Improved Niche Genetic Algorithm Research Articles

Parameters optimum of multiple underframe suspended equipment on high-speed railway vehicle carbody vibration control by using an improved genetic algorithm

Carbody vibration control and ride comfort improvement are important research parts of high-speed railway vehicle dynamic. The effectiveness of utilizing carbody underframe suspended equipment as dynamic vibration absorbers for carbody vibration suppression has been validated. While previous research has focused mainly on a single suspended equipment without considering its vibration tolerance, a balance must be struck between reducing carbody vibration and maintaining equipment performance. To address the challenging, a multi-objective and multi-parameter optimization method is employed, integrating an improved niche genetic algorithm (INGA) into the numerical simulation process. Considering carbody flexible and multi-suspended equipment, a rigid-flexible dynamics model of a high-speed railway vehicle is established and validated on a vehicle dynamics test rig. Based on the numerical analysis, the optimal parameter combination of the stiffness and the longitudinal installation position is obtained from an evaluation function that comprehensively assesses the vibration performance of both the vehicle carbody and the equipment. Comparative analysis between the results obtained from the optimal and primitive parameters demonstrates that the optimization process not only achieves significant improvements in carbody vibration reduction but also effectively controls equipment vibration within reasonable limits. The multi-objective and multi-parameter optimization method applied on carbody underframe suspended system proves to be valuable in improving the dynamic performance of high-speed railway vehicles.

Identification of Vivo Material Parameters of Arterial Wall Based on Improved Niching Genetic Algorithm and Neural Networks

Cardiovascular diseases are seriously threatening human health and the incidence rate is high. Many scholars are devoted to studying arterial mechanical properties and material parameters. In this study, the bovine artery was selected as the experimental object and the uniaxial tensile test was carried out by cutting the specimens along its axial, circumferential and [Formula: see text] directions. The finite element software ABAQUS and hyperelastic Holzapfel Gasser Ogden (HGO) constitutive model were used to simulate the experimental process. Niche technology is introduced on the basis of genetic algorithm, and the program of Improved Niche Genetic Algorithm for material parameter identification is compiled based on Python language. In addition, BP Neural Network was constructed based on Tensorflow mathematical system. The material parameters of the constitutive model of bovine artery in different directions were identified by finite element method and experimental data. The results show that Improved Niche Genetic Algorithm and Neural Network, respectively, combined with finite element are both effective and accurate methods for predicting the parameters of arterial vascular hyperelastic materials, which can provide reference and help for the study of arterial vascular mechanical properties.

Parameter optimization of multi-suspended equipment to suppress carbody vibration of high-speed railway vehicles: a comparative study

ABSTRACTParameter optimization of equipment suspended under the carbody underframe is one of the important measures to suppress carbody vibration of high-speed trains. Previous work focused on using one equipment as a dynamic vibration absorber to control carbody vibration, overlooking the coupling effects of multiple equipments and their vibration tolerance. To address this, a comprehensive study involving multi-objective and multi-parameter optimization to enhance both carbody and equipment vibration performance is proposed. First, a theoretical model with one equipment is established to derive optimal parameters, and a simulation model with multi-suspended equipment is built to conduct optimize calculations with an improved niche genetic algorithm (INGA). Afterwards, a comparative study on carbody and equipment vibration performance is analysed based on the optimal parameters, proving that the optimal method with INGA is more effective in improving vehicle performance. Finally, the method is applied to a newly designed vehicle, and the performance is validated on a rig, providing technical support for the design of high-speed vehicles.

