Multi-island Genetic Algorithm Research Articles

Structural Optimization Design of a Typical Adhesive Bonded Honeycomb-Core Sandwich T-joint in Side Bending Using Multi-Island Genetic Algorithm

Sandwich structure T-joints are increasingly broadly applied in aviation and aerospace industries due to the need for lightweight design. This paper deals with the lightweight optimization of a typical adhesively bonded Nomex honeycomb-core sandwich T-joint in side bending load, considering the strength constraints. The optimization problem, with discrete and continuous design variables, is a compound optimization problem involving size optimization for the whole structure and stacking sequence optimization for multiple variable-thickness composite laminates. A self-adjusted parametric modeling with user-defined suppression process is proposed. An integrated combination of progressive damage model methodology, self-adjusted parametric modeling with user-defined suppression process and multi-island genetic algorithm is applied for the optimization problem. The optimization result showed 30.75% weight reduction compared to the original T-joint configuration. On the basis of history data, we investigate the correlations between design variables and concerned constraint variables.

Performance analysis and optimization of a novel cooling plate with non-uniform pin-fins for lithium battery thermal management

• Cold plate with varying-diameter pin-fins is proposed for battery thermal management. • Heat generation rate of lithium battery cell is evaluated with experiment method. • The impacts of varying diameter pin-fins on cold plate performance are studied. • Optimization is performed to search the optimal varying pattern of pin–fin diameter. • The pump work, weight and T sd are reduced by 29.8%, 29.0% and 17.4%, respectively. The structure of the flow path has significant impacts on the performance of cooling plate for battery thermal management. This paper aims at improving the temperature uniformity, power consumption and weight performance by proposing a novel cooling plate with non-uniform pin-fins. Experimental method is employed to obtain the battery heat generation rate at 5C discharge rate. Then the performances of cooling plates with fixed diameter and varying diameter pin-fins are evaluated based on CFD method. To explore the best varying pattern of the pin–fin diameter, multi-objective optimization with multi-island genetic algorithm considering the maximum temperature (T m ), temperature standard deviation (T sd ), power consumption (P) and mass of cooling plate (m) is performed. Then numerical simulation is employed to compare the optimized design with the traditional parallel mini-channel design. The result shows that optimized design with non-uniform pin fins can reduce the power consumption, weight and temperature standard deviation by 29.84%, 29.00% and 17.43%, respectively. The maximum temperature is also reduced by 1.04 °C.

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing.

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number and penalty factor . For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy () can reflect the sparsity of the signal, and Renyi entropy () can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, and are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy.

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm.

In the present work, the working state of the crane leg is analyzed and discussed, and its structure is optimized. SolidWorks software is used for modeling; ANSYS software is used for finite element analysis. First of all, the constrained finite element method (CFEM) is used to analyze the linear eigenvalue buckling and geometric nonlinear buckling of outriggers with different cross-section shapes. Prove that the curved leg has certain advantages in buckling. At the same time, analyzing the leg along a different path of buckling condition and stress changes provide the basis for the design of the subsequent reinforcement. After selecting the best cross-section shape of the outrigger, the agent-based multi-island genetic algorithm is used to optimize the structural parameters of the outrigger under the transverse stiffened plate reinforced structure and the longitudinally stiffened plate reinforced structure respectively. It is proved that the outrigger with the transverse stiffened plate has a significant effect in improving the bearing capacity and in the lightweight of the structure. Finally, the gap between the movable leg and the fixed leg was changed, the stress of different gaps was analyzed by using the finite element method, and the appropriate gap value was selected according to the high-order fitting curve.

Parameter optimization of bidirectional re-entrant auxetic honeycomb metamaterial based on genetic algorithm

In this work, we design and test a parameter optimization method by Python script to meet the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on the selection of parameters according to the mass and Poisson’s ratio of the honeycomb metamaterial. The bidirectional re-entrant honeycomb is proposed as an objective to be optimized and the formula of Poisson's ratio is deduced theoretically to establish the internal relation. Besides, the accuracy of the Python script results is verified by static compression experimental results and theoretical results. Combined the Python script programming model with the genetic algorithm optimization method, the optimal honeycomb metamaterial solutions are obtained. Results show that the parameter optimization method using multi-island genetic algorithm (GA) can avoid a local solution’s appearance, and both the shell model and the solid model can obtain the ideal optimal solution. Furthermore, the 3D honeycomb has an admirable auxetic effect according to the optimized parameters, which provides a piece of strong evidence for the continuous application of optimization algorithms to improve the mechanical properties of honeycomb metamaterial.

Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor

An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method.

