Hybrid Taguchi-genetic Algorithm Research Articles

Spindle Thermal Error Prediction Based on Back Propagation Neural Network and Hybrid Taguchi Genetic Algorithm for a Computer Numerical Control Machine Tool

Spindle Thermal Error Prediction Based on Back Propagation Neural Network and Hybrid Taguchi Genetic Algorithm for a Computer Numerical Control Machine Tool

Application of Chaotic Encryption and Decryption in Wireless Transmission From Sensory Toolholders on Machine Tools

In recent years, several industries have increased their demand for processing precision, automatic detection, and visualization interfaces. Therefore, to keep pace with the Fourth Industrial Revolution, machine tool operators install a large number of sensors on machine tools to obtain more precise physical quantities during processing, and use a variety of sensors to obtain measurements in various situations. However, these additional sensors on machine tools result in complicated wire layouts, which exhibit negative effects on processing. Such problems lead to the birth of wireless data transmission, which is expected to become the new standard in the future. At present, there are more and more interactive integration between relevant embedded system and the machine tool, which allows information communication between each other in the wireless domain. However, generally, the majority of machine tool operators focus on optimizing the sensing conditions during processing, but they disregard the importance of information security. In the current era of the Internet of Things, information security is regarded as a crucial factor. For the wireless communication between the Internet of Things (IoT) equipment for each machine in the machining field, the transmitted data is almost exposed within public space directly due to the loss in constraint and protection of physical wiring. Therefore, such process can easily be intercepted by other devices that capture information on relevant status of the machine, or command messages received by the controller, where intentional individual may possibly control the operating mechanism and progress of entire plant. This leads to theft of relevant secrets in manufacturing technology for the subject company, or intentional shutdown, machine damage and vicious blackmailing attacks. Therefore, introducing the mechanism of safety protection during wireless signal transmission is an inevitable technology to maintain the company interests, and also the necessary study lacking at present. Within the encrypted algorithm under such category, the chaotic system has become the popular option of study based on its characteristic of difficulty in decryption. Therefore, in this study, a signal transmission encryption and decryption system to be used by sensory toolholders during processing was designed. The unpredictability of the chaotic system was utilized, and chaotic synchronization control was used to calculate the encryption and decryption of the key, rendering the key difficult to compromise. Two control methods of the synchronization process were then compared: sliding mode control and proportional-integral-derivative (PID) control. The parameters of the PID controller were calculated using optimization algorithms such as the genetic algorithm, hybrid Taguchi-genetic algorithm, and particle swarm optimization. The experimental results revealed that, compared with the PID control, the sliding mode control featured advantages such as fewer iterations required for synchronization, fewer errors after synchronization, and higher robustness and performance recovery. Thus, the sliding mode control synchronization system was able to generate more complex keys and had superior information security and system stability.

Stable and quadratic-optimal parallel-distributed-compensation controller design for time-varying Takagi–Sugeno fuzzy model System: A complementary computational approach

A complementary computational approach is proposed for the time-varying Takagi–Sugeno fuzzy model system (TVTSFMS). The proposed approach integrates orthogonal-functional approach (OFA), hybrid Taguchi genetic algorithm (HTGA), and a stabilizability condition (SC) for use in designing stable and quadratic-optimal parallel-distributed-compensation (SQOPDC) controllers for optimal control problems. First, the SC was set according to linear matrix inequalities (LMIs). Next, OFA was used to derive an algorithm that only required algebraic computation to solve the TVTSFMS. Finally, The HTGA could be used to search the SQOPDC controller for the TVTSFMS. The SQOPDC controller obtained by the proposed complementary computational approach was evaluated in a case study of a vibratory pendulum design; the successful design verified the usability of the proposed hybrid intelligent computing method.

Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation

BackgroundAtrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model.ResultsThis paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911.ConclusionIn feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.

Optimisation of friction stir welding parameters using combined Taguchi L9 and genetic algorithm

'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

Optimisation of friction stir welding parameters using combined Taguchi L9 and genetic algorithm

'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

Optimal parallel-distributed-compensation controller design for a class of time-varying Takagi–Sugeno fuzzy model–based time-delay systems by using the orthogonal function approach–assisted genetic algorithm

This study proposes a method of designing quadratic optimal fuzzy parallel-distributed-compensation controllers for a class of time-varying Takagi–Sugeno fuzzy model–based time-delay control systems used to solve the finite-horizon optimal control problem. The proposed method fuses the orthogonal function approach and the improved hybrid Taguchi-genetic algorithm. The Taguchi-genetic algorithm only requires algebraic computation to perform the algorithm used to solve time-varying Takagi–Sugeno fuzzy model–based time-delay feedback dynamic equations. The fuzzy parallel-distributed-compensation controller design problem is simplified by using the Taguchi-genetic algorithm to transform the static parameter optimization problem into an algebraic equation. The static optimization problem can then be solved easily by using the improved hybrid Taguchi-genetic algorithm to find the quadratic optimal parallel-distributed-compensation controllers of the time-varying Takagi–Sugeno fuzzy model–based time-delay control systems. The applicability of the proposed integrative method is demonstrated in a real-world design problem.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.

