Chaos Genetic Algorithm Research Articles

Group‐based chaos genetic algorithm and non‐linear ensemble of neural networks for short‐term load forecasting

This study presents a non-linear ensemble of partially connected neural networks for short-term load forecasting. Partially connected neural networks are chosen as individual predictors due to their good generalisation capability. A group-based chaos genetic algorithm is developed to generate diverse and effective neural networks. A novel pruning method is employed to develop partially connected neural networks. To further enhance prediction accuracy, an artificial neural network-based non-linear ensemble of partially connected neural network predictors is developed. The proposed non-linear ensemble neural network is evaluated on a PJM market dataset and an ISO New England dataset with promising results of 1.76 and 1.29% error, respectively, demonstrating its capability as a promising predictor.

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs) and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 andk-nearest neighbor (KNN). The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Diagnosis of Ship Generator Optimized Neural Network Based on Multi-population Chaos Genetic Algorithm

In view of fault diagnosis of the ship generator , the paper proposes improved fault diagnosis method of ship generator ,which is Optimized Neural Network based on Multipopulation Chaos Genetic Algorithm. The results prove that the method effectively solves low precision,slow constringency and local minimum of neural network and improves global search ability, optimizes the rate and precision of fault diagnosis. The method has a certain application prospect for the ship power system generator fault diagnosis.

A Comparative Study of Data Mining Algorithms for Image Classification

Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It’s also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques. Image classification to produce the accurate prediction results in their target class for each case in the data. It is a very predominant and challenging task in various application domains, including video surveillance, biometry, biomedical imaging, industrial visual inspection, vehicle navigation, remote sensing and robot navigation. The aim of this study compares the some predominant data mining algorithms in image classification. For this review SVM, AdaBoost, CART, KNN, Artificial Neural Network, K-Means, Chaos Genetic Algorithm, EM Algorithm, C4.5 algorithms are taken.

Zoning Modulus Inversion Method for Concrete Dams Based on Chaos Genetic Optimization Algorithm

For dams and rock foundations of ages, the actual mechanical parameters sometimes differed from the design and the experimental values. Therefore, it is necessary to carry out the inversion analysis on main physical and mechanical parameters of dams and rock foundations. However, only the integrated deformation modulus can be inversed by utilizing the conventional inversion method, and it does not meet the actual situation. Therefore, a new method is developed in this paper to inverse the actual initial zoning deformation modulus and to determine the inversion objective function for the actual zoning deformation modulus, based on the dam displacement measured data and finite element calculation results. Furthermore, based on the chaos genetic optimization algorithm, the inversion method for zoning deformation modulus of dam, dam foundation and, reservoir basin is proposed. Combined with the project case, the feasibility and validity of the proposed method are verified.

분석적 계층 과정과 카오스 유전자 알고리즘을 이용한 전열교환 환기 시스템의 최적 유량과 설치위치 결정

This paper addresses an optimal design problem of ERV (Energy Recovery Ventilator) system that seeks for proper sizing and location of supply diffusers/exhaust registers. A typical of a residential unit consisting of three bedrooms, a living room, and a kitchen was selected. The elements in a cost function includes energy use, thermal comfort, and <TEX>$CO_2$</TEX> concentration, leading to multi-criteria optimization problem. In this study, the aforementioned optimization problem was solved using Analytic Hierarchy Process (AHP), Chaos Genetic Algorithm (CGA) and EnergyPlus 8.0. AHP was employed to calculate relative weighting factors between the aforementioned different three performance aspects. Since the given model-based optimization problem is discontinuous and nonlinear, the authors integrated CGA to EnergyPlus. It has been elaborated in the paper that the CGA approach is superior to a traditional Genetic Algorithm (GA) in terms of simulation time and searching for global optima.

Optimization of Operating Mode in Copper Flash Smelting Process

A batch of operating parameters which need to be resolved on line are represented by operating modes.Operating mode optimization for copper flash smelting process based on fuzzy neural networks is presented. First of all, the optimal samples set is screened from the historical samples set. Then mode decomposition based on fuzzy neural networks is used, and chaos genetic algorithm is used to rake the optimal operating sub-pattern.This way is used to copper flash smelting process.The simulation result shows that this way can guide production.

Intelligent Optimization of Optimal Operating Mode in the Production Process of Copper Converter

In order to enhance the standard of copper converter operation,operating modes are employed to describe a group of operating parameters which need to be decided on line, and an intelligent optimization way based on neural network and improved chaos genetic algorithm for operating modes is presented. At first, the optimal samples is sieved from the historical sample set. Secondly, the functional relationship with optimization objective and technological parameters is drilled by a neural network model. At last, chaos genetic algorithm with chaotic variables is adopted to seek the optimal operating mode. This method is applied to real-time optimization of copper converter operating parameters.The running results show that the yield of converter raises by 5.9%, and the quantity of the disposed cool materials increases by 7.7%.

