Multi-objective Design Optimization Research Articles

Multi-objective Optimization Design of Buckling-Restrained Braced Frames Based on Performance Evaluation and Risk of Repair Cost and Time

Multi-objective Optimization Design of Buckling-Restrained Braced Frames Based on Performance Evaluation and Risk of Repair Cost and Time

Coupled multi-objective optimization of water distribution network design and partitioning: a spectral graph-theory approach

ABSTRACT This paper proposes a novel method that integrates a tailored spectral graph metric and economic aspects within a coupled bi-objective optimization framework for the simultaneous pipe sizing and partitioning of water distribution networks (WDNs). Besides minimum pressure head and topological WDN connectivity, the telescopic diameter distribution criterion is applied through a new algorithm in the sizing phase to help the optimization converge towards sound engineering solutions. The application of the proposed method to a synthetic layout and a real Italian case study proves the superiority of the solutions obtained with the novel method from hydraulic and managerial/operational viewpoints in comparison with those obtained with a traditional decoupled method (separate pipe sizing and partitioning) in terms of i) compliance with service pressure constraints and ii) ease of management following WDN partitioning into district metered areas (DMAs).

Multi-objective design optimization of polymer spur gears using a hybrid approach

Polymer gears are used in applications requiring small to moderate loads to effectively transmit power and use the limited place available as possible. Various commercial standards have been provided designers with the rating criteria and acquaintance of different polymer material properties for the process of design. However, the result was unsatisfactory in terms of economy, time, and the optimality of the product. Thus, classic and stochastic algorithms have been embraced to reach the best design of polymer gears. Taking advantage of the former and latter algorithm’ methods, optimal design of gears could be attained with an increased gear life span and decreased failure modes. In this study, polyoxymethylene (POM) spur gear set has been optimized combining the mathematical model from plastic standards and hybrid optimization approach of multi-objective genetic algorithm (MOGA) and sequential quadratic programming (SQP). Weight and power loss were the objective functions. Five design variables were optimized with the satisfaction of bending and contact stresses, temperature, wear, and deformation as constraints. Solutions of the problem were formulated as Pareto optimal set. The results of multi-objective were compared with previously published single-objective optimization. The results favored multi-objective optimization (82.67%, 31.39% reduction in weight and power loss respectively) as it gave the best applicable solution fitting in real life situations. The results also went hand in hand with literature confirming the efficiency of the proposed algorithm. With the variation of operating parameters, various optimal designs could be obtained where the designers can choose the design that is suitable for a particular application.

Multi-objective optimization for generative morphological design using energy and comfort models with a practical design of new rural community in China

In recent years, the development of new rural communities has emerged as a pivotal new trend in china, aimed at enhancing energy efficiency and residential comfort in rural regions. This study introduces a multi-objective optimization design framework for the morphological design of residential area, with a focus on performance objectives such as building energy use intensity, summer thermal comfort, and winter thermal comfort. A case study of a new rural community design project in Hongnan Village, Yancheng City, was conducted. Utilizing the Rhino-Grasshopper parametric platform, this study undertook parametric modeling, performance simulation, and multi-objective optimization to receive the Pareto-optimal solutions. ParetoResults indicate that in the Pareto-optimal morphology scheme for the Hongnan Village community, open spaces are predominantly located on the upwind side with respect to the prevailing summer winds of the site. The number of duplex houses is often 28, and the building orientations are predominantly aligned at 23°, mirroring the direction of the surrounding structures. There is a significant correlation between residential unit configuration, building spacing, and energy use intensity, with correlation coefficients of 0.53 and −0.43, respectively. This study would like to offer valuable insights into the energy-efficient design of new rural communities, thereby contributing to the creation of a more sustainable and habitable rural living environment.

Dual multi-objective optimization design method for compliant guide mechanism

Dual multi-objective optimization design method for compliant guide mechanism

A Multi-objective Optimization of Window and Light Shelf Design in Office Buildings to Improve Occupants’ Thermal and Visual Comfort

