Multi-objective Genetic Optimization Research Articles

Multi-objective rapid optimization method for a variable-stroke valve train device

Raising the maximum valve lift in two-stroke drive mode is the key to increasing the power output of the variable-stroke engine, so the limit design of the variable-stroke valve train device is critical. The multi-objective genetic optimization algorithm NSGA-2 was coupled with the single-freedom dynamics model of the variable-stroke valve train device. A multi-objective rapid optimization program for the valve train device was constructed in Matlab. Two optimization methods explored the maximum valve lift limit in two-stroke drive mode. The first was to optimize the rocker structure, and the second was to optimize the cam position and the rocker structure together. The results show that the single-freedom dynamics model has sufficient computational accuracy. The collaborative optimization results of the cam position-rocker arm structure are more optimal. Under the same maximum lift of the valve, compared to the results of individual optimization of the rocker arm structure, the maximum contact force is reduced by up to 285 N. The maximum contact stress is reduced by up to 63 MPa through collaborative optimization. Compared with the original case, the maximum increase rate of the maximum valve lift obtained through optimizing the rocker structure is 12.45%, while that obtained through collaborative optimization is 12.85%.

Multi-flow channel proton exchange membrane fuel cell mass transfer and performance analysis

This paper presents a study on the proton exchange membrane fuel cell (PEMFC) with a high humidity three-dimensional two-phase multi-channel model using COMSOL. The effects of gradient porosity in the gas diffusion layer (GDL) and gradient electrolyte volume fraction in the catalyst layer (CL) in the Y-direction on oxygen distribution, liquid water removal, current density surface area reaction rate (SCS), and current density magnitude of PEMFC are investigated. The results indicate that an appropriate gradient porosity in the GDL can improve the performance of PEMFC, especially in terms of oxygen distribution. Similarly, an appropriate gradient electrolyte volume fraction in the CL can enhance the current density uniformity. In addition, an improved multi-objective genetic optimization algorithm (RONSGA-II) is proposed in Chapter 4, along with an optimization method based on neural networks and RONSGA-II. As an example, the gradient porosity in the GDL in the Y-direction is optimized for current density and water removal. The optimized current density is increased by 1.22%, and water removal performance is improved by 16.67%.

Optimization of crashworthiness of CFRP thin-walled beams filled with aluminium honeycomb based on surrogate model

The CFRP thin-walled beam filled with aluminum honeycomb is a new design form in the crashworthiness design of automobile energy-absorbing component. It not only meets the requirements of strength, rigidity and lightweight under the normal load, but also ensures absorption of crash energy in a stable failure mode when a crash occurs. In this paper, under two typical conditions of axial impact and lateral impact, structural sample points are established by orthogonal experiment; a highly feasible surrogate model is constructed based on the radial basis function; the initial load peak Fmax and specific energy absorption (SEA) are taken as the main criteria to evaluate the energy absorption performance of the structure; the multi-objective genetic optimization algorithm NSGA-II is used to optimize the energy absorption design of CFRP thin-wall beams filled with aluminum honeycomb. The structure and material parameters of the minibus frame are optimized according to the crash condition to improve the energy absorption performance of the minibus in frontal and side crashes, and on this basis, an effective method to improve the crashworthiness of minibuses is explored.

Multi-objective optimization of fin shape in a cylindrical encapsulated phase change material for thermal energy storage applications

This study presents a novel fin design for a latent heat storage unit that improves its thermal performance through a methodology that integrates numerical simulation, the response surface method, and a multi-objective genetic optimization algorithm. The stored latent energy per mass (Em) and mean power (Pt) are chosen as the objectives for the optimization process. The fin parameters are optimized using the NSGA-II (non-dominated sorting genetic algorithm) to achieve the objectives of maximizing both the stored latent energy per mass (Em) and mean power (Pt). The proposed design is evaluated through simulations and the results are analyzed to determine the optimal fin configuration and the effect of the fin design parameters on the stored energy and power of the unit. The results show that the implementation of full-fin in the latent heat storage unit leads to a 7.2% reduction in total stored energy, but a 34% increase in power in comparison to the finless case. However, the use of an optimization approach results in a substantial increase in power output while maintaining minimal compromise to the stored latent energy. In particular, the optimal fin configuration, Design B, is recommended when power and stored energy are given equal importance, as it results in a 90.1% increase in power and a negligible 4.3% decrease in stored energy per mass.