An Improved Anycast QoS Routing Algorithm Based on Chaos Genetic Algorithm

To overcome the problem of tending to converge to part in anycast QoS routing based on Genetic Algorithm, a new improved anycast QoS routing algorithm based on Genetic Algorithm is proposed which adopt the improved chaotic disturbance operator. The simulated result shows that this algorithm has a good ability of global search, and it can find a set of low-cost paths with bandwidth constraints and delay requirements efficiently and quickly. By comparing the simulation data, the success rate of finding the optimal solutions is improved, and the larger network scale, its advantage is more obvious.

An Improved Chaos Genetic Algorithm for T-Shaped MIMO Radar Antenna Array Optimization

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

Slope Stability Forecast of LS-SVM Based on Chaos Genetic Algorithm Optimization

As the relationship between geotechnical slope stability and influencing factors is complex and nonlinear, the least squares support vector machines (LS-SVM) is used to establish the nonlinear relation between slope stability and influencing factors. And in consideration of that parameters selection of LS-SVM exerts a major influence on modeling results, the parameters of LS-SVM are optimized by chaos genetic algorithm (CGA). Thus the CGA LSSVM is proposed for forecasting slope stability. Through the comparison between the method and the simple genetic algorithm (SGA) parameters optimization. The result shows that parameters optimization of the CGA has better faster convergence speed, higher prediction precision. And the model is applied to predict the safety factor of the actual slope engineering and the results are well consistent with the actual situation. It is shown that the model is reasonable and feasible.

Research on Single Distribution Center Logistics Transportation Scheduling Problem Based on Static Road Capacity Constraints

Related Vehicle Routing Problem is another form of Vehicle Routing Problem. RVRP also belongs to NP-Hard with a wide range of application areas and major economic value. The research based on single distribution center RVRP with road capacity static constraint, to build a model of single distribution RVRP and propose a kind of chaos genetic algorithm to solve this problem, with experiments verify the feasibility and effectiveness of the algorithm.

A Rapid Mixed Batch Blending Method Based on Chaos Genetic Algorithm

A Rapid Mixed Batch Blending Method Based on Chaos Genetic Algorithm

Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

AbstractAn effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is uti...

The Optimization Design of Six-bar Linkage Mechanism

At present, multi-bar linkage mechanism is one of the most important directions of mechanical presses’ development. Utilizing the typical multi-linkage drawing mechanism of the plunger slide and compounding its parameters scientifically are a fairly effective way to realize the drawing technology demand. This paper established kinematics mode of the six-bar drawing mechanism by bar-group method, and produced simulated system by Visual Basic, which simulated the actual motion of the mechanism. With the objective function-velocity fluctuation in drawing and drawing depth, using chaos genetic algorithm method, carried out optimization design of the mechanism, and acquired several groups’ data. The optimization results showed that their performance in kinematics was improved greatly and had exceeded the original mechanism. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2863

Modelling and control of flexible joint robot based on Takagi–Sugeno fuzzy approach and its stability analysis via sum of squares

This article proposes a Takagi–Sugeno (T-S) fuzzy model of single-link rotary flexible joint robot. The proposed control method is based on parallel distributed control. The parameters of T-S controller are improved by distributed population genetic algorithm (GA) with chaos GA. Using Hermite–Biehler theorem in distributed population, GA is made to have a fast convergence. Dividing search space into several sub-spaces causes a better response, and chaos disturbance helps the whole algorithm to reach a best answer. The stability of the controller is analysed via the sum of squares programming, and finally, it is implemented on the plant.

Urban Logistics Demand Forecasting Based on Regression Support Vector Machine Optimized by Chaos Genetic Algorithm

Urban Logistics Demand Forecasting Based on Regression Support Vector Machine Optimized by Chaos Genetic Algorithm

Worst-case tolerance analysis on array antenna based on chaos-genetic algorithm

Worst-case tolerance analysis on array antenna based on chaos-genetic algorithm

A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation

By combing the properties of chaos optimization method and genetic algorithm, an adaptive mutative scale chaos genetic algorithm (AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [−1, 1]. Some measures in the optimization algorithm, such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline, were taken to ensure its speediness and veracity in seeking the optimization process. The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision. Furthermore, the average truncated generations, the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally. It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.

Soft sensing and optimization of pesticide waste incinerator

Three soft sensor models [radial basis function (RBF), support vector machine (SVM), and independent component analysis–support vector machine (ICA–SVM)] are developed to infer the Chemical Oxygen Demand (COD) of the quench water produced from the pesticide waste incinerator, respectively. An optimization model of COD is further proposed based on the aforementioned soft sensor models. Furthermore, chaos genetic algorithm is introduced to solve the optimization model. The procedure is demonstrated and discussed with the practical industrial cases, where the mean relative error of the proposed ICA–SVM model for COD prediction is 0.16%, and the mean COD of the practical factory is decreased from original 1140 mg/L to current 393 mg/L, decreasing 65.53%, with the proposed optimal soft sensing approach. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.