Research on discrete intelligent workshop lot-streaming scheduling with variable sublots under engineer to order

Engineer to order is a highly customized production pattern. A flexible job shop scheduling problem with lot streaming is introduced in the discrete intelligent workshop, which considers setup time, changeover time, and single-piece flowing workpieces under engineer to order. For lot streaming, the advantages of variable sublots are analyzed by comparing different lot splitting strategies. Upon variable sublots, a mixed-integer linear programming model is formulated, but gets restricted in solving large-size problems. Then a hyper-heuristic improved genetic algorithm consisting of high and low levels is proposed. An improved niche genetic algorithm is employed for the high level due to its excellent global search performance, while the low level encapsulates the particle swarm optimization as its perturbation operator. Furthermore, several problem-oriented components are designed to enhance its performance, for example, a high-low level coordination mechanism with clearly global–local division, a decoding method based on greedy and saving rules, a self-adaptive earliest start time, and a population initialization and resetting method. Finally, experiments are organized, and the result shows the proposed algorithm performs up to 24.46% better than the reference group in solving benchmark instances, for which several components are tested to distinguish their main contributions. The general computational performance is also examined through the CEC’17 test suite. For enterprise examples with medium to large sizes, an average 99.52% operation rate demonstrates its high practicality.

A Novel Constitutive Parameters Identification Procedure for Hyperelastic Skeletal Muscles Using Two-Way Neural Networks

Muscle soreness can occur after working beyond the habitual load, especially for people engaged in high-intensity work load. Prediction of hyperelastic material parameters is essentially an inverse process, which possesses challenges. This work presents a novel procedure that combines nonlinear finite element method (FEM), two-way neural networks (NNs) together with experiments, to predict the hyperelastic material parameters of skeletal muscles. FEM models are first established to simulate nonlinear deformation of skeletal muscles subject to compressions. A dataset of nonlinear relationship between nominal stress and principal stretch of skeletal muscles is created using our FEM models. The dataset is then used to establish two-way NNs, in which a forward NN is trained and it is in turn used to train the inverse NN. The inverse NN is used to predict the hyperelastic material parameters of skeletal muscles. Finally, experiments are carried out using fresh skeletal muscles to validate the predictions in great detail. In order to examine the accuracy of the two-way NNs predicted values against the experimental ones, a decision coefficient [Formula: see text] with penalty factor is introduced to evaluate the performance. Studies have also been conducted to compare the present two-way NNs approach with the other existing methods, including the directly (one-way) inverse problem NN, and improved niche genetic algorithm (INGA). The comparison results show that two-way NNs model is an accurate approach to identify the hyperelastic parameters of skeletal muscles. The present two-way NNs method can be further expanded to the predictions of constitutive parameters of other type of nonlinear materials.

Fuzzy Random Characterization of Pore Structure in Frozen Sandstone: Applying Improved Niche Genetic Algorithm

Nuclear magnetic resonance (NMR) technology provides an innovative method employed in detecting the porous structures in frozen rock and soil masses. On the basis of NMR relaxation theory, fuzzy random characteristics of the NMR T2 spectrum and pore structure are deeply analyzed in accordance with the complex and uncertain distribution characteristics of the underground rock and soil structure. By studying the fuzzy random characteristics of the NMR T2 spectrum, the fuzzy random conversion coefficient and conversion method of the T2 spectrum and pore size distribution are generated. Based on the niche principle, the traditional genetic algorithm is updated by the fuzzy random method, and the improved niche genetic algorithm is proposed. Then, the fuzzy random inversion of the conversion coefficient is undertaken by using the improved algorithm. It in turn makes the conversion curve of the T2 spectrum and pore size distribution align with the mercury injection test curve in diverse pore apertures. Compared with the previous least square fitting method, it provides a more accurate approach in characterizing complicated pore structures in frozen rock and soil masses. In addition, the improved niche genetic algorithm effectively overcomes the shortcomings of the traditional genetic algorithm, such as low effectiveness, slow convergence, and weak controllability, which provides an effective way for parameter inversion in the section of frozen geotechnical engineering. Finally, based on the T2 spectrum test of frozen sandstone, the fuzzy random characterization of frozen sandstone pore distribution is carried out by using this transformation method. The results illustrate that the conversion coefficient obtained through the improved algorithm indirectly considers the different surface relaxation rates of different pore sizes and effectively reduces the diffusion coupling effects, and the pore characteristics achieved are more applicable in engineering practices than previous methods.