Experimental mechanics and numerical prediction on stress relaxation and unrecoverable damage characteristics of rubber materials

Stress relaxation and unrecoverable damage deformation are common phenomenon in rubber materials, which greatly induces instability of geometrical dimension and deteriorates the mechanical performances. The present work proposes experimental mechanics and numerical prediction for studying stress relaxation and unrecoverable damage characteristics. A time-dependent strain energy principle is established to describe the relaxation decay over time, and a quasi-plastic function is combined by considering the plastic flow criteria in order to depict the unrecoverable deformation after stress relaxation. Experimental study is conducted on multi-modes hyper-elastic mechanics and stress relaxation performances, and key parameters of the proposed constitutive model are identified by multi-island genetic algorithm (MIGA). The established constitutive equation is integrated in finite element simulation, and the numerical prediction is conducted. Combined experimental results clearly present nonlinear elastic properties, time-decay relaxation, and unrecoverable damage deformation of rubber materials. The numerical results are in good agreement with experiment ones, and the mechanism of relaxation and damage characteristics are explored. Additionally, based on the time-temperature superposition principle (TTSP) method, long-term stress relaxation behavior at low temperature is estimated by the short-term high-temperature test. This work provides efficient methods of experimental mechanics and numerical prediction for predicting the stress relaxation and damage characteristics of rubber materials, which could be also beneficial for characterizing rubber-base devices in engineering applications.

Optimization of a segmented mirror with a global radius of curvature actuation system based on multi-fidelity surrogates

Separately-polished segmented mirrors hardly meet the co-phasing surface shape error due to the fabrication difficulties. Therefore, some space-based segmented telescope system such as the James Webb Space Telescope (JWST) uses the global radius of curvature (GRoC) actuation system as an effective solution. A segmented mirror with a GRoC actuation system was optimized in this paper. The high-precision finite element model (FEM) usually has low computational efficiency, and the low-precision finite element model cannot guarantee calculation accuracy. In order to alleviate the conflict between computational cost and calculation accuracy in the optimization of a segmented mirror, multi-fidelity surrogates (MFS) based on sensitivity analysis were proposed. The surrogates were then optimized through the multi-island genetic algorithm (MIGA), and the segmented mirror produces 0.0623λ (λ = 632.8) surface shape error RMS per 1 mm of GRoC is corrected, which met the design requirement. Besides, it can also be deployed to solve other complicated engineering problems.

Multi-objective optimization of crash box filled with three-dimensional cellular structure under multi-angle impact loading

Oblique impact loading conditions are common in automobile collision accidents, which strongly influence the energy absorption performance of thin-walled structures. In this paper, a novel three-dimensional (3-D) hexagonal structure-filled crash box with better comprehensive crashworthiness under multiple working conditions is proposed. First, the finite element models of four 3-D typical cellular structures (hexagon structure, re-entrant hexagon structure, star-shape structure, double arrow structure) are established. The dynamic responses of four structures under different impact angles (from 0° to 30°) and impact velocities (from 5 to 15 m/s) are discussed. The results reveal that the 3-D hexagonal structure has great advantages in terms of the energy absorption at varying inclination angles. Second, the 3-D hexagonal structure is filled in the crash box in the form of gradient distribution. The multi-island genetic algorithm (MIGA) based on the response surface model (RSM) is utilized to explore the optimal design of crash box. Compared with the traditional crash box, the optimal 3-D hexagonal structure-filled crash box increases 18.9% in specific energy absorption and decreases 21.7% in maximum peak force, which demonstrates great potential for applications in impact engineering.

Multi-condition optimization of a cross-flow fan based on the maximum entropy method

Cross-flow fans are widely used in heating, wind-curtains, and air-conditionings, as well as other ventilation systems. A single or double arc is generally used as the camber line of cross-flow fans, but this design leads to constraints in the geometry of the blade profiles. In this study, the camber line of a cross-flow fan blade was parameterized by five parameters based on the fourth-order Bezier curve. A two-dimensional computational fluid dynamics (CFD) simulation was conducted to predict the aerodynamic characteristics and the internal flow field. It is necessary in multi-condition optimization, to evaluate the relative importance of the performance parameters under different working conditions and determine their weight factors. Here, a novel maximum entropy method (MEM) was proposed to quantify of volume flow rate, because the method avoids the subjectivity in the selection of the weights. Subsequently, a multi-island genetic algorithm (MIGA), combined with numerical simulation, was used to search the global optimum in the given design space. The results indicated that the optimum combination of the structural parameters reduced the blade channel vortex in a particular location of the impeller and changed the position and size of the eccentric vortex. The volume flow rate of the optimized impeller was 4.28% higher at the minimum rotation speeds and 12.87% higher at the maximum rotation speeds.