Optimizing the Planning of Airport Airside Expansion Projects to Minimize Air Traffic Disruptions and Construction Cost

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects.

Novel Partitioned Stator Flux-Switching Permanent Magnet Linear Machine: Design, Analysis, and Optimization

Over the past few decades, flux-switching permanent magnet (FSPM) machines have gained more attention. A novel flux-switching permanent magnet linear machine with a partitioned stator (FSPMLM-PS), which has the advantages of high thrust force density and high cost efficiency for short-stroke applications, is presented and analyzed in this article. Firstly, the twelve mover slots and seven stator poles (12s/7p) structure of FSPMLM-PS is introduced, and the fundamental principle of operation is investigated. The partitioned stator helps with the reduction in iron losses and the overall cost of the proposed FSPMLM-PS. One of the frequent issues in linear machines is the end effect, which is compensated for by setting assistant teeth at both ends of the mover. The proposed machine’s main design specifications are globally optimized through a multiobjective genetic optimization algorithm using JMAG software ver. 16.1, although the volumes of the PM and the magnetic and electric loadings are kept the same. The peak-to-peak flux linkage, thrust force, thrust ripples, and detent force are improved by 26.98%, 27.98%, 22.03%, and 68.33%, respectively, after optimization. The comparison results show that the proposed FSPMLM-PS is preferable to the conventional FSPMLM given in the literature. Under the same PM volume, the proposed machine provides 27.95% higher thrust force density.

Research on performance optimization method of human-machine physical interaction system considering exoskeleton wearing comfort

In order to improve the wearing comfort and bearing effectiveness of the exoskeleton, based on the prototype and working mechanism analysis of a relaxation wearable system for knee exoskeleton robot, the static optimization synthesis and its method are studied. Firstly, based on the construction of the virtual prototype model of the system, a comprehensive wearable comfort evaluation index considering the factors such as stress, deformation and the proportion of stress nodes was constructed. Secondly, based on the static simulation and evaluation index of system virtual prototype, multi-objective genetic optimization and local optimization synthesis of armor layer topology were carried out. Finally, the model reconstruction simulation data confirmed that the system had good wearing comfort. Our study provides a theoretical basis for the bearing performance and prototype construction of the subsequent wearable system.

A New Optimized IPMSM for EVs With Reduced Magnet Loss for Over-Modulation Operation

In the traction motor drives of electric vehicle applications, the over-modulation strategy is usually implemented to enhance the output torque capability of the interior permanent magnet synchronous motor (IPMSM), so that the dc input voltage of the inverters can be fully utilized by operating the inverter up to the six-step mode. However, it will inevitably introduce rich harmonic components into phase current, leading to the worsening issue of magnet eddy-current loss (MECL). Therefore, a new hybrid magnet rotor structure for fractional-slot concentrated-winding IPMSM with optimum design for magnet loss reduction is employed with the vital improvement measures concluded as follows: first, high-resistivity ferrite magnets are introduced in the rotor flux paths of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -axis and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -axis, respectively, to form a hybrid magnets rotor structure together with Nd–Fe–B magnets; second, a rotor structure is proposed by employing a V-type placement of Nd–Fe–B magnets in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -axis path with the angle of V-type as one of the critical optimization parameters. An optimized design is finally obtained by employing the multi-objective genetic algorithm optimizer in JMAG finite element analysis tools, which is tested to show that the MECL produced in the over-modulation operation can be significantly suppressed by the optimized design.

Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics

ABSTRACT Even though metal may be effectively shaped by a variety of other manufacturing techniques, machining continues to play a significant role in industries. Turning is a conventional chip-forming operation that removes undesirable or surplus material from a cylindrical workpiece. The major objective of process optimization in turning operation research is focusing on the development of statistical modeling and optimization techniques for boosting production rate, lowering costs, and reducing product rejection. The current work aims to improve the CNC turning process of Al 7075 using coated carbide inserts. This investigation uses grey relational analysis, desirability function analysis, Multi-objective Genetic Algorithm (MOGA) and Multi-objective Genetic Particle Swarm Optimization (MOPSO) to optimize factors to minimize responses like surface roughness and cutting force. Comparative evaluation of results of optimum input parameter sets results in close agreement, both in traditional optimization and metaheuristic-based optimization. In particular, MOGA is found to be more efficient to solve this stated multi-criterion decision-making problem as compared to other methods.