In office buildings, an efficient design of windows and using light shelves as a passive design strategy significantly influence the thermal and visual comfort of occupants while enhancing the productivity and health of users. This study proposes a multi-objective optimization for the optimal design of windows and light shelves in office buildings to improve occupants' comfort. Initially, a parametric model was developed using Grasshopper parametric software. Afterward, the Honeybee energy and daylight plugin was employed for simulating thermal and visual conditions, and finally, multi-objective optimization was conducted with the Octopus plugin. This plugin can determine the best solution as a compromise decision for maximizing occupants' comfort. In this paper, an office building in Tehran has been chosen as a case study. The decision variables are window-to-wall ratio (WWR), shading control strategy, viewpoint, the transmission of glass, light shelf length, and light shelf height. The objective functions of the study are the annual average Predicted Percentage of Dissatisfied (PPD) and the annual average Discomfort Glare Probability (DGP). According to the results, the proposed optimization model leads to an 18.5–70.1% and 9.3–57.1% reduction in DGP and PPD indexes, respectively. The study findings provide practical and useful instruction for architects to select optimal specifications of windows and light shelves to develop occupants' thermal and visual comfort in office buildings.

Research on Multi-Objective Optimization Design of University Student Center in China Based on Low Energy Consumption and Thermal Comfort

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building’s exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs.

Analysis and multi-objective optimization design of sinusoidal DSEM with two different excitation modes based on Gray-Fuzzy-Taguchi method

The nonlinearity of sinusoidal doubly salient electro-magnetic machine (SDSEM) leads to the fuzzy optimization range of its design parameters. And there is a conflict problem in multiple optimization objectives. Therefore, the multi-objective optimization of SDSEM with two different excitation modes is carried out based on the Gray-Fuzzy-Taguchi method. Firstly, the proposed sinusoidal method of square wave back electromotive force (EMF) phase modulation is introduced. And the key parameters to improve the symmetry and sinusoidal degree of the back EMF waveform are pointed out. Then, the sensitivity of key parameters is analyzed and the signal-to-noise (S/N) ratio is calculated by Taguchi orthogonal test. Secondly, the S/N ratio is linearly normalized by grey correlation analysis. Then, fuzzy reasoning is used to synthesize multiple performance indexes of the motor into a multiple performance characteristics index (MPCI) value, and the optimal scheme is obtained. Finally, the optimized SDSEM with concentrated magneto-motive forces (SDSEM-CF) and SDSEM with distributed magneto-motive forces (SDSEM-DF) are simulated and compared. The SDSEM-DF prototype is developed and verified by experiments. The results show that the no-load back EMF of the prototype has a good sinusoidal degree. Based on vector control, the sinusoidal drive of DSEM is realized without increasing the control complexity, which greatly reduces the torque ripple of the flux reluctance motor.

Cooperative Multi-Objective Bayesian Design Optimization

Cooperative Multi-Objective Bayesian Design Optimization

Multi-objective optimization design for the table of a CNC machine tool based on bionic structures

To improve the static and dynamic performance of CNC machine tool tables, a multi-objective optimization design method based on bionic structures is proposed, which involves bionic design and size optimization design. Based on the bionics, four types of bionic structure tables for the original XK5032 machine tool were designed, namely bamboo cross-section bionic structure table, spider web bionic structure table, honeycomb bionic structure table, and prairie rushes bionic structure table. Static and modal analysis of the original and four types of bionic tables were conducted using finite element simulation methods validated through modal experiments. The results show that the overall mechanics of the four bionic tables have been improved compared to the original table. Using the entropy weight TOPSIS method, the prairie rushes bionic structure table was selected from four types of bionic tables. Conducting multi-objective optimization design on the prairie rushes bionic structure table, three optimization candidate schemes were obtained. The entropy-weighted TOPSIS method was used again to select the optimal solution from these three schemes, resulting in a 0.8% reduction in mass, an increase in first-order natural frequency by 2.8%, a decrease in maximum displacement by 3.2%, and a significant 21.6% reduction in maximum equivalent stress compared to the original table.

Design and Optimization of MEMS Resonant Pressure Sensors with Wide Range and High Sensitivity Based on BP and NSGA-II.

With the continuous progress of aerospace, military technology, and marine development, the MEMS resonance pressure sensor puts forward the requirements of not only a wide range but also high sensitivity. However, traditional resonators are hardly compatible with both. In response, we propose a new sensor structure. By arranging the resonant beam and the sensitive diaphragm vertically in space, the new structure improves the rigidity of the diaphragm without changing the thickness of the diaphragm and achieves the purpose of increasing the range without affecting the sensitivity. To find the optimal structural parameters for the sensor sensitivity and range, and to prevent the effects of modal disturbances, we propose a multi-objective optimization design scheme based on the BP and NSGA-II algorithms. The optimization of the structure parameters not only improved the sensitivity but also increased the interference frequency to solve the issue of mode interference. The optimized structure achieves a sensitivity and range of 4.23 Hz/kPa and 1-10 MPa, respectively. Its linear influence factor is 38.07, significantly higher than that of most resonant pressure sensors. The structural and algorithmic optimizations proposed in this paper provide a new method for designing resonant pressure sensors compatible with a wide range and high sensitivity.