Parameter optimisation of a carrier-based UAV drawbar based on strain fatigue analysis

ABSTRACTCarrier-based unmanned aerial aircraft (UAV) structure is subjected to severe tensile load during takeoff, especially the drawbar, which affects its fatigue performance and structural safety. However, the complex structural features pose great challenges for the engineering design. Considering this situation, a structural design, fatigue analysis, and parameters optimisation joint working platform are urgently needed to solve this problem. In this study, numerical analysis of strain fatigue is carried out based on the laboratory fatigue failure of the carrier-based aircraft drawbar. Taking the sensitivity of drawbar parameters to stress and life into account and optimum design of drawbar with fatigue life as a target using the parametric method, this study also includes cutting-edge parameters of milling cutters, structural details of the drawbar and so on. Then an experimental design is applied using the Latin hypercube sampling method, and a surrogate model based on RBF neural network is established. Lastly, a multi-island genetic algorithm is introduced for optimisation. The results show that the error between the obtained optimal solution and simulation is 0.26%, while the optimised stress level is reduced by 15.7%, and the life of the drawbar is increased by 122%.

Two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model

Efficient aerodynamic design optimization method is of great value for improving the aerodynamic performance of little UAV's airfoil. Using engineering or semi-engineering estimation method to analyze aerodynamic forces in solving aerodynamic optimization problems costs little computational time, but the accuracy cannot be guaranteed. However, CFD method ensuring high accuracy needs much more computational cost, which is unfordable for optimization. Surrogate-based optimization can reduce the number of high-fidelity analyses to increase the optimization efficiency. However, the cost of CFD analyses is still huge for aerodynamic optimization due to multiple design variables, multi-optimal and strong nonlinearities. To solve this problem, a two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model is proposed. In the first optimization stage, the solutions by early termination CFD convergence and the convergenced CFD solutions are regarded as low-and high-fidelity data respectively for building variable-fidelity model. Then, the multi-island genetic algorithm is used in the global optimization based on the built variable-fidelity model. The modeling efficiency can be greatly improved due to many cheap low-fidelity data. In the second stage optimization, the global optimum from the first optimization stage is treated as the start of the Hooke-Jeeves algorithm to search locally based on convergenced CFD computations in order to acquire better-optimum. The proposed method is utilized in optimizing the aerodynamic performance of the airfoil of little UAV, and is compared with the EGO method based on single-fidelity Kriging surrogate model. The results show that the present two-level aerodynamic optimization method consumes less time.

Control and optimisation of a dual-motor coupling drive system of pure electric vehicle based on multi-island genetic algorithm

In order to improve the endurance of pure electric vehicles, improving the energy utilisation is an effective way to increase the mileage. Energy utilisation can be effectively improved by using the new structure of dual motor coupling drive system as the power source. In the new structure, the energy utilisation is improved by eight mode switching. In this paper, CRUISE is used to build the vehicle model of pure electric vehicle, its control strategy is established by Simulink. The reliability of the model and control strategy is verified by using CRUISE and Simulink joint simulation method. The preset parameters in the control strategy are optimised by Multi Island Genetic Algorithm, making the model, parameters and control strategy more accurate so as to provide a scheme for the research of improving the energy utilisation rate of pure electric vehicles.

Control and optimisation of a dual-motor coupling drive system of pure electric vehicle based on multi-island genetic algorithm

In order to improve the endurance of pure electric vehicles, improving the energy utilisation is an effective way to increase the mileage. Energy utilisation can be effectively improved by using the new structure of dual motor coupling drive system as the power source. In the new structure, the energy utilisation is improved by eight mode switching. In this paper, CRUISE is used to build the vehicle model of pure electric vehicle, its control strategy is established by Simulink. The reliability of the model and control strategy is verified by using CRUISE and Simulink joint simulation method. The preset parameters in the control strategy are optimised by Multi Island Genetic Algorithm, making the model, parameters and control strategy more accurate so as to provide a scheme for the research of improving the energy utilisation rate of pure electric vehicles.