Application of a multi-objective genetic optimization algorithm for the thermodynamic optimization of reed valves

Application of a multi-objective genetic optimization algorithm for the thermodynamic optimization of reed valves

Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery

The ORC technology is subject to manifold sources of uncertainty that can have a severe impact on the thermodynamic and economic efficiency of plant components, particularly when the system is operated at off-design conditions. In this contribution we focus on the development of ORC turbines with stable performance under uncertainty: a novel multi-fidelity robust design optimization (RDO) strategy is used to design the first nozzle of an ORC turbine for high temperature waste-heat recovery. For this kind of application, the turbine inlet and outlet conditions may vary randomly over a large range. The RDO strategy combines parsimonious uncertainty quantification techniques with a multi-objective genetic algorithm optimizer based on surrogate models. The multi-fidelity approach allows to estimate with high accuracy and with a low computational cost the statistical moments of the probability distribution function of the quantity of interest, which here is the entropy generation within the cascade. To improve the accuracy of the surrogate model coupled with the optimizer, the multi-objective expected improvement criterion is adopted. The optimization converges to an efficient optimum solution, ensuring improved and stable performance over the whole considered range of uncertain operating conditions and with a computational cost that is significantly lower than other RDO approaches proposed in literature.

Underwater polarization imaging based on two-layer multi-index optimization

&lt;sec&gt;Underwater imaging is of great significance in exploring seabed resource , monitoring marine environment, implementing underwater rescue and military reconnaissance, etc. by providing clear vison. Among various underwater imaging techniques, the polarization imaging is considered to be an effective way to improve the quality of underwater imaging. It can realize underwater image restoration by using the difference in polarization characteristic between the target light and backscattered light. A classical underwater active polarization imaging method was presented by Treibitz [Treibitz T, Schechner Y Y &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1109/TPAMI.2008.85"&gt;2009 &lt;i&gt;IEEE Trans. Pattern Anal. Mach. Intell.&lt;/i&gt; &lt;b&gt;31&lt;/b&gt; 385&lt;/ext-link&gt;], in which the degrees of linear polarization (DoLPs) of target light and backscattered light are used to recover clear image. A variety of improved methods have been derived from this, but most of them require background areas and human-computer interaction. Then, a new underwater active polarization imaging method without prior knowledge was presented by Zhao [Zhao Y, He W, Ren H, Li Y, Fu Y &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1016/j.optlaseng.2021.106777"&gt;2022 &lt;i&gt;Opt. Lasers Eng.&lt;/i&gt; &lt;b&gt;148&lt;/b&gt; 106777&lt;/ext-link&gt;], in which the DoLPs of target light and backscattered light can be automatically obtained without background region. However, sometimes the above two parameters are very close and thus introduce a lot of noise into the restored images, for this method takes only the contrast into account.&lt;/sec&gt;&lt;sec&gt;In this work, an underwater active polarization imaging method based on two-layer multi-index optimization is proposed. First, the mutual information and contrast are taken as the upper objective functions, and the Pareto optimal solution set is obtained by the multi-objective genetic optimization algorithm. Second, the information entropy is taken as the lower objective function to obtain the optimal parameters from this optimal solution set. Based on the optimal parameters, the restored images are obtained. According to the difference between the DoLPs of target light and backscattered light, these restored images are further improved by the digital image processing method.&lt;/sec&gt;&lt;sec&gt;The experimental results indicate that our method can not only enhance image details effectively but also balance various evaluation indexes of the imaging quality to obtain high-quality restored images. The proposed algorithm is suitable for underwater targets with low and high DoLPs, with or without background regions.&lt;/sec&gt;

Multi-Objective Optimization of Base-Isolated Tanks with Supplemental Linear Viscous Dampers

