Archive-based Micro Genetic Algorithm Research Articles

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

A battery track engineering vehicle faces challenges such as derailment and other safety concerns when navigating an R20m minimum radius curve, primarily owing to its low vertical and horizontal stabilities. To address these issues, a methodology integrating genetic optimization algorithms with a rigid and flexible coupled multi-body dynamics simulation is proposed to optimize the primary suspensions of the bogie of the vehicle. Initially, a multi-objective optimization model combining rigid and flexible coupled multi-body dynamics of battery track engineering vehicles with a genetic optimization algorithm was formulated. Subsequently, the optimal Latin hypercube design was applied to analyze the sensitivity of vertical and horizontal stability to various suspension parameters. Finally, a non-dominated sorting genetic algorithm (NSGA-II) and an archive-based micro genetic algorithm (AMGA) were applied to optimize the primary suspensions to enhance stability. Consequently, a set of optimal suspension parameter combinations was obtained. A notable enhancement was observed in the lateral stability of the optimized battery track engineering vehicles by 23.33% and in the vertical stability by 3.5% when traversing the R20m minimum radius curve, thereby establishing a theoretical foundation for further improving the running safety of railway vehicles and resolving the shortcomings of less research on the smallest radius curve.

Mechanical Design and Gait Optimization of Hydraulic Hexapod Robot Based on Energy Conservation

Minimizing energy consumption is significant for the hydraulic walking robot to reduce its power unit weight and increase working hours. However, most robot leg designs are inefficient due to their bio-mimetic or mission-specific mechanical structure. This paper presents a structural optimization method of the hydraulic walking robot by optimizing its mechanical structure and gait parameters simultaneously. The mathematical model of the total power of the hydraulic hexapod robot (HHR) is established, which is derived based on a general template for designing the hydraulic walking robot. The archive-based micro genetic algorithm (AMGA) is used to optimize the highly nonlinear multi-constraint multi-objective optimizations. In the optimal solution, the energy consumption of the HHR has reduced more than 40% by comparison with the original mechanical structure and gait parameter. Design sensitivity analysis is carried out to determine the regulation of mechanical structure, and a virtual prototype is used to verify the effectiveness of the proposed methods.

Optimization of the Burnishing Process for Energy Responses and Surface Properties

In the current work, the optimal factors are selected to achieve the improvements in the energy consumption (EB), power factor (PB), decreased roughness (DR) and improved surface hardness (IH) for the roller burnishing operation. The process inputs are the burnishing speed (V), the feed (f), and the depth (d). A hybrid approach comprising the principal component analysis and Technique for Order of Preference by Similarity to Ideal Solution was used to explore the weight values of burnishing performances and select the optimum parameters. Moreover, another optimization technique employing the response surface method and archive-based micro-genetic algorithm was adopted to identify the optimal outcomes in the continuous domain. The main findings showed the performances measured are primarily affected by the burnishing feed, depth and speed, respectively. The energy consumption and roughness are approximately decreased by 31.46% and 7.41%, while the power factor and hardness are improved by 17.47% and 43.09%, respectively, as compared to the general process. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for burnishing applications in industrial practices.

Optimization of WEDM process of mould material using Kriging model to improve technological performances

Improving the technical performance of the wire electro-discharge machining (WEDM) process is an effective solution to decrease manufacturing costs. This paper addresses a multi-response optimization to simultaneously improve the cutting area rate CAR and decrease the kerf width AKW, while the average surface roughness ASR is predefined as a constraint. The processing conditions considered include the pulse-on time Ton, the current I, the voltage V and the wire speed S. A WEDM machine was adopted in conjunction with the Box–Behnken matrix to conduct experimental trials for machining of SKD61 steel. Highly nonlinear relationships between machining parameters and technological outputs were developed using the Kriging models. Finally, an archive-based micro-genetic algorithm (AMGA) was used to resolve the trade-off analysis among three responses and determine the optimal values of the processing factors. The results showed that a set of feasible solutions can be determined for the low kerf width as well as the surface roughness and the high cutting area rate. The selection of optimum parameters could help the WEDM operators to save the machining costs and time. The combination of the Kriging model and AMGA could be considered as an intelligent approach for modelling WEDM processes and predicting optimal results.

Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling

This work presents the highly nonlinear relationships between processing conditions and the specific cutting energy, arithmetical mean roughness, and means roughness depth with the aid of the Kriging models in the dry milling of SKD61 material. Four processing conditions include the depth of cut, spindle speed, feed rate, and nose radius. The aim of this paper is to optimize machining factors for decreasing specific cutting energy and improving the material removal rate while the roughness properties are predefined as constraints. An evolutionary algorithm entitled archive-based micro-genetic algorithm (AMGA) is applied to generate the optimal inputs. The results show that a set of feasible optimal solutions can be determined to observe a low specific cutting energy coupled with a smooth surface and high material removal rate. Furthermore, the hybrid approach comprising the Kriging model and AMGA can be considered as an intelligent approach for optimization of the milling processes.

Study of multi-objective optimization of process parameters in film posting based on outside mold decoration

In order to optimize the process parameters in film posting based on outside mold decoration (OMD), the polymethyl methacrylate (PMMA) plastic sheets were tested in uniaxial tensile tests at high temperature with different temperatures and strain rates. The coefficients of the viscoelastic constitutive model called Duan Saigal Greif Zimmerman (DSGZ) were calculated based on the uniaxial tensile tests, and the predicted curve of DSGZ model and experimental data were compared and analyzed. The user material subroutine in ABAQUS of the DSGZ model was designed and written by adopting elastic prediction-radial consumers algorithm and was integrated into ABAQUS for the simulation of forming process of mouse shell posting film using the OMD process. After solving the model, the film thickness distribution and its deforming distribution in the X- and Y-direction were obtained. The optimization objective was to minimize the standard deviation of film thickness and its deforming distribution in the X- and Y-direction for 50 nodes on the thin film of mouse shell. The process parameters of OMD were sampled based on the method of optimal Latin hypercube design. In addition, the radial basis functions model was built. As the analysis shows, the radial basis functions model is sufficiently accurate. Based on the models, the process parameters of OMD were multi-objective optimized and analyzed by adopting archive-based micro-genetic algorithm. The best process parameters group was obtained after comprehensive analysis.

Optimization of interior roller burnishing process for improving surface quality

ABSTRACTImproving the surface characteristics of roller burnishing processes is one of effective approaches to decrease the machining costs and time. This paper systematically investigates the nonlinear relationships between machining parameters and surface characteristics, including surface roughness (Ra), surface hardness (H), and hardness depth (HD) of the interior roller burnishing using response surface method (RSM) model. Three process parameters considered include spindle speed S, feed rate F, and burnishing depth D. A set of physical experiments was carried out with AISI 1045 steel on a computer numerical control (CNC) milling machine using the roller burnishing tool. The target of the current complex optimization is to enhance the surface hardness and hardness depth, while the surface roughness is considered as the constraint. Finally, an evolutionary algorithm entitled archive-based micro genetic algorithm (AMGA) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low surface roughness and high the surface hardness as well as hardness depth. Furthermore, the integration of RSM model and AMGA can be considered as a powerful approach for modeling and optimizing interior roller burnishing processes.

Structure design and multi-objective optimization of a novel crash box based on biomimetic structure

In order to improve the crashworthiness and energy absorption performance, this paper introduces the structural bionics to the structural design of crash box and proposes a novel structure. Taking the human tibia as a bionic object, the novel crash box is composed of a concave structure shell and an inner core filled with negative Poisson's ratio (NPR) structure material. In view of the gradient characteristics of the cancellous bone structure of the tibia, the NPR inner core is designed as a functional gradient distribution structure along the longitudinal direction of the crash box. Based on this, by combining the optimal Latin hypercube design and response surface methodology, a multi-objective optimization design is conducted for the novel crash box based on archive-based micro genetic algorithm (AMGA) and improved non-dominated sorting genetic algorithm (NSGA-II). Simulation results show that the novel crash box optimized by NSGA-II algorithm can improve the energy absorption characteristics and comprehensive crashworthiness more effectively, and make the collision process controllable and stable. The results of this paper can provide some reference for the design and optimization of the crash box.