Multi-objective optimal input design for grey-box identification modelling of ship manoeuvring motion

ABSTRACT Grey-box modelling has been widely used to predict the ship manoeuvring motion. In order to design input signals for the grey-box model of ship manoeuvring motion, a multi-objective optimal input design (MOID) method is proposed in this paper. In the proposed method, the MOID is regarded as a multi-objective optimization problem (MOP), where both of D-optimality criterion and condition number are taken as two-objective functions to improve the accuracy and robustness of identifying the grey-box model. The correlation factor is also presented as a constraint for reducing the parameter drift. The MOP is solved by the non-dominated sorting genetic algorithm-II (NSGA-II) with a constraint handling technique. The technique for order preference by similarity to an ideal solution (TOPSIS) is also adopted to screen out an optimal solution from the Pareto solutions. The effectiveness of MOID signals is validated by Monte Carlo analysis and grey-box identification of the MMG model.

Multi-objective design optimization of electromagnetic coil for effective actuation of magnetic endocapsule devices

Magnetic actuation with cylindrical electromagnetic coils is an upcoming technology due to its straightforward design and adaptability for magnetic endocapsule control in the gastrointestinal tract. This paper presents a mathematical model for characterizing the magnetic field generated by cylindrical electromagnetic coils and optimizing design parameters tailored for a typical 6.50-gram magnetic endocapsule. This paper adopted a dual-set parallel cylindrical electromagnetic coil configuration. The cylindrical electromagnetic coil design was conceptualized as a multi-objective optimization problem, which concerns geometrical parameters and copper winding usage, resolved through the amalgamation of a genetic algorithm and a Pareto optimal methodology. The optimal design produces an actuation force of 132.20 mN for a 6.50-gram magnetic endocapsule, and the results are validated with finite element simulations. The adopted and optimized system configuration has a average motion resolution of 2.7 mm and an average motion velocity of 162 mm/min.

Multi-objective optimization of parameters design based on genetic algorithm in annulus aerated dual gradient drilling

AbstractThe optimization of drilling parameters is crucial for resolving the drilling problems in low-pressure and leaky formations using the annulus aerated dual gradient drilling technology. However, the previous studies have mostly focused on engineering applications and wellbore fluid flow models, with less emphasis on parameter optimization. This paper combines the wellbore multiphase flow model with genetic algorithms for the first time, proposing a key parameter optimization method for annulus aerated dual gradient drilling based on genetic algorithms. The study investigates the impact of selection operators on the performance of genetic algorithms and compares genetic algorithms with PSO algorithm and SAA. The results indicate that the convergence and stability of genetic algorithms can be improved by enhancing the selection operators. Compared to the gas–liquid ratio parameter optimization method, the IRSGA optimization method reduces the cost coefficient by 36.46%. Through comparative analysis of different optimization methods, the IRSGA demonstrates over 95% accuracy in large-scale computations. The research findings contribute to the optimization of parameters design under low-cost conditions and are of significant importance for promoting the use of this technology to address the serious issue of lost circulation in drilling technology.

Novel design method for inward-turning inlets with non-uniform inflow

For the integrated design of the forebody and hypersonic inlet, the non-uniform inflows generated by the forebody have significant impacts on the inlet performance. Traditional inlet designed by the Internal Conical Flow C (ICFC) flowfield or Method of Characteristics (MOC) flowfield, is difficult to match non-uniform inflows, which can easily lead to reduced captured flow and total pressure recovery. Hence, this paper proposes a novel design method for inward-turning inlets to overcome these problems. The key of this method is the parent flowfiled called the Internal Conical Flow N (ICFN). A multi-objective design optimization is integrated into the design of the two-dimensional ICFN flowfield, which enables the “design-optimization” to implement simultaneously. The new ICFN flowfield makes full use of the high compression effect and avoids the existence of Mach reflection at the focal point. Complete details of the design method of ICFN flowfield and inward-turning inlet are presented. The flowfield structure of ICFN flowfield and inlet are analyzed. Numerical simulation results indicate that the new inward-turning inlet can effectively increase captured flow and total pressure recovery. This reveals that the design method gained here can be usefully applied to the design of high-performance inward-turning inlets.