Base isolation of liquid storage tanks has proven to be an efficient seismic protection measure, leading to a drastic reduction of a superstructure’s distress. However, many such tanks are located near seismic tectonic faults, which generate strong pulse-like ground motions that can impose excessive displacement demands on the isolators. For this reason, viscous dampers are incorporated into the isolation system to avoid overconservative isolators design. To optimize the seismic performance of hybrid isolation systems consisting of single friction pendulum bearings and linear viscous dampers, two novel multi-objective optimization approaches are proposed in the current study. Furthermore, suitable constraint functions and design variables are selected, considering the most critical parameters of the hybrid isolation system. The multi-objective genetic algorithm optimizer is used for the solution of both problems. The results are presented in the typical form of Pareto and certain optimal design solutions are carefully chosen and compared in terms of isolators fragility curves and tank accelerations. The main aim is to optimize the critical design parameters by achieving a reasonable balance among contradicting objectives. The tank industry can substantially benefit from this study, as a more cost-efficient design of hybrid base-isolation can be attained for large-scale tanks.

Data-driven prediction and optimization of residential building performance in Singapore considering the impact of climate change

Research on the performance of tropical residential buildings requires careful consideration of the effects of the changing weather conditions. Based on machine learning and multi-objective genetic optimization algorithms, this study proposes an integrated framework for predicting and optimizing the performance of residential buildings in Singapore. The framework was used for two Singapore residential building types, the point block building and the slab block building, to examine their optimal performance in terms of daylight performance, energy efficiency, and thermal comfort under future climate conditions in the short term (2021–2040), middle term (2041–2060), and long term (2061–2100). The results show that the XGBoost algorithm and transfer learning method perform well in building performance prediction, specifically in the source domain (point block buildings) with R2 = 0.95 and in the target domain (slab block building) with R2 = 0.87. The general trend in the optimal design parameters for residential buildings is to enhance the thermal insulation of building walls and envelopes to better cope with future warming climate conditions, while providing adequate indoor lighting. This study contributes to the field of climate-resilient residential buildings in Singapore. This is closely linked to the Singapore government's quest for sustainable development and to enhance the well-being of its residents.

Process design and multi-objective optimization of efficient heat utilization distillation based on the influence of pressure and entrainer flow on separation performance

A novel strategy for optimizing extractive pressing-swing distillation for separating pressure-sensitive azeotropic mixtures comprising ethyl acetate/ethanol/butanone was proposed. Dimethyl sulfoxide was selected as the optimal entrainer based on its effect on the vapor–liquid equilibria conditions of the azeotropic mixture. A multi-objective genetic optimization algorithm was used to optimize the economic and gas emissions performance of the process. Pressure and entrainer flow have different effects on the separation of the azeotropes. The operating pressure and entrainer flow were comprehensively optimized as variables. To fully exploit the residual heat, vapor recompression and heat integration were used to improve the process. The improved processes were evaluated based on economic, gas emissions, and exergy analyses. Compared with the extractive pressure-swing distillation process, the total annual cost of the vapor recompression process increased by 5.13%, whereas the gas emission and operation costs decreased by 20.94% and 10.06%. The total annual cost and gas emissions of extractive pressing-swing distillation with heat integration decreased by 17.95% and 26.96%, whilst the exergy efficiency increased by 8.16%. The energy consumption of different control structures in the system subjected to disturbances was compared. The CS2 structure has lower energy consumption than CS1. The energy consumption of the T3 column was greatly reduced.

Evolutionary optimization of thermo-physical properties of MWCNT-Fe[formula omitted]O[formula omitted]/water hybrid nanofluid using least-squares support vector regression-based models

Decisions on optimizing design and operating parameters are challenging when using hybrid nanofluids (HNFs). A procedure is proposed and implemented for predicting and optimizing the thermal conductivity and dynamic viscosity of MWCNT-Fe3O4/water HNF. The procedure involves using precise least-squares support vector regression (LSSVR) models, multi-objective genetic optimization of thermal properties, and automated selection of optimal design conditions. Tuned parameters are the volume fractions of nanoparticles and the operating temperature. The cross-validated and carefully optimized LSSVR models for thermal conductivity and dynamic viscosity showed excellent performances, with testing mean percentage errors of −0.246 and −0.103%, and relative root mean square errors of 1.325 and 2.165%, respectively. By assigning equal importance to the two response parameters, an HNF with volume fractions of 0.302 (Fe3O4) and 0.183% (MWCNT), operating at 55 °C, is highlighted as the optimal design within the considered range of tuned parameters. This corresponds to a particle mixing proportion (PMP) of 0.605, and the corresponding values of thermal conductivity and dynamic viscosity are 0.803 W/m K and 0.625 mPa s, respectively.

