Electromagnetism-Like Mechanism Algorithm with New Charge Formula for Optimization

The electromagnetism-like(EM)algorithm is a meta-heuristic optimization algorithm,which uses a novel searching mechanism called attraction-repulsion between charged particles.It is worth pointing out that there are two potential problems in the calculation of particle charge by the original EM algorithm.One of the problems is that the information utilization rate of the population is not high,and the other problem is the decline of population diversity when the population size is much greater than the dimension of the problem.In contrast,it is more fully to exploit the useful search information based on the proposed new quadratic formula for charge calculation in this paper.Furthermore,the population size was introduced as a new multiplier term to improve the population diversity.In the end,numerical experiments were used to verify the performance of the proposed method,including a comparison with the original EM algorithm and other well-known methods such as artificial bee colony(ABC),and particle swarm optimization(PSO).The results showed the effectiveness of the proposed algorithm.

Gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems

A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.

A CLOSED-FORM ALGORITHM FOR THE STEADY-STATE RESPONSE OF ELASTIC MECHANISMS

This paper presents a closed-form algorithm for the steady-state response of elastic mecha-nisms. Based on an analytic expression of the initial conditions, the steady-state response can beobtained by just one cycle of integration, thus the algorithm is of high efficiency. The algorithm isthen verified by comparing the computational results with the previously published experimental re-sults.

Performance Comparison of Electromagnetism-Like Algorithms for Global Optimization

Electromagnetism-like (EML) algorithm is a new evolutionary algorithm that bases on the electromagnetic attraction and repulsion among particles. It was originally proposed to solve optimization problems with bounded variables. Since its inception, many variants of the EML algorithm have been proposed in the literature. However, it remains unclear how to simulate the electromagnetic heuristics in an EML algorithm effectively to achieve the best performance. This study surveys and compares the EML algorithms in the literature. Furthermore, local search and perturbed point are two techniques commonly used in an EML algorithm to fine tune the solution and to help escaping from local optimums, respectively. Performance study is conducted to understand their impact on an EML algorithm.

Machining Parameters Optimization of Multi-Pass Face Milling Using a Chaotic Imperialist Competitive Algorithm with an Efficient Constraint-Handling Mechanism

The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass milling.Optimization of machining parameters is of great significance.However,it is a nonlinear constrained optimization problem,which is very difficult to obtain satisfactory solutions by traditional optimization methods.A new optimization technique combined chaotic operator and imperialist competitive algorithm(ICA)is proposed to solve this problem.The ICA simulates the competition between the empires.It is a population-based meta-heuristic algorithm for unconstrained optimization problems.Imperialist development operator based on chaotic sequence is introduced to improve the local search of ICA,while constraints handling mechanism is introduced and an imperialist-colony transformation policy is established.The improved ICA is called chaotic imperialist competitive algorithm(CICA).A case study of optimizing machining parameters for multi-pass face milling operations is presented to verify the effectiveness of the proposed method.The case is to optimize parameters such as speed,feed,and depth of cut in each pass have yielded a minimum total product ion cost.The depth of cut of optimal strategy obtained by CICA are 4 mm,3 mm,1 mm for rough cutting pass 1,rough cutting pass 1 and finish cutting pass,respectively.The cost for each pass are$0.5366 US,$0.4473 US and$0.3738 US.The optimal solution of CICA for various strategies with at=8 mm is$1.3576 US.The results obtained with the proposed schemes are better than those of previous work.This shows the superior performance of CICA in solving such problems.Finally,optimization of cutting strategy when the width of workpiece no smaller than the diameter of cutter is discussed.Conclusion can be drawn that larger tool diameter and row spacing should be chosen to increase cutting efficiency.

Integrated Optimization of Mechanisms with Genetic Algorithms

This paper offers an integrated optimization of mechanisms with genetic algorithm, the principle of which is to use a neural network as a global calculation program and to couple the network with stochastic methods of optimization. In other words, this paper deals with the integrated optimization of mechanisms with genetic algorithms, and, in conclusion, the possible use of neural networks for complex mechanisms or processes.

Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

Seismic relocation,focal mechanism and crustal seismic anisotropy associated with the 2010 Yushu M_S7.1 earthquake and its aftershocks

The 2010 Yushu MS7.1 earthquake occurred in Ganzi-Yushu fault,which is the south boundary of Bayan Har block.In this study,by using double difference algorithm,the locations of mainshock(33.13°N,96.59°E,focal depth 10.22 km)and more than 600 aftershocks were obtained.The focal mechanisms of the mainshock and some aftershocks with MS>3.5 were estimated by jointly using broadband velocity waveforms from Global Seismic Network(GSN)and Qinghai Seismic Network as well.The focal mechanisms and relocation show that the strike of the fault plane is about 125°(WNW-ESE),and the mainshock is left-laterally strike-slip.The parameters of shear-wave splitting were obtained at seismic stations of YUS and L6304 by systematic analysis method of shear-wave splitting(SAM)method.Based on the parameters of shear-wave splitting and focal mechanism,the characteristics of stress field in seismic source zone were analyzed.The directions of polarization at stations YUS and L6304 are different.It is concluded that after the mainshock and the MS6.3 aftershock on April 14,the stress-field was changed.