Investigation of sensitivity analysis and compensation parameter optimization of geometric error for five-axis machine tool

To improve the accuracy of five-axis machine tool with a swiveling head, an approach for optimizing compensation values taking into account the sensitivity of position-independent geometric error is proposed in this paper. At first, the synthetic volumetric error model of five-axis machine tool has been established with homogeneous transformation method and multibody system theory, which describes the effect of geometric error components on position and orientation error vector intuitively. Second, the probability distributions of geometric errors in workspace of machine tool are sufficiently taken into account in order to overcome the defects of the analysis at certain locations, and then, global quantitative sensitivity analysis is introduced for determining the effect of each geometric error on precision of machine tool. Next, the optimum values are obtained by multiobjective quality loss and precision robustness trade-offs based on archive-based microgenetic algorithm (AMGA) for geometric error compensation. Finally, the geometric error compensation experiments were carried out, and the results show that the accuracy of measuring trajectories are improved significantly, which reaches 73.7% after error compensation with optimum values based on sensitivity analysis. Hence, the proposed methodology of analysis and compensation are effective for analyzing the effect of geometric errors and improving the precision of machine tool.

Multi-Objective Optimization Design for Cooling Unit of Automotive Exhaust-Based Thermoelectric Generators

In order to improve the performance of cooling units for automotive thermoelectric generators, a study is carried out to optimize the cold side and the fin distributions arranged on its inner faces. Based on the experimental measurements and numerical simulations, a response surface model of different internal structures is built to analyze the heat transfer and pressure drop characteristics of fluid flow in the cooling unit. For the fin distributions, five independent variables including height, length, thickness, space and distance from walls are considered. An experimental study design incorporating the central composite design method is used to assess the influence of fin distributions on the temperature field and the pressure drop in the cooling units. The archive-based micro genetic algorithm (AMGA) is used for multi-objective optimization to analyze the sensitivity of the design variables and to build a database from which to construct the surrogate model. Finally, improvement measures are proposed for optimization of the cooling system and guidelines are provided for future research.

Design and optimization of wheel-legged robot: Rolling-Wolf

Though the studies of wheel-legged robots have achieved great success, the existing ones still have defects in load distribution, structure stability and carrying capacity. For overcoming these shortcomings, a new kind of wheel-legged robot(Rolling-Wolf) is designed. It is actuated by means of ball screws and sliders, and each leg forms two stable triangle structures at any moment, which is simple but has high structure stability. The positional posture model and statics model are built and used to analyze the kinematic and mechanical properties of Rolling-Wolf. Based on these two models, important indexes for evaluating its motion performance are analyzed. According to the models and indexes, all of the structure parameters which influence the motion performance of Rolling-Wolf are optimized by the method of Archive-based Micro Genetic Algorithm(AMGA) by using Isight and Matlab software. Compared to the initial values, the maximum rotation angle of the thigh is improved by 4.17%, the maximum lifting height of the wheel is improved by 65.53%, and the maximum driving forces of the thigh and calf are decreased by 25.5% and 12.58%, respectively. The conspicuous optimization results indicate that Rolling-Wolf is much more excellent. The novel wheel-leg structure of Rolling-Wolf is efficient in promoting the load distribution, structure stability and carrying capacity of wheel-legged robot and the proposed optimization method provides a new approach for structure optimization.

A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem.

AMGA2: improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization

In this article, an improved Archive-based Micro Genetic Algorithm (referred to as AMGA2) for constrained multi-objective optimization is proposed. AMGA2 is designed to obtain fast and reliable convergence on a wide variety of optimization problems. AMGA2 benefits from the existing literature in that it borrows and improves upon several concepts from existing multi-objective optimization algorithms. Improvements and modifications to the existing diversity assessment techniques and genetic variation operators are also proposed. AMGA2 employs a new kind of selection strategy that attempts to reduce the probability of exploring less desirable search regions. The proposed AMGA2 is a steady-state genetic algorithm that maintains an external archive of best and diverse solutions and a very small working population. AMGA2 has been designed to facilitate the decoupling of the working population, the external archive, and the number of solutions desired as the outcome of the optimization process. Comprehensive benchmarking and comparison of AMGA2 with the current state-of-the-art multi-objective optimization algorithms demonstrate its improved search capability.