Genetic Algorithm-based Optimization Research Articles

Effect of Microfluidization on Deteriorative Enzymes, Sugars, Chlorophyll, and Color of Sugarcane Juice

Various enzymes are responsible for the quality and color degradation of sugarcane juice and need to be inactivated through suitable interventions. In this study, microfluidization of sugarcane juice was attempted over a pressure range of 50–200 MPa with 1–7 processing cycles to remediate the problem. The impact of microfluidization on the activity of polyphenol oxidase (PPO), peroxidase (POD), and sucrose neutral invertase (SNI) along with the sugar, color, and chlorophyll content of sugarcane juice was assessed. Results showed that microfluidization reduced the PPO activity by 39.4–64.7% and POD activity by 16.4–75.0%. However, SNI activity was restricted only at specific processing conditions. Reducing sugar increased with microfluidization but was found comparable to control, while total sugars decreased by 11–54%. Chlorophyll content increased up to 100 MPa pressure and showed no degradation at higher pressures. Juice color was relatively stable up to a pressure of 150 MPa with distinct high intensity of greenness. Fourier transform infrared (FTIR) spectra of the processed juice revealed that amide II band in the structure of PPO and POD was more susceptible to pressure treatment, while SNI can be more effectively reduced at 50 and 100 MPa. Genetic algorithm-based optimization showed 159 MPa pressure/1 cycle as suitable processing condition for maintaining the quality of sugarcane juice. Although microfluidization significantly reduced the activity of deteriorative enzymes in sugarcane juice while maintaining its color characteristics, complete inactivation could not be achieved which could hinder long-term preservation. In this regard, microfluidization-assisted technologies could be a future breakthrough for juice preservation.

Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving

Demand-controlled ventilation (DCV) is widely used for the energy-efficient provision of indoor air quality. However, existing DCV requires a continuously variable airflow rate, and is not applicable to the constant-air-volume mechanical ventilation (generally in buildings with room air conditioners and free-running buildings). This study proposes a novel DCV for constant-air-volume mechanical ventilation. To achieve high ventilation efficiency, the proposed DCV operates the constant-air-volume mechanical ventilation continuously and intermittently at full-load/quasi-full-load and partial-load conditions respectively, which is controlled by the upper and lower concentration limits of indoor airborne pollutants (indicated by indoor CO2 concentration). The larger upper and lower concentration limits are preferred for energy saving, but could deteriorate indoor air quality, and cause durability problems due to frequent on-off operations. A genetic algorithm-based optimization is developed to determine the upper and lower concentration limits to maximize energy efficiency while satisfying the demand on indoor air quality and avoiding excessively frequent on-off operations. Case studies verify that the ventilation performance of the proposed DCV optimization is more sensitive to the lower concentration limit than the upper concentration limit. The conventional methods (the continuous ventilation and the intermittent ventilation) for the constant-air-volume mechanical ventilation risk low energy efficiency and deteriorated durability. The proposed DCV optimization improves energy efficiency by up to 88% while meeting demanded indoor air quality and durability.

Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.

The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

Genetic Algorithm-based Optimization of Deep Neural Network Ensemble

Personal identification is the task of authenticating a person using individual biological features. Deep neural networks (DNNs) have demonstrated an impressive performance in this field. Since no general algorithm is available for the design of network structures and the parameters adopted in DNNs for every application problem, DNNs should be determined according to the programmers’ experiments and know-how. For a new application task, it is very time-consuming for non-experts to design network structure, hyperparameters and an ensemble of base models adequately and effectively. In this paper, we present a genetic algorithm (GA)-based approach to construct network structures, tune their hyperparameters, and generate base models for the ensemble algorithm. The ensemble is constructed from base models with different network structures according to the voting ensemble algorithm. Our original personal identification dataset is employed as the numerical example to illustrate the performance of the proposed method. The results show that the prediction accuracy of the ensemble model is better than that of the base models and that the prediction of walking behavior toward the Kinect at 90 degrees and 225 degrees is more difficult than other walking behaviors.