Thermodynamic Analysis and Optimization of Variable Effect Absorption Refrigeration System Using Multi-Island Genetic Algorithm

Thermodynamic Analysis and Optimization of Variable Effect Absorption Refrigeration System Using Multi-Island Genetic Algorithm

THERMAL PERFORMANCE IN A PIN FIN-DIMPLE/PROTRUSION DUCT WITH DIFFERENT OPTIMAL OBJECTIVES

In the present study, the optimal objective effect on the thermal performance of a rectangular duct with pin fins and dimples/protrusions is introduced. Four different optimal objectives are selected (entropy generation, entransy dissipation, enerty, and volume goodness factor). The baseline k-u turbulence model is applied to solve the governing equations. The multi-island genetic algorithm (MIGA) is used to find the optimal solution. The results indicate that the thermal performance is sensitive to the optimal objective. By using the dimple/protrusion configuration, the Nusselt number is increased remarkably in the pin-finned duct. Using the volume goodness factor as an optimal objective, the Nusselt number and friction factor reach their highest values due to the greatest strength of the horseshoe vortex. An optimal objective of enerty produces a minimal increase in the Nusselt number. A relatively low friction factor is found in the cases that use entransy dissipation and enerty as optimal objectives. (Less)

Optimization of gear shifting strategy for hybrid electric vehicles on the process of braking

Abstract A new optimization method for gear shifting on single shaft hybrid electric vehicles is proposed to improve the braking performance. Three parameters (vehicle acceleration, motor torque and weight of vehicle) in objective function are determined to have influence on the process of braking. Optimal shifting velocity is found from the given parameters by Multi-Island Genetic Algorithm (MIGA) and values of objective function are compared to decide whether vehicle shifts or not. In the Chinese typical city cycle, the strategy appears to have less sum of braking pedal aperture on the process of braking and avoid the problem of shifting frequently by fuzzy control.

Reliability-based design optimization for the lattice boom of crawler crane

The reliability-based design optimization (RBDO) problem involving discrete design variables and multi-working constraints is tackled with the consideration of the uncertainty factors in the optimal design of crawler crane’s lattice boom. In the optimization model, the uncertainties of material property, geometric parameters and loads are represented by random variables. The geometrical dimensions, performance functions’ reliability indexes and the minimum weight of lattice boom are defined respectively as the random design variables, probabilistic constraints and objective function. Integrating the reliability analysis method based on response surface method (RSM) with multi-island genetic algorithm (MIGA), an efficient reliability-based design optimization process for the lattice boom is developed under the typical constrained working conditions. Several verification cases are presented to demonstrate the effectiveness of optimization results. It is shown that the reliability-based design optimization method can not only satisfy the reliability constraints of the lattice boom, but also ensure the safety and economy of crawler crane boom as well as minimize the structure weight. The developed method is capable to be extended to the reliability-based design optimization of other large and complex construction machinery structures.

Mathematical modeling and multi-objective optimization design of eccentric telescopic rod conveyor

In order to improve the conveying performance of the eccentric telescopic rod conveyor, theoretical analysis is used to find rotation center position of the telescopic rod and the relationship among various structural parameters of the conveyor with position alteration of the eccentric axis. A mathematical model of one periodic conveying capacity of the device and the velocity and acceleration equations of the telescopic rod end are established. The multi-objective optimization analysis are done by the virtual prototyping technology and the multi-island genetic algorithm, which takes the maximum conveying volume of one periodic and minimum acceleration of the rod end as the optimization goals. The results show that after multi-objective optimization, the conveying efficiency is improved, while the impact of telescopic rod striking on materials during the conveying process is reduced and the overall performance is better. Then, an example is taken to verify the mathematical models and multi-objective optimization analysis results, and the wireless acceleration test system is used to test the telescopic rod end acceleration before and after the structural optimization. The experimental and mathematical model calculation results show good agreement before and after the structural parameters’ optimization. The study shows that the mathematical model and the multi-objective optimization results are credible. It provides a theoretical and methodological reference for the practical design and optimization of eccentric telescopic rod conveyor.

Design of a Segmented Mirror with a Global Radius of Curvature Actuation System: Contributions of Multiple Surrogates

Due to fabrication difficulties, separately-polished segmented mirrors cannot meet the co-phasing surface shape error requirements in the segmented telescope system. Applying the global radius of curvature (GRoC) actuation system for the individual segments has become an effective solution in space-based telescopes. In this paper, we designed a segmented mirror with a GRoC actuation system. The direct optimization by numerical simulations has low computational efficiency and is not easy to converge for optimizing the actuation point’s position on the segmented mirror. For this problem, three common surrogates, including polynomial response surface (PRS), radial basis function neural network (RBFNN), and kriging (KRG), were summed to propose the multiple surrogates (MS) which have the higher approximate ability. The surrogates were then optimized through the multi-island genetic algorithm (MIGA), and the segmented mirror met the design requirement. Compared with direct optimization through numerical simulations, the results show that the proposed multiple-surrogate-based optimization (MSBO) methodology saves computational cost significantly. Besides, it can be deployed to solve other complex optimization problems.