An optimal expansion planning of electric distribution network incorporating health index and non-network solutions

The health status of distribution equipment and networks is not considered directly in existing distribution network planning methods. In order to effectively consider the health status and deal with the risk associated with load and renewable generation uncertainties, this paper presents a new optimal expansion planning approach for distribution network (EPADN) incorporating equipment's health index (HI) and non-network solutions (NNSs). HI and relevant risk are used to help develop the optimal equipment replacement strategy and temporary NNSs are considered as promising options for handling the uncertainties of load growth, reliability requirements of power supply and output of distributed energy resources (DERs) at a lower cost than network alternatives. An EPADN model using network solutions (NSs) and NNSs is proposed. The planning objectives of the proposed model are safety, reliability, economy, and `greenness' that are also the meaning of distribution network HI. A method integrating an improved niche genetic algorithm (INGA) and a spanning tree algorithm (STA) is fitted to solve the model presented here for real sized networks with a manageable computational cost. Simulation results of an actual 22-node distribution network in China, illustrate the effectiveness of the proposed approach.

Improved niche genetic algorithm based parameter identification of excitation system considering parameter identifiability

In order to obtain the parameters of excitation system accurately, and improve the accuracy and efficiency of parameter identification much further, an improved niche genetic algorithm was adopted, which can overcome local convergence of genetic algorithm by the fitness sharing mechanism that could increase population diversity. Moreover, the proposed algorithm could avoid the disadvantage of usual niche genetic algorithm that is difficult in determining the niches. By using the method of fuzzy clustering, niche groups can be created dynamically. In addition, regarding the problem that the partial parameters of excitation system cannot identify stably because of the relevance among these parameters, this study analysed the identifiability of excitation system model primarily, separated associated parameters into several parameter sets then. This type of relevance would be removed by assigning typical values to representative parameters, which belong to different sets. Identifiability analysis can not only avoid identifying parameters blindly, but also increase the credibility of the results. The test study shows that the proposed method can acquire accurate and reliable parameter values.

Constitutive Model for the Thermo-viscoplastic Behavior of Hexagonal Close-Packed Metals with Application to Ti–6Al–4V Alloy

In this paper, a new physically based constitutive model is developed for hexagonal close-packed metals, especially the Ti–6Al–4V alloy, subjected to high strain rate and different temperatures based on the microscopic mechanism of plastic deformation and the theory of thermally activated dislocation motion. A global analysis of constitutive parameters based on the Latin Hypercube Sampling method and the Spearman’s rank correlation method is adopted in order to improve the identification efficiency of parameters. Then, an optimal solution of constitutive parameters as a whole is obtained by using a global genetic algorithm composed of an improved niche genetic algorithm, a global peak determination strategy and the local accurate search techniques. It is concluded that the proposed constitutive modal can accurately describe the Ti–6Al–4V alloy’s dynamic behavior because the prediction results of the model are in good agreement with the experimental data.

Items assignment optimization for complex automated picking Systems

Order picking operation is the most expensive and time consuming process of all assignments in distribution center. The high efficient and low consumption automated systems, especially complex automated picking systems have been widely promoted and used. How to improve the efficiency and accuracy of the automated picking system becomes an important issue for improving the business capability of the distribution centers. By analyzing the operating mode of complex automated picking systems, the model of complex automated sorting systems is established and the operation time model is constructed based on the serial order picking strategy with the sequence from right to left. The items assignment optimization models for different types of picking machines are constructed, minimizing total picking time. The improved niche genetic algorithm is designed. In order to improve algorithm convergence speed, the k-means clustering method is used and the results are regarded as the initial population of clustering algorithm. By restricting the number of chromosomes whose location distribution of a certain item is fixed, the niche elimination operation process is improved and the diversity of population is maintained. Through the simulation analysis, the items assignment optimization algorithm leads to 7.5% decrease of the total order picking time, in comparison with the results determined by the traditional EIQ-ABC method. On the basis of items assignment optimization simulation experiments, items configuration and sort comparison tests in a multi-items picking area are designed, again validating the effectiveness.

