Tabu Search Optimization Research Articles

Adaptive Tabu Search Algorithm Based on Quantum Dwelling and KNN Classifier for Creative Game Selection in Preschool Education

In order to select the best creative games for preschool education more quickly and accurately, a method for selecting creative games for preschool education based on tabu search tabu search (TS) algorithm is proposed. The TS algorithm is improved by using quantum population, and an adaptive neighborhood mapping mechanism is formed to dynamically adjust the tabu length and accelerate the convergence of the algorithm. The KNN algorithm is used to calculate the initial solution of the characteristics of preschool creative games, and the quantum adaptive optimization tabu search algorithm is used to iterate from the initial solution, and the subset of feature vectors containing feature weights and feature selection vectors is continuously searched as the input of KNN classifier. The improved objective function is used as a guide to combine the selection results of classifiers by simple voting, and the output of the selection results of creative games in preschool education is realized. Experiments show that this method can select more critical features of creative games in preschool education at a faster speed while ensuring the accuracy of the selection results. At the same time, the improved tabu search algorithm has faster convergence speed and better search performance, and can recommend creative games that are most suitable for preschool education according to the actual situation.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

A Novel Optimized Colonic adenocarcinoma Detection using Deep Transfer Learning Approach with XceptionTS Model

Colonic adenocarcinoma is a major contributor to global mortality, highlighting the crucial need for efficient detection and classification techniques. This research presents a new method called XceptionTS for classifying and detecting colon cancer using colonoscopy pictures. The XceptionTS method utilizes deep transfer learning techniques by leveraging the Xception model architecture. Nonlinear Mean Filtering (NMF) is used as a noise reduction method in image processing to improve the quality of colonoscopy pictures. We combine the MobileNetV2 and ResNet-50 models for healthcare image segmentation and feature extraction, respectively. The XceptionTS classifier efficiently gives accurate class labels to medical photos by combining Tabu Search Optimization with the strong Xception architecture. The assessment of the effectiveness of XceptionTS model is done using a dataset of 1560 colonoscopy images. An extensive comparison study is undertaken by analyzing the efficacy of our suggested approach with existing research. The XceptionTS system outperforms previous methodologies in colon cancer classification and detection tasks, showing higher accuracy and robustness according to experimental results. Our findings indicate that the XceptionTS technique shows potential as an advanced tool to increase the effectiveness of Colonic adenocarcinoma diagnosis, which could lead to better patient outcomes and healthcare management.

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

This paper delves into the practical application of K-Nearest Neighbors (KNN), Kernel Ridge Regression (KRR), and Lasso Regression for the prediction of viscosity of ionic liquids in a dataset characterized by categorical variables (Cation, Anion) and numeric variables (T(K), xIL(mol%)). Indeed, mole percentage of ionic liquids and temperature were considered as inputs for the models. The models' effectiveness is rigorously assessed, with K-Nearest Neighbors notably exhibiting exceptional predictive performance. To enhance model accuracy, Tabu Search is employed as an optimization tool for hyperparameter tuning. Numeric results showcase KNN's superiority, supported by a remarkable R2 test score of 0.91628 and the lowest RMSE among the models. Tabu Search optimization further refines model performance, emphasizing the critical role of hyperparameter tuning in achieving robust regression models in predicting the viscosity of ionic liquid-water mixtures. This study contributes valuable insights into the optimization process, demonstrating tangible improvements in predictive accuracy for viscosity predictions in similar contexts.

Protecting Sensitive Images with Improved 6-D Logistic Chaotic Image Steganography

In the digital age, strong methods for safeguarding sensitive data are essential. Image steganography is a practical technique for data protection that hides information inside seemingly authentic images. The proposed method increases security by using chaotic based image encryption and decryption. Encryption approaches based on chaotic logistic theory present an abundance of attractive and novel opportunities for developing secure image encryption methods. Nevertheless, these maps are frequently used for specific, unpredictable starting parameters. This problem is addressed by the research's Tabu search optimisation method. This method optimises chaotic map initial settings using a fitness function with numerous objectives. For image encryption, the most efficient methods are used to produce confidential keys. The input picture undergoes horizontal and vertical diffusion and permutation during encryption. These operations further scramble the image data, making it even more difficult to detect the hidden information. The primary goal is to develop a safe method for encrypting both color and grayscale images. Two common grayscale and color images that are accessible to the public were used to test the suggested strategy. compare our method with key existing works, specifically on the basis of Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal-to-Noise Ratio (PSNR) and speed, respectively, our approach improves upon these studies. Our results demonstrate that 7.9981, 99.8245, 35.6507, and 78.871 are the greatest values of entropy, NPCR, UACI, and PSNR, respectively. Whereas the respective speeds of encryption and decryption increase to 0.8433 and 1.4387 seconds. This innovative approach presents a powerful tool for applications requiring secure image steganography