Comprehensive optimization of electronic and optical properties of polar III-nitride laser

A detailed investigation is performed to obtain optimized optical performance of polar III-nitride single quantum well (SQW) semiconductor lasers by band engineering through symmetric and asymmetric tuning of device parameters. Electronic, optical, and threshold characteristics are calculated to analyze the effects of band engineering on device performance. Here, the InN-In0.25Ga0.75 N SQW structure is chosen as a case study. This work shows a significant improvement in optical properties from higher wavefunction overlap integral obtained from band modification due to the tuning which also provides higher momentum matrix elements, spontaneous emission rate, and optical gain, as well. The maximum TE-polarized optical gain for the symmetric structure is found as around 6291 cm−1 for 6 A well width and 6 A barrier width at 710 nm, while for asymmetric laser structure, a maximum TE-polarized optical gain of around 6225 cm−1 at around 800 nm is observed for 6 A well width with asymmetric barrier width of 6 A and 8 A. The optimization is carried out over 500–1600 nm wavelength range to obtain the optimum structure for maximum optical performance at different wavelengths. A time-efficient genetic algorithm-based optimization is also performed that provides the same optimization results in reduced time.

Acid hydrolysis-based sugarcane bagasse biorefining for levulinic acid production: Dynamic mechanistic modeling under varying operating conditions

The study on the lignocellulosic material conversion into bio-based platform chemicals, such as levulinic acid (LA), is one of the most promising routes to promote the development of advanced biorefineries. In this work, a dynamic mechanistic model is developed to simulate the LA production from lignocellulosic material. A wide operating range is used to estimate the parameters of the reaction kinetics. Because multi-parameter estimation problem is complex, a genetic algorithm-based optimization procedure is used to determine the optimum parameters values. Measurements are obtained for various reaction times (0 - 45 min) temperatures (150 – 200 °C) and acid concentration of 7.0 % w/v H2SO4. The calculated reaction rates for the state variables, concentrations of LA, glucose, 5-hydroxymethylfurfural and humins are used to construct the dynamic mechanistic model. The prediction of measured state variables was particularly accurate, as determined by the root mean square error (RMSE) and correlation coefficient (R2). Therefore, a satisfactory agreement between experimental LA yield of 57.2 mol% and computed LA yield of 56.4 mol% was achieved (at 200 °C, 7.0 % w/v H2SO4, 45 min). The proposed methodology drives the systematic development of an industrially reliable dynamic mechanistic model for LA production from sugarcane bagasse as a means to increase the LA yields in the biorefinery.

MOGATS: a multi-objective genetic algorithm-based task scheduling for heterogeneous embedded systems

Multi-objective optimisation is an unavoidable requirement in different steps of embedded systems design, including task mapping and scheduling. In this paper, a new multi-objective genetic algorithm-based task mapping and scheduling (abbreviated as MOGATS) is presented for heterogeneous embedded system design. In MOGATS, the architecture of the hardware platform and the set of tasks in the form of a task graph are assumed to be given as the inputs. Task mapping and scheduling problems are modelled as a genetic algorithm-based optimisation approach. In summary, our task scheduling tool is the first multi-objective task scheduling in the design stage of embedded systems to help the designer to figure out which set of scheduling would provide their desired outcome. Additionally, in comparison to EGA-TS, the state-of-art task scheduling algorithm, in terms of speedup and SLR, we have gain 27.8 and 28.6 immediate improvements respectfully.

Modeling and optimization of drilling operation of lead-free brass alloys considering various cutting tool geometries and copper content

Machinability, stable productivity, and quality of the components are crucial factors in manufacturing industry. In industrial applications, when cutting parameters and conditions are determined considering experiences gained by engineers which does not resolve the whole problems faced with in case cutting tools and their geometries are involved. Thus, it requires further knowledge that is built through systematic and scientific research. This study presents the results of an experimental investigation of a drilling process of hot forged lead-free brass alloys by using a form cutting tool. Experimental data on cutting forces, dimensional accuracy, and surface quality of the holes is presented considering the tools with different geometries, feed rates and rotational speeds on hot forged lead-free brass alloys with various copper content. Artificial neural networks modelling, and genetic algorithm-based optimization methods have been used to predict and optimize the machining process and related responses. The computational results demonstrated that the developed model and optimization procedure can notably improve the efficiency of the process by reducing the production cost with the obtained optimum machining conditions.