A network security situation prediction model based on wavelet neural network with optimized parameters

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN) with optimized parameters by the Improved Niche Genetic Algorithm (INGA). The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA) so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and WNN.

Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm.

With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods.

Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm

The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical and microwave hybrid communication. Considering the resources, missions and restraints of data relay satellite optical and microwave hybrid links system, a model of hybrid links resources scheduling is established and a scheduling algorithm based on an improved niche genetic algorithm is put forwarded. According to the multi-user-satellite, multi-time-window, multi-class-antenna and a number of missions with priority weight, the scheduling schemes are generated randomly to begin with and evaluated with the fitness functions. To obtain an optimal scheduling result, an improved niche genetic algorithm is adopted to optimize the scheduling schemes. The simulation result reveals that a satisfactory result is obtained and the improved niche genetic algorithm has advantages in both efficiency and performance in a scenario including a relay satellite with 2 optical antennas and 1 microwave antenna for user satellites connection, 8 user satellites with 64 missions. The simulation indicates that the model and the optimization algorithm are suitable for multi-user, multi-mission and multi-class-antenna hybrid communication recourses scheduling problem.

Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning

This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.

A Master-Slave Model NGA and its Application in the Multidimensional 0-1 Knapsack Problem

The Multidimensional 0-1 knapsack problem is a NP hard problem, though there are many algorithm is used to solve the problem, but there is still not a good solution to solving the problem. This paper improved niche genetic algorithm, established a master-slave mode niche genetic algorithm, and carried on adaptive setting the individual Euclidean distance criterion, making it can changed with the evolving algebra incremental. At last, used master-slave niche genetic algorithm to solve the Multidimensional 0-1 knapsack problem, test results showed, the algorithm has good applicability and superiority in solving the Multidimensional 0-1 knapsack problem.

An Improved Niche Genetic Algorithm

As a result of poor local research performance and short of the population diversity in niche genetic algorithm (NGA), an improved niche genetic algorithm (I-NGA) is presented in this paper. The algorithm adopts migration strategy, grading gradient method and gene equilibrium strategy, and it has better performance on accelerating the convergence rate and increasing the population diversity. This approach is used in Camel function for confirming. Through comparisons to NGA and I-NGA, the improved algorithm shows its better optimization.

A Unidirectional Guided-path Network Design Method under Flexible Job Shop Environment

With the objective of minimizing makespan,an improved niche genetic algorithm(INGA) is proposed to solve the unidirectional guided-path network design problem of automated guided vehicle system under flexible job shop environment.In the genetic algorithm,each chromosome consists of unidirectional guided-path network chromosome and operation chromosome,denoting unidirectional segment direction and feasibility scheduling respectively.Corresponding crossover and mutation operators are designed for two types of chromosome.Neighborhood search is adopted to improve the convergence speed of the algorithm.To keep a high degree of population diversity,niche technology and spirit strategy are applied.The experimental results show that the proposed NGA is effective.

Finite Element Analysis and Structural Optimization of a Permanent Magnet Spherical Actuator

In order to derive the characteristics and adjust the electromagnetic system to the ultimate purpose of achieving better torque output and material usage reduction with constraints, this paper presents the finite element analysis and using improved niche genetic algorithm to the optimal design of a permanent magnet spherical actuator. The magnetic field distribution and torque calculation model based on finite element software are introduced and discussed. The proposed optimization algorithm surmounts effectively the local convergence problem of standard genetic algorithm. The sharing-between-population mechanism is proposed by means of using the better factor of population, saving best result strategy and enhancing global and partial searching ability for earlier achievement of optimal solution. The results show the output torque has been increased and the material has been effectively saved by comparison, which provides the references for related problems. Ill. 5, bibl. 15, tabl. 6 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.114.8.699