering mechanism, the modified gray code, and the modified HBMO are applied to the IEEE 118-bus and IEEE 300-bus systems. Test results conclude that the proposed hybrid technique is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the seri al SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved.

This paper addressed the modeling and optimization of loading path in T‐shape hydroforming of tubes using Simulated Annealing (SA) algorithm. Analysis of variance shows that some of pre‐selected parameters in loading paths have not significant effect on the deformed tube. Hence, some of optimized parameters found initially, are replaced with their own fixed optimum values in order to seek for the other parameters in more detail by the Simulated Annealing (SA) algorithm. According to the intensity of effectiveness on the deformation, six more important parameters are chosen and their minimum and maximum limitation values are determined. In this case, sixty four different tests for different loading paths are designed by Design of Experiment (DOE) and full factorial method. By using mathematical modeling all required loading parameters are obtained. Proposed models of formability embedded into Simulated Annealing algorithm and optimum value for loading parameters and optimal load paths are found. The obtained results show that more accurate loading path may be found for T‐shape of tube hydroforming.

A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem

This paper presents an image segmentation method based on fuzzy clustering and level set methods. The MRI image is firstly segmented with the fuzzy clustering method, and then the resulting fuzzy memberships are used to generate a new constraint function which guides the level-set curves evolution. Thanks to the new constraint term, the presented method is able to adaptively determine the directions of the curves evolution without any manual intervention. The new method does not depend excessively on the gradient information as the Geodesic Active Contour method does; therefore it is less sensitive to the noise. The region information provided by the first step of the algorithm allows to improve the segmentation accuracy of the brain tissues. The experimental quantitative and qualitative analyses indicate the capability of the proposed method to segment the brain tissues with high accuracy comparing with the fuzzy clustering method.

This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state‐of‐the‐art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo‐inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

Group technology’s basic logic is grouping and producing products of the same type together. An important reason behind Group Technology becoming such an important topic is that nowadays companies have quite an extensive range and workshop type production has increased. Both fuzzy clustering and rank order clustering methods use for grouping parts and machines based on a part-machine matrix created from the production flow technique in order to increase productivity and reduce cost and workmanship required. In this study, Group Technology techniques such as the rank order clustering and fuzzy clustering methods were applied in order to increase the efficiency of the production line, reduce transportation between machines, and form a machine-parts groups in the wood cutting department of a furniture company producing modular furniture in Istanbul. The TOPSIS method was used to determine which products to take into account. According to results of the study, it is shown that fuzzy clustering method has overperformed rank order clustering method based on the evaluation criteria which are group productivity with 21,36%, group efficiency with 43,21% and grouping measure with 82,33%.

In order to improve the capability of enterprise economic risk assessment and prediction, this paper combines panel data analysis method to construct enterprise economic risk prediction model, proposes enterprise economic risk prediction model based on big data fusion, establishes fusion clustering model of enterprise economic risk assessment panel data, and uses fuzzy correlation fusion clustering method to make statistical analysis of enterprise economic risk assessment panel data. Quantitative regression analysis and fuzzy clustering methods are used for panel data mining of enterprise economic risk assessment, adaptive block feature matching in the risk prediction process is carried out according to the mining results of panel data bureau, interference suppression in the enterprise economic risk prediction process is carried out by using correlation feature matching method, large data fusion in the enterprise economic risk prediction process is carried out by combining fuzzy information clustering and statistical analysis methods, and optimization of enterprise economic risk prediction is realized according to the rule distribution relation of recursive entropy. The simulation results show that the method has high accuracy in predicting the economic risks of enterprises, improves the data mining and information fusion capabilities of enterprises' economic risks, and improves the anti-risk capability of enterprises.

In this paper, simulated annealing (SA) algorithm is employed to solve the flowshop scheduling problem with the objective of minimising the completion time variance (CTV) of jobs. Four variants of the two-phase SA algorithm (SA-I to SA-IV) are proposed to solve flowshop scheduling problem with the objective of minimising the CTV of jobs without considering the right shifting of completion time of jobs on the last machine. The proposed SA algorithms have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed SA variants are compared with the best CTV of jobs reported in the literature, and the proposed SA-III is found to perform well in minimising the chosen performance measure (CTV) particularly in medium and large size problems.

A modification of the simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulation is proposed. In this modification, the Metropolis criterion depends on whether the objective function values indicate a statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, the proposed method uses a constant temperature. It is shown that the configuration that has been visited most often in the first k iterations converges almost surely to a global optimizer. Computational results and comparisons with other SA algorithms are presented to demonstrate the performance of the proposed SA algorithm.

With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

In practical application scenarios such as logistics distribution and UAV cruise, the spherical multi-point path optimization problem has significant research value and practical significance. This research focuses on solving the spherical multi-point path optimization problem. The study first establishes a spherical distance calculation model, deriving the distance formula between any two points using spherical geometric principles. Taking eight specific points as research objects, the problem is transformed into a graph theory problem, and a single-objective linear programming model is constructed. The study employs Simulated Annealing Algorithm and Ant Colony Algorithm for comparative analysis. Through parameter optimization, the Simulated Annealing Algorithm achieved a shortest path length of 7.0381 kilometers. In comparison, the Ant Colony Algorithm obtained an optimal path (44-61-158-83-147-100-31-115) with a length of 4.0635 kilometers and a total time of 390.2 minutes, showing a 2.2% improvement in efficiency compared to the Simulated Annealing Algorithm. The results demonstrate that the Ant Colony Algorithm exhibits better performance and stability in this spherical multi-point path optimization problem.

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

As demonstrated in Chap. 3, different evolutionary algorithms, including genetic algorithm (GA), simulated annealing (SA) algorithm, hybrid GASA algorithm, particle swarm optimization (PSO) algorithm, continuous ant colony optimization (CACO) algorithm, artificial bee colony (ABC) algorithm, and immune algorithm (IA), are employed to determine suitable for parameter combination of an SVR-based electric load forecasting model. These forecasting results indicate that almost all SVR-based models with different evolutionary algorithms are superior to other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models); however, these algorithms almost lack knowledge memory or storage mechanisms which would be neither time-consuming nor inefficient in searching the suitable parameters, that is, premature convergence (being trapped in local optimum). For example, for SVRGA model, in GA processing, new individuals are generated by the following operators, selection, crossover, and mutation. For all types of objective functions, the generation begins with a binary coding for the parameter set. Based on this special binary coding process, GA is able to solve some specified problems which are not easily to be solved by traditional algorithms. GA can empirically provide a few best fitted offsprings from the whole population; however, after some generations, due to low diversity of the population, it might lead to a premature convergence [1]. For SVRSA model, SA algorithm is a generic probabilistic search technique that simulates the material physical process of heating and controlled cooling. Each step of SA attempts to replace the current state by a random move. The new state may then be accepted with a probability that depends both on the difference between the corresponding function values and also on a global parameter, temperature. Thus, SA has some institution to reach more ideal solutions. However, SA algorithm requires the subtle and skillful adjustment in the annealing schedule; therefore, the settings of the size of the temperature steps during annealing, the temperature range, the number of restarts, and the redirection of the search will be taken into account carefully [1]; in addition, its Monte Carlo scheme and lack of knowledge memory mechanism will also lead to time-consuming and inefficient searching in annealing process.