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

A Modified Asymmetric Bouc–Wen Model-Based Decoupling Control of an XY Piezoactuated Compliant Platform with Coupled Hysteresis Characteristics

Due to the inherent hysteresis characteristics of the smart material, the positioning accuracy of the piezo-driven manipulator was always decreased. Especially in multi-degrees-of-freedom (MDOFs) compliant manipulators driven by multismart actuators, the cross-coupled hysteresis among MDOFs decreased the positioning accuracy of such compliant platforms significantly. In this paper, the hysteresis feature identification and coupled hysteresis compensation of a piezo-driven XY manipulator was investigated. To establish the hysteresis characteristics of the piezo-driven manipulator, a modified Bouc–Wen model has been proposed, and a Genetic Algorithm-based Particle Swarm Optimization (GA-PSO) was adopted to recognize the parameters of the model. To improve the output performances of the manipulator, the decoupling controller of the XY micromanipulator was designed, and the driven voltages were modified using the estimated coupling displacements. The experiments validated that the modified Bouc–Wen model featured the ability to present the hysteresis process effectively, and the maximum prediction errors are 0.19 μm and 0.16 μm in the two directions separately. The coupled hysteresis displacement before and after implementing the decoupling controller in the X-direction was reduced from 0.56 μm to 0.15 μm, and the coupling effect is reduced by 73.2%, while in the Y-direction, the coupling effect was also decreased by 72.9%.

Equivalent Circuit Design Method for Wideband Nonmagnetic Absorbers at Low Microwave Frequencies

An accurate equivalent circuit (EC) design approach for wideband nonmagnetic absorbers operating at the low microwave frequency (1–10 GHz) is presented. Following the impedance matching approach, this communication introduces an EC model based on the simulated data and synthetic asymptotes for single- and double-layer frequency-selective surface (FSS)-based nonmagnetic absorbers. Two simple and commonly used resistive FSSs, i.e., square patch and single square loop, are considered in this communication. Compared to the full-wave simulations, the proposed EC model shows more than 95% accuracy. By employing the proposed model and genetic algorithm-based optimization, several designs of broadband absorbers are demonstrated. The presented single- and double-layer FSSs show 126% and 161% fractional bandwidth, respectively, with the total thickness close to the Rozanov limit. The results confirm that the proposed method is a simple and efficient way of designing thin wideband absorbers using single- or double-layer FSS configurations.

The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications

Thermal energy storage can be employed for air conditioning system load management, i.e., load shifting and leveling, to serve the peak electricity demand for the air-conditioning system with high capacity utilization. Ice and phase change material-based thermal energy storage systems were modeled and optimized for air-conditioning applications. The mathematical modeling involved energy, exergy, environmental and economic analysis of both the systems at full and partial operating modes. The system is then optimized for a commercial building to give maximized exergy efficiency and minimized total annual investment and operating cost over five different system temperatures as decision variables. The full operating mode strategy resulted in a higher exergy efficiency for both systems, whereas partial operating mode proved to be a more economical operating strategy. The multi-objective genetic algorithm-based optimization is carried out with two different refrigerants (R134a and R717) in the vapor compression refrigeration cycle of the systems. A single system design point is then selected using a multi-criteria decision-making technique. The electricity consumption while utilizing the thermal energy storage based system was lower as compared to the conventional system for air-conditioning applications. The two modeled systems are compared based on storage media, operating strategies, and the refrigerant used.

Machine learning modeling and genetic algorithm-based optimization of a novel pilot-scale thermosyphon-assisted falling film distillation unit

Engaged in the global trend towards more energy-efficient and sustainable technologies, our research team has developed a falling film distillation apparatus with an innovative heat supply through a two-phase closed thermosyphon. In order to evaluate the performance of this energy-intensified distillation process, a supervised machine learning (ML) predictive model based on artificial neural networks is implemented for the separation of the ethanol–water binary mixture. The feed temperature, the evaporator temperature, and the feed flow rate are the three input variables of the model, whereas the ethanol mass fraction in the distillate, the distillate mass flow rate, the recovery factor, and the separation factor are the four performance indicators evaluated. The feed-forward ML has been trained, tested, and validated using a total dataset of 64 experimental runs carried out in the pilot-scale unit, covering a wide operating range of the input variables. Despite the high non-linearity, the ML approach was capable of modeling this new process accurately. The optimal topology of the ML model was achieved with a network arrangement of 10 neurons within 1 hidden layer (3:10:4), with a correlation coefficient (R) greater than 0.95 for all data. The predictive abilities of the ML model were harnessed to investigate the individual and synergistic interaction effects of the operating variables by plotting generalization graphs. Finally, the optimal operating conditions were evaluated by the genetic algorithm (GA) technique, being the feed temperature of 90.6 °C, the evaporator temperature of 109.6 °C, and the feed flow rate of 26.3 L/h, the process operating values that led the maximum of the four performance indicators, simultaneously. Under these operating conditions, a distillate mass flow rate of 4.9 kg/h, with 50.6%wt ethanol-enriched, a recovery factor of 84.9%, and a separation factor of 57.4 have been achieved, showing the high-performance feasibility of the pilot-scale unit.

