Dynamic Neighborhood Particle Swarm Optimization Research Articles

Research on Fault Diagnosis of External Short Circuit of Lithium Battery for Electric Vehicle

This study conducted an experimental study on the external short circuit (ESC) fault characteristics of lithium-ion batteries for electric vehicles. An experimental platform was established to simulate the electrical behavior of lithium batteries during ESC failure using a modified first-order RC model. The model parameters are reidentified by the dynamic neighborhood particle swarm optimization algorithm. An ESC fault diagnosis algorithm based on two-layer model is proposed. The first layer performs initial fault detection and the second layer performs accurate model-based diagnostics. The four new units are shorted to evaluate the proposed algorithm. The results show that the ESC fault can be diagnosed within 5 s, and the error between the model and the measured data is less than 0.36 V. The proposed algorithm can make a correct diagnosis.

Multi-objective optimization for surface grinding process using a hybrid particle swarm optimization algorithm

Optimization for the surface grinding process is a problem with high complexity and nonlinearity. Hence, evolutionary algorithms are needed to apply to get the optimum solution of the problem instead of the traditional optimization algorithms. In this work, a hybrid particle swarm optimization (HPSO) algorithm which combines the dynamic neighborhood particle swarm optimization (DN-PSO) algorithm with the strategy of mutation considering constraints is presented to handle multi-objective optimization for surface grinding process. Such four process parameters as wheel speed, workpiece speed, depth of dressing, and lead of dressing are considered as the variables for optimization, and the following three objectives such as production cost, production rate, and surface roughness are used in a multi-objective function model with a weighted approach. Meanwhile, the constraints of thermal damage, wheel wear, and machine tool stiffness are considered. Computational experiments are conducted on cases of both rough grinding and finish grinding, and comparison results with the previously published results obtained by using other optimization techniques shows the efficiency of the proposed algorithm.

Dynamic Neighborhood Particle Swarm Optimization Based on External Archive

According to the defect of falling into a local optimum when dealing with multimodal problems with basic particle swarm optimization, a dynamic neighborhood particle swarm optimization with external archive (EA-DPSO) is proposed. The Ring topology, All topology and Von Neumann topology are adopted, and dynamically refining particle history optimal position, and then store them on the external archive. In terms of particles characteristics in the external archive, a kind of effective extract mechanism method is designed to choose learning sample. Three peak problems as simulation function are chosen and the results show that EA-DPSO can effectively jump out of local optimal solution. Therefore, it can be seen as an effective algorithm for solving multimodal problems.

Modified PSO algorithm for multi-objective optimization of the cutting parameters

Economic profit of machining is essentially based on the optimal selection of cutting parameters. In this paper, a multi-objective particle swarm optimization approach is introduced to optimize the cutting parameters in turning processes: cutting speed, feed rate and cutting depth. The proposed model presents the problem in form of a multi-objective problem with production rate and used tool life objectives and has a set of constraints that represent the important limitations to be satisfied. To obtain the non dominated solutions and build the Pareto front graph, a modified dynamic neighborhood particle swarm optimization (DNPSO) technique is used. In addition, a fuzzy-based mechanism is employed to extract the best compromise solution. The results on an illustrative sample reveal the capabilities of the proposed DNPSO approach to generate well-distributed Pareto optimal solutions. Comparison with multi-objective deterministic approach (Min–Max) shows the superiority of the proposed approach and confirms its potential for solving multi-objective problems.

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.