BackgroundBreast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.ObjectiveThis study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.MethodsA hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.ResultsThe proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.ConclusionsWe systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm

In this paper, the multi-objective Hybrid Taguchi-Genetic Algorithm is used to search for the best processing parameters with specified processing accuracy. The experimental cutting parameters used for the L9 orthogonal table process are cutting depth, cutting velocity and feed rate. The surface roughness of the machined workpiece surface was measured according to the standard of centerline average roughness. The Material Removal Rate will be calculated by measuring the diameter of the processed workpiece from the formula to give the Material Removal Rate. A linear regression model is constructed from the processed quality and the processing parameters of the orthogonal table, and the reliability of the model is confirmed by analysis of variance. A Hybrid Taguchi-Genetic Algorithm was used to calculate the optimal cutting parameters for multi-objective processing. The results of the experiments indicate that Hybrid Taguchi-Genetic Algorithm gave better convergence and robustness than the conventional Genetic Algorithm using the same number of iterations. This process produces multiple combinations of optimal cutting parameters for material removal rate and surface roughness. As the enhancement of material removal rate improved efficiency on the production line, the optimal cutting parameters were based on the tolerance range of Ra $1.6\mu \text{m}$ to $3.2\mu \text{m}$ according to the international standard of surface roughness. After actual processing with the selected optimum cutting parameters, the quality of processing is even better than the experimental design of the L9 Orthogonal table.

Hybrid Taguchi Genetic Algorithm-Based AGC Controller for Multisource Interconnected Power System

—This article presents hybrid Taguchi-genetic algorithm (HTGA) tuned automatic generation controller for two areas interconnected multisource power system model. The control areas consist of thermal, hydro, and doubly fed induction generator-based wind power plants. The optimal tuning of AGC controller using genetic algorithm is less robust due to larger standard deviation of the fitness values. Therefore, Taguchi method which is based on modified statistical approach and has systematic reasoning ability is used to enhance the performance of genetic algorithm. The simulation study is carried out with the optimum gains of AGC controller obtained from HTGA, conventional GA and conventional techniques. The dynamic stability of the power system model is examined to analyze the performance of the HTGA technique. From investigations it is quite clear that the optimum gains of AGC system obtained from HTGA technique has better response in the dynamic stability of the interconnected multisource power system as compared to genetic algorithm and conventional technique.

Genetic algorithm with Gaussian function for optimal P-wave morphology in electrocardiography for atrial fibrillation patients

Atrial fibrillation (AF), which is a rapid-irregular heartbeat and shows abnormal heart rhythm of the supraventricular tachycardia class, has proved to increase the risks of heart failure, dementia, and stroke. To detection AF, P-wave morphology in electrocardiography (ECG) is suggested to be a strong indicator. To make computerized detection possible, most approaches decompose the ensemble of signals into a finite set of features and establish the relation between symptoms and values of features. Therefore, the disease can be asserted solely by the values of the decomposed features. For early diagnosis of AF, this study develops a hybrid Taguchi-genetic algorithm (HTGA) that facilitates Gaussian decomposition in ECG signals, because P-wave morphology can be well approximated by a family of Gaussian functions. The HTGA optimizes features with minimized performance index of the normalized root mean square error. With accurate decomposition in characterizing parameter values of P-wave morphology, the performance of disease classification improves by using appropriate feature set. Our experiments indicate that the proposed HTGA with Gaussian function obtains a better fit to the actual P-wave compared to the conventional nonlinear least-squares approaches.

Robust-stable quadratic-optimal fuzzy-PDC controllers for systems with parametric uncertainties: A PSO based approach

The present paper shows how robust stable quadratic-optimal fuzzy controllers can be designed using particle swarm optimization (PSO) algorithm, a popular metaheuristic optimization technique. This controller is designed following the Parallel distributed compensation (PDC) technique, employed for Takagi–Sugeno (TS) fuzzy model based control systems, and is termed as a mixed H2/LMI controller. These controllers are designed to achieve two objectives simultaneously i.e. robust stabilization of the uncertain system with parametric uncertainties in hand and also to achieve desired transient response. The robust stabilization is guaranteed when a set of linear matrix inequalities (LMIs) formulated get satisfied. The controller simultaneously achieves the desired transient performance by minimizing a quadratic finite-horizon integral performance criterion for the nominal dynamical system. The proposed PSO based mixed H2/LMI controller has been employed for a benchmark dynamical system and it has been demonstrated to achieve better performance than a recently proposed hybrid Taguchi genetic algorithm (HTGA) based approach, implemented for the same problem.

Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm

Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

Intelligent Hybrid Taguchi-Genetic Algorithm for Multi-Criteria Optimization of Shaft Alignment in Marine Vessels

An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust flange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the fitness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A finite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are significantly better than those obtained by the conventional local search methods with some trial-and-error procedures.