Toward sustainable water quality monitoring systems using particle swarm, Ant Colony, and Tabu Search optimization methods

Toward sustainable water quality monitoring systems using particle swarm, Ant Colony, and Tabu Search optimization methods

Controlling the significance of BLDC motor internal faults using dual examine algorithm in electric vehicle applications

<span lang="EN-US">This paper presents dual examine algorithm (DEA) to reduce residual error as well as to provide accurate phase currents without any distortion for a closed loop brushless direct current (BLDC) motor of an electric vehicle (EV). The underlying technology of DEA is a hybrid of the tabu search optimization (TSO) method and the genetic algorithm (GA). During closed loop operation of BLDC motor residual error is introduced by the discrepancy between the actual and reference speed, and the phase current distortion lowers the efficiency of the machine as a result machine performance is degraded. To address these issues, GA algorithm calculates the necessary parameters for the controller to produce precise current without distortion based on stator phase currents, and the suggested TSO algorithm limits the repeated operations in the PID controller to reduce the residual error to the greatest degree feasible. After primary examining, dual examine process initiate the transposing operation such as TSO is used to prove and calculate the phase current controller parameters, and GA is used to correct for remaining inaccuracy. To validate the proposed DEA algorithm is compared with advanced particle swarm optimization (APSO). The results verified the superiority of proposed DEA algorithm using MATLAB/Simulink platform.</span>

Machine learning in seismic structural design: an exploration of ANN and tabu-search optimization

Machine learning in seismic structural design: an exploration of ANN and tabu-search optimization

Navigating route planning for multiple vehicles in multifield agriculture with a fast hybrid algorithm

Optimisation of route planning is becoming increasingly valuable aspect in agriculture. This study focuses on Agricultural Route Planning (ARP) in multifield areas (with a specific entrance point), incorporating several heterogeneous agricultural machines. The aim of this research is to improve the route planning of (semi-)autonomous machines by producing more efficient route plans. The problem sets of ARP contain both medium and large numbers of tracks consisting of irregular and rectangular fields. This research proposes a Fast Hybrid Algorithm (FHA) to address this problem. FHA incorporates various combinatorial operators into its structure. The experimental results demonstrate that, compared to Tabu Search, (Improved) Genetic Algorithm and Ant Colony Optimisation, FHA can reduce the distance travelled by an average of 16.21%. Furthermore, the efficiency of FHA is also reflected in its running time, which saves up to 54.23% compared to the other methods.

Fuel loading pattern optimization of ALLEGRO fast reactor using the tabu search metaheuristic technique

In this work an improved tabu search (TS) optimization technique is used to find the optimal fuel loading pattern in the experimental fast reactor ALLEGRO. The penalty function method is used to solve this multi-objective combinatorial optimization problem. The objective function and constrains are computed via an interface between the developed optimizer and the ERANOS 2.3 deterministic code. To reduce the running time, the operating cycle simulation is carried out using the diffusion approximation and a 7-group neutron energy structure. The proposed objective function maximizes the k-eff value at the end of cycle, satisfying the maximum power factor, the excess hot reactivity, and the linear heat generation rate constraints. A novel procedure developed in ERANOS to perform fuel assembly swaps for neighbor search is presented. The algorithm performed as expected throughout the iterative process and, unlike other metaheuristics based on local search, did not get stuck in local optima, which is a well-known advantage of tabu search technique.

Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent

This paper investigates the application of Support Vector Regression with Quadratic Kernel (QSVR) for modeling the solubility of lornoxicam in supercritical carbon dioxide. The dataset comprises temperature (T) and pressure (P) as input variables, while the solubility (Y) of lornoxicam serves as the output variable entire the modeling. The temperature is measured in Kelvin (K), and the pressure is measured in bar as the inputs of models. To improve the predictive performance of the QSVR model, three distinct hyper-parameter optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), and Bayesian Hyperparameter Optimization (BHO) are employed. These optimization methods are utilized to fine-tune the hyper-parameters of the QSVR model and enhance its predictive accuracy. The BHO-QSVR model achieved an impressive R2 score of 0.96725, indicating a strong fit between the predicted and actual solubility values. Additionally, it exhibited a Mean Absolute Error (MAE) of 1.75666E-05 and a maximum error of 3.02849E-05. Comparatively, the GA-QSVR and TS-QSVR models also performed well, achieving R2 scores of 0.95346 and 0.95882, respectively. The GA-QSVR model achieved an MAE of 1.56725E-05 and a maximum error of 4.92382E-05, while the TS-QSVR model exhibited an MAE of 1.84075E-05 and a maximum error of 5.02443E-05.