Feedback Mechanism-driven Mutation Reptile Search Algorithm for Optimizing Interpolation Developable Surfaces

Curvature lines are special and important curves on surfaces.It is of great significance to construct developable surface interpolated on curvature lines in engineering applications.In this paper,the shape optimization of generalized cubic ball developable surface interpolated on the curvature line is studied by using the improved reptile search algorithm.Firstly,based on the curvature line of generalized cubic ball curve with shape adjustable,this paper gives the construction method of SGC-Ball developable surface interpolated on the curve.Secondly,the feedback mechanism,adaptive parameters and mutation strategy are introduced into the reptile search algorithm,and the Feedback mechanism-driven improved reptile search algorithm effectively improves the solving precision.On IEEE congress on evolutionary computation 2014,2017,2019 and four engineering design problems,the feedback mechanism-driven improved reptile search algorithm is compared with other representative methods,and the result indicates that the solution performance of the feedback mechanism-driven improved reptile search algorithm is competitive.At last,taking the minimum energy as the evaluation index,the shape optimization model of SGC-Ball interpolation developable surface is established.The developable surface with the minimum energy is achieved with the help of the feedback mechanism-driven improved reptile search algorithm,and the comparison experiment verifies the superiority of the feedback mechanism-driven improved reptile search algorithm for the shape optimization problem.

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.

Dynamic Accuracy Analysis of a 5PSS/UPU Parallel Mechanism Based on Rigid-Flexible Coupled Modeling

In order to improve the low output accuracy caused by the elastic deformations of the branch chains,a finite element-based dynamic accuracy analysis method for parallel mechanisms is proposed in this paper.First,taking a 5-prismatic-spherical-spherical(PSS)/universal-prismatic-universal(UPU)parallel mechanism as an example,the error model is established by a closed vector chain method,while its influence on the dynamic accuracy of the parallel mechanism is analyzed through numerical simulation.According to the structural and error characteristics of the parallel mechanism,a vector calibration algorithm is proposed to reduce the position and pose errors along the whole motion trajectory.Then,considering the elastic deformation of the rod,the rigid-flexible coupling dynamic equations of each component are established by combining the finite element method with the Lagrange method.The elastodynamic model of the whole machine is obtained based on the constraint condition of each moving part,and the correctness of the model is verified by simulation.Moreover,the effect of component flexibility on the dimensionless root mean square error of the displacement,velocity and acceleration of the moving platform is investigated by using a Newmark method,and the mapping relationship of these dimensionless root mean square errors to dynamic accuracy is further studied.The research work provides a theoretical basis for the design of the parameter size of the prototype.

An Iteration Method Based on Homotopy Function for Solving Polynomial Systems and Application to Mechanisms Problems

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Reduced Detailed Mechanism for Methane Combustion

Simulated results from a detailed elementary reaction mechanism for methane-containing species in flames consisting of nitrogen (NOx), C1 or C2 fuels are presented, and compared with reduced mechanism;this mechanism have been constructed with the analysis of the rate sensitivity matrix f (PCAF method), and the computational singular perturbation (CSP). The analysis was performed on solutions of unstrained adiabatic premixed flames with detailed chemical kinetics described by GRI 3.0 for methane including NOx formation. A 9-step reduced mechanism for methane has been constructed which reproduces accurately laminar burning velocities, flame temperatures and mass fraction distributions of major species for the whole flammability range. Many steady-state species are also predicted satisfactorily. This mechanism is especially for lean flames. This mechanism is accurate for a wide range of the equivalence ratio (1, 0.9, 0.8, and 0.7) and for pressures as high as 40 atm to 60 atm. For both fuels, the CSP algorithm automatically pointed to the same steady-state species as those identified by laborious analysis or intuition in the literature and the global reactions were similar to well established previous methane-reduced mechanisms. This implies that the method is very well suited for the study of complex mechanisms for heavy hydrocarbon combustion.

Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm

Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research.This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys.Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys,a momentum back-propagation(BP)neural network with a single hidden layer was established.Particle swarm optimization(PSO)was applied to optimize the BP model.In the neural network,the input variables were the contents of Mg,Li and Al,and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy's composition and its mechanical properties.It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model.The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.

Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation(BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA) is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM? Tog-L-Loc system with different technologic parameters.The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network(ANN) for training.The validating samples' experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

An Optimization Design of a Weft Insertion Mechanism for Rapier Looms

By analyzing a combined and spatial 6-bar linkage weft insertion mechanism, its practical model for optimization design is set up and the modification of penalty strategy is put forward so that the genetic algorithm can be better used in optimization design for mechanisms with non- linear constraints. The design result is discussed.

Structure Analysis of Locking Mechanism of Gear-Rack Typed Ship-Lift

Contact nonlinear theory was researched. Contact problem was transformed into optimization problem containing Lagrange multiplier, and unsymmetrical stiffness matrix was transformed into symmetrical stiffness matrix. A finite element analysis (FEA) model defining more than 300 contact pairs for long nut-short screw locking mechanism of a large-scale vertical gear-rack typed ship-lift was built. Using augmented Lagrange method and symmetry algorithm of contact element stiffness, the FEA model was analyzed, and the contact stress of contact interfaces and the von Mises stress of key parts were obtained. The results show that the design of the locking mechanism meets the requirement of engineering, and this method is effective for solving large scale nonlinear contact pairs.