Eco-Efficiency Performance for Multi-Objective Optimal Design of Carbon/Glass/Flax Fibre-Reinforced Hybrid Composites

An eco-efficiency optimisation study on unidirectional carbon/glass fibre-reinforced hybrid composites with natural fibre (i.e., flax) and without flax is presented in this paper. The mechanical performance was assessed by determining the flexural properties obtained via finite element analysis (FEA)-based simulation. Given the required flexural strength, optimal candidate designs were found using a set of design rules and regression analysis, with minimising the cost and weight being the objectives. An eco-efficiency framework was applied to determine the eco-efficient hybrid composites. Life cycle assessment was an indispensable component of the framework as it helped determine the life cycle environmental impacts and costs of the hybrid composite materials. The environmental impacts and cost values were converted to the eco-efficiency portfolios of these composites for both comparison and selection purposes. The hybrid composites using bio-based flax fibre have been found to be eco-efficient in most of the cases due to the avoidance of energy-intensive and expensive reinforcing materials. The environmental impacts of the hybrid composites using flaxes are 12 to 13% less than the ones using no flaxes and the former are 7 to 13% cheaper than the latter, making the flax-based hybrid composites eco-efficient.

Analysis of Multi-Objective Optimization Design of Interior Double Radial and Tangential Combined Magnetic Pole Permanent Magnet Drive Motor for Electric Vehicles

A new type of interior combined pole permanent magnet drive motor is proposed in this paper, which aims to improve the power and comfort of electric vehicles. In view of the complex magnetic circuit structure and rich harmonic magnetic field of the motor, the initial magnetic pole parameters of the rotor are determined by the equivalent magnetic circuit method. Then, aiming at the complex magnetic circuit and rich harmonic magnetic field, a multi-objective optimization method based on the Taguchi method and response surface method is proposed to reduce the cogging torque, high harmonic content in air gap magnetic flux density and increase the output torque. Based on the finite element analysis of the electromagnetic performance of the new type interior combined magnetic pole permanent magnet drive motor before and after optimization, it can be seen that the improved rotor structure can effectively reduce the torque ripple and increase the torque density. Finally, a prototype was developed and experiments were conducted, and experimental results verified the correctness of the proposed multi-objective optimization algorithm.

Multi-objective optimization of structural fire design

Probabilistic risk assessment can be used for determining a design with a residual risk that is As Low As Reasonably Practicable (ALARP). Existing risk-based design approaches however predominantly focus on single objectives, which can be impractical considering the need for structures to meet diverse performance criteria across various dimensions, including monetary costs, environmental costs, and structural performance measures. To address these challenges, this study proposes the use of a multi-objective optimization (MOO) approach within a risk-based framework. Within the MOO framework, design goals for sustainability and resilience are incorporated together with safety objectives. The MOO approach is developed here for the risk-based design of structures exposed to fire. A multi-objective problem is formulated for a reinforced concrete slab in a multi-family dwelling by identifying design parameters, objectives, and constraints. Taking environmental costs into account has little effect on the optimum design obtained through MOO for the case study, except in cases where CO2 emissions are highly valued. A limited increase in investment is observed to render the structure repairable after a fire incident, highlighting the importance of factoring in post-fire reparability in structural fire design.

Research on the Optimal Design Approach of the Surface Texture for Journal Bearings

Aiming to improve the comprehensive performance of the journal bearing system, this paper presents a multi-objective adaptive scale texture optimization design approach. A mixed lubrication model for the textured journal bearing system is established by considering the effects of cavitation and roughness. The geometrical parameters of the textures were co-optimized using a multi-objective grey wolf optimizer to obtain the optimal texture schemes that are suitable for different operating conditions. Through this approach, the influences of different texture schemes under transient operating conditions can be investigated. According to the results, it was found that different texture schemes result in different friction reduction effects. Proper surface texture is beneficial in increasing the minimum oil film thickness and reducing the possibility of asperity contact. The adaptive scale texture exhibits strong adaptability and achieves significant hydrodynamic effects. Therefore, the developed approach provides valuable insights for the optimization design of journal bearing systems.

Multiobjective design optimization of reinforced concrete beam coupled with market practice based constructability function using simulated annealing

Multiobjective design optimization of reinforced concrete beam coupled with market practice based constructability function using simulated annealing