An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence.

Publisher Correction: ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications

Publisher Correction: ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications

ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications

ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications

Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material

Poor surface finish on die and mould transfers the bad quality to processed parts. High surface roughness is an example of bad surface finish that is normally reduced by manual polishing after conventional milling machining process. Therefore, in order to avoid disadvantages by manual polishing and disadvantage by the machining, a sequence of two machining operations is proposed. The main operation is run by the machining and followed by Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this sequence operation requires optimum input parameters to generate the lowest surface roughness. Hence, this paper aims to optimize the input parameters for both machining operations by three soft-computing approaches – Genetic Algorithm, Tabu Search, and Particle Swarm Optimization. The method adopted in this paper begins with a fitness function development, optimization approach usage and ends up with result evaluation and validation. The soft-computing approaches result outperforms the experiment result in having minimum surface roughness. Based on the findings, the conclusion suggests that the lower surface roughness can be obtained by applying the input parameters at maximum for the cutting speed and vibration frequency, and at minimum for machining feed rate. This finding assists manufacturers to apply proper input values to obtain parts with minimum surface roughness.

Energy-Aware Clustering in the Internet of Things using Tabu Search and Ant Colony Optimization Algorithms

Energy-Aware Clustering in the Internet of Things using Tabu Search and Ant Colony Optimization Algorithms

Fuzzy modeling system based on GA fuzzy rule extraction and hybrid of differential evolution and Tabu search approaches: Application in synthesis gas conversion to valuable hydrocarbons process

In the present study, a fuzzy approach is employed in order to develop a model for the conversion of synthesis gas to valuable hydrocarbon process. The fuzzy rules are extracted by clustering technique in this study. The numbers of fuzzy sets for input and output variables are optimized by genetic algorithm (GA) so that the number of data which follows the inconsistent rules, is minimized. Firstly, the center values of clusters are estimated by a proposed logarithmic distance method and the clusters and their centers are then calculated by k-means method. The prediction of CO conversion and olefin and paraffin selectivities is performed based on five input variables: H2/CO content, Time on Stream (TOS), temperature, Weight Hourly Space Velocity (WHSV) and phosphorous content by the proposed fuzzy model so that the parameters of which are optimized by combination of Differential Elevation and Tabu Search optimization approaches (DETS). The comparison of the performance of the designed approaches with two other models (i.e. fuzzy and GA fuzzy models) is executed and the results clearly demonstrate that the developed DETS- GA clustering fuzzy model can be defined as a powerful tool in modeling of the conversion of synthesis gas to hydrocarbons process with regression analysis (R2) of over 96%.

Time-Windowed Vehicle Routing Problem: Tabu Search Algorithm Approach

Vehicle routing problem (VRP); it is defined as the problem of planning the best distribution or collection routes of the vehicles assigned to serve the scattered centers from one or more warehouses in order to meet the demands of the customers. Vehicle routing problem has been a kind of problem in which various studies have been done in recent years. Many vehicle routing problems include scheduling visits to customers who are available during certain time windows. These problems are known as vehicle routing problems with time windows (VRPTWs). In this study, a tabu search optimization is proposed for the solution of time window vehicle routing problem (VRPTWs). The results were compared with the current situation and the results were interpreted.

Evaluation and Analysis of Intelligent Logistics Distribution Using the Expectation-Maximization Algorithm Calculation Model

The purpose of this article is to solve the problem that the accuracy of logistics distribution path planning is affected by the lack of data in the process of traditional logistics distribution planning and management. This exploration innovatively applies an effective data addition algorithm expectation-maximization (EM) algorithm to the intelligent logistics distribution system to improve logistics distribution’s overall efficiency and management quality. First, the concept of intelligent logistics and the composition and main functions of the intelligent logistics system are introduced. Then, the core idea of the EM algorithm and its applications in intelligent logistics are described. The logistics distribution of a chain company is taken as an example. Finally, the advantages and disadvantages of the intelligent logistics system based on the EM algorithm are compared with those of the traditional intelligent logistics systems based on variable neighborhood search (VNS), Tabu search (TS), and ant colony optimization (ACO). The performance test results show that the EM algorithm’s optimal solution times are 7 times. Its convergence speed is slightly lower than that of the ACO, but there is no obvious difference. The intelligent logistics distribution system based on the EM algorithm has faster order processing speed and higher efficiency in the actual case application. The average processing time of each order is 1.78 min, which is 0.237 min less than that of VNS and only 0.022 min more than that of ACO. It reveals that the intelligent logistics distribution system based on the EM algorithm is more efficient. The study provides a new idea for the efficient distribution of enterprise logistics.