Modeling and solving the parallel mixed-flow remanufacturing disassembly line balancing problem for multi-variety products

The types and numbers of components in end-of-life (EOL) products are often uncertain during remanufacturing, leading to low disassembly efficiencies for traditional remanufacturing disassembly lines. To address this problem, a parallel mixed-flow workstation layout was designed, and a novel parallel mixed remanufacturing disassembly line balancing optimization method for multi-variety products was proposed. A mixed-flow product disassembly task hierarchical assignment matrix was constructed to perform disassembly task allocations for similar components. Furthermore, a parallel mixed-flow remanufacturing disassembly line balancing (PMRDLB) optimization model was developed with the optimization objectives of minimizing the number of workstations, the disassembly line balancing rate, and the remanufacturing value indexes of the components. Furthermore, the multi-objective non-dominated genetic optimization method (NSGA-III) was improved, in which a chromosome construction method, based on the parallel mixed-flow disassembly task allocation matrix, was proposed to conduct mapping between the chromosomes and the PMRDLB model. In addition, non-dominated solution sorting was performed based on a Pareto hierarchy, which increased the searching rate of the algorithm during optimization. Finally, a case study verified the effectiveness and feasibility of the proposed method.

Modeling, Optimization, and Analysis of Segmented Stator Flux Switching Linear Hybrid Excited Machine for Electric Power Train

This article proposes a segmented and double stator flux switching linear hybrid excited machine (SDSFSLHEM). To reduce the machine’s iron loss and overall cost, the proposed SDSFSLHEM utilizes a segmented stator. At both ends of the mover, assistant teeth compensate for the unbalance in the three-phase flux, which is a common problem in linear machines. Field excitation (FE) is used, which adds flux regulation capability to the proposed machine and allows it to operate at a wide range of speeds. A magnetic equivalent model (MEM) is used to find the best coil combination and no-load flux linkage, to reduce computational time. The leading design parameters of the machine are globally optimized by multiobjective genetic global optimization, while keeping the slot area, electric, and magnetic loadings constant. The optimization improved peak-to-peak flux linkage by 12%, thrust force by 34.7%, thrust ripples by 17.97%, and detent force by 10.07%. Compared to the flux switching permanent magnet (PM) machine proposed in the literature, the proposed machine reduces the volume of the PM by 39.18% and provides 31.2% higher thrust force and thrust density.

Hybrid Multi-Objective Optimization Approach With Pareto Local Search for Collaborative Truck-Drone Routing Problems Considering Flexible Time Windows

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.

Optimizing The Transportation of Petroleum Products in A Possible Multi-Level Supply Chain

The goal of many supply chain optimization problems is to minimize the costs of the entire supply chain network. However, since environmental protection is one of the main concerns, the green supply chain network has been seriously considered as a solution to this concern in order to minimize its effects on nature. This article refers to the modeling and solution of a green supply chain network for the transportation of petroleum products in order to reduce the annual costs, considering the environmental effects. In this article, the cost elements of the supply chain such as the transportation costs of each petroleum product, operating costs, the cost of purchasing crude oil products and the fixed costs of building oil centers as well as the components of the environmental effects of the supply chain such as the amount of gas emissions and volatile organic particles produced by transportation options in the supply chain. considered green. Considering these two components (cost and environmental impact), we have proposed a multi-objective supply chain model. In this facility model, oil centers have limited capacity and at each level of the chain, there are several types of transportation options with different costs. To solve the problem, we have used two multi-objective particle swarm optimization algorithms and genetic multi-objective optimization algorithm with non-dominant sorting II with a priority-based decoding to encode the chromosome. Finally, we have used TOPSIS method to compare these two algorithms.