Solving the Flexible Job Shop Scheduling Problem With Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm

Enterprises exist in a competitive manufacturing environment. To reduce production costs and effectively use production capacity to improve competitiveness, a hybrid production system is necessary. The flexible job shop (FJS) is a hybrid production system, and the FJS problem (FJSP) has drawn considerable attention in the past few decades. This paper examined the FJSP and, like previous studies, aimed to minimize the total order completion time (makespan). We developed a novel method that involves encoding feasible solutions in the genes of the initial chromosomes of a genetic algorithm (GA) and embedding the Taguchi method behind mating to increase the effectiveness of the GA. Two numerical experiments were conducted for evaluating the performance of the proposed algorithm relative to that of the Brandimarte MK1–MK10 benchmarks. The first experiment involved comparing the proposed algorithm and the traditional GA. The second experiment entailed comparing the proposed algorithm with those presented in previous studies. The results demonstrate that the proposed algorithm is superior to those reported in previous studies (except for that of Zhang et al. : the results in experiment MK7 were superior to those of Zhang, the results in experiments MK6 and MK10 were slightly inferior to those of Zhang, and the results were equivalent in other experiments) and effectively overcomes the encoding problem that occurs when a GA is used to solve the FJSP.

IIR Band Pass and Band Stop Filter Design employing Teaching-Learning based Optimization Technique

In this paper newly developed teaching-learning based optimization (TLBO) algorithm is applied for designing band pass (BP) and band stop (BS) digital IIR filters. TLBO is heuristic algorithm based on the social phenomenon of teaching-learning process. The effectiveness of purposed algorithm is validated by designing the BP and BS filters by approximating the magnitude response with Lp-norm error criterion, minimizing pass band and stop band ripples along with guaranteed stability. The results obtained employing TLBO are compared to those obtained by the well known evolutionary algorithms such as hierarchical genetic algorithm, hybrid taguchi genetic algorithm and immune algorithm. The results reveal that the purposed TLBO algorithm gives better optimal filter in terms of magnitude response and ripples in pass band and stop band.

Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals

Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood. A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi-genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach. The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability. In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the mapping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice.

Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm

In this paper Takagi–Sugeno (T–S) fuzzy approach is used for control of a flexible joint robot (FJR). The control method in this paper is based on parallel distributed compensation approach (PDC). In this method, a state feedback controller is designed using a fuzzy model that consists of a set of linear fuzzy local models. These fuzzy local models are combined to get an overall fuzzy inference mechanism. In order to improve the efficiency of the system and to set the parameters of the controller the Hybrid-Taguchi genetic algorithm (HTGA) is used. The use of orthogonal arrays (OA) speeds up the convergence of the algorithm and the contribution of signal to noise (S/N) ratio will result in a more appropriate response. The stability of the controller is analyzed and finally it is implemented on the plant. The experimental results confirm the efficiency of the proposed intelligent method in controlling FJRs. Therefore, it can be considered as a proper method in controlling systems that are based on flexible robot arms.

The Hybrid Taguchi-Genetic Algorithm for Mobile Location

To estimate the mobile location is an important topic in wireless communication. It is well known that non-line-of-sight (NLOS) problem is the most pivotal part that causes the estimated error. When we transmit the signal from mobile station (MS) to base stations (BSs), the direct path between MS and BS is sealed off by some obstacles, and the signal measurements will measure the error due to the signal reflection or diffraction. The hybrid Taguchi-genetic algorithm (HTGA) combines the Taguchi method with the genetic algorithm (GA). In this paper, we bring up a novel HTGA algorithm that utilizes time of arrival (TOA) measurements from three BSs to locate MS. The proposed algorithm utilizes the intersections of three TOA circles based on HTGA to estimate the MS location. Finally, we compare HTGA with GA and find that the Taguchi algorithm can enhance genetic algorithm. We also can find that the average convergence of generation number will not be affected no matter which propagation models we use. Obviously HTGA is more robust, statistically sound, and quickly convergent than the other algorithms. The simulation results show that the HTGA can converge more quickly than GA and furthermore the HTGA can enhance the accuracy of the mobile location.

A Robust Evolutionary Algorithm for Large Scale Optimization

In this paper we present a new cooperative coevolution hybrid Taguchi-genetic algorithm (CCHTGA) to solve high-dimensional optimization problems with continuous variables which are typical in large scale systems. The CCHTGA employs cooperative coevolution frame to decompose the problem into several subsystems and adopts a novel decomposition technique “random grouping strategy”. Subsystem optimizer is an improved hybrid Taguchi-genetic algorithm (HTGA) which is effective, more robust and rapidly convergent. The performance of the CCHTGA compared to a state-of-the-art cooperative coevolution particle swarm algorithm (CCPSO2). The simulation results obtained from the application of this method to function optimization indicate that CCHTGA can be a competitive algorithm to achieve better and robust results.