Optimal Control Strategies for Switchable Roof Insulation Systems Applied to US Residential Buildings

AbstractSwitchable building envelope systems, including passive and active systems, have recently seen an increase interest in the literature. Unlike static insulation, switchable insulation systems (SISs) have the ability to adjust the thermal properties of envelope elements. Advanced control strategies for SISs are evaluated in this analysis using genetic algorithm-based optimization techniques. In particular, this study investigates the potential heating and cooling energy savings for deploying optimal controls specific to SIS technologies when applied to residential roofs located in representative US climates. Moreover, energy use and peak demand savings obtained by optimal controls are compared with those obtained from the 2-step rule-based controls. Overall, the analysis results indicate that the maximum monthly additional savings obtained by optimal controls can reach up to 32% compared with 2-step rule sets when an annual analysis is conducted for a residential building located in Golden, CO.

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization.

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pdn (n = 1-21) clusters supported on CeO2(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

Optimization of arrangement of conducting fins and insulated obstacles inside a cavity: the couple of numerical solutions and genetic algorithm methods

The present work optimized the arrangement of the conducting fins and insulated obstacles in the cavity by coupling numerical simulation and genetic algorithm-based optimization methods. The height and width of the fins and the distance between them, as well as the temperature of the hot surface, were considered as the design variables. The design of the experiment and the response surface method were used in the optimization process. The objective function was defined to maximize the heat transfer coefficient on the hot wall. Based on the results, in the case of installing the conducting fins on the hot wall, the distance between the fins was the most effective geometrical parameter that exhibited the highest effect on heat transfer. By using the insulated obstacles installed on the hot wall, the width of the insulated obstacles has the highest effect on the heat transfer procedure. According to results, in the case of installing the conducting fins on the hot wall, the heat transfer coefficient exhibited the highest sensitivity to the wall temperature. Moreover, at the optimal point, the genetic algorithm predicts the optimum point thermal performance with an error of less than 2%, and the heat transfer in the cavity with insulated obstacles was 1.63 times more than that of the cavity with the conducting fin.

Genetic algorithm-based joint Spectral-Energy efficiency optimisation for 5G heterogeneous network

ABSTRACT Carrier Aggregation (CA) technology, introduced in 3GPP release 10, aims at increasing Spectral Efficiency (SE) only, which may lead to huge undesirable circuit power consumption. Hence, not only SE, but also Energy Efficiency (EE), particularly the tradeoff between both the parameters, is crucial in the design of CA systems. In this work, a novel approach, using Genetic Algorithm (GA) is proposed to solve the SE-EE tradeoff problem for a 5 G heterogeneous network with intra-band contiguous Carrier Aggregation (CA). Complete physical layer simulation is performed for generating the CA waveform, with parameters set in accordance with practical deployment scenarios, as specified in 3GPP TS 38.104. Carrier aggregated waveform is generated in 5 G New Radio (NR) frequency band FR1, using frequency bands in India which are currently not allocated for licenced usage. From simulations, it is observed that the proposed technique ensures that neither SE nor EE is seriously degraded for improving the other, for any given value of transmit power. The proposed technique, which performs Multi Objective Optimisation (MOO) using SPEA-2 GA outperforms Single Objective Optimisation (SOO) methods providing a maximum of 37% improvement in SE and 43% improvement in EE.

A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models.

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Analysis of Torque Ripple of a Spoke-Type Interior Permanent Magnet Machine

This paper investigates the theoretical reasons behind the low torque ripple of a fractional-slot concentrated-winding (FSCW) spoke-type interior permanent-magnet (IPM) machine obtained via a genetic algorithm-based optimization. To better understand the torque performance of the IPMM, this paper uses the frozen permeability method to segregate the overall torque into four components—magnet torque, reluctance torque, cogging torque, and the torque caused by cross-magnetization. Equations and detailed procedures of the torque separation method are discussed in the paper. Analysis of the separated torque components reveals that the counteraction between ripples of different torques leads to the low torque ripple. At high-load conditions, the magnetic saturation alters the torque ripples caused by cross-magnetization to offset ripples of other components resulting in minimization of the overall torque ripple. A detailed parametric analysis of the geometric parameters was carried out to understand their effect in producing minimum torque ripple in the optimized design. In the end, a prototype was built and used for the verification of the torque separation method and the analytical findings based on it.