Genetic Optimization Research Articles

Optimized Art Design Model With Statistical Model with Digital Media

Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media technology. The statistical technique comprises the estimation of the features in the art design model. Through the integration of WGO with the statistical model features related to the art design with the incorporation of digital media are evaluated. The statistical features in the art design are observed as the digital information such as geometric, GLCM and HUE are the essential features in the integrated WGO with statistical techniques. The estimated features are applied over the deep learning model with the LSTM network for the automated classification of art design that uses digital media for improvement. Simulation results demonstrated that the proposed WGO integrated statistical model achieves the HUE value ranges from 0 -360 which is effective for art design modeling. Also, the proposed model achieves a significant classification rate of 0.98 accuracy with a loss value of 0.2 which is ~9% less loss than the conventional techniques.

0-1 Knapsack Problem Solving using Genetic Optimization Algorithm

0-1 Knapsack Problem Solving using Genetic Optimization Algorithm

Prediction method of particle size qualification rate of high-pressure roller mill based on end-edge-cloud synergy

The pass rate of granules is an essential indicator during the high-pressure grinding process, as it accurately reflects the processing quality. Currently, the pass rate of granules is detected primarily based on manual experience judgments or offline inspections. Hence, this article presents a methodology for predicting the pass rate of granularity via an optimized support vector regression approach improved through genetic algorithms. Initially, a time-delay analysis method based on the particle swarm optimization algorithm is applied to mitigate the effects of time delays between the granularity pass rate and other data, thus aligning the dataset on a temporal scale. Subsequently, the feature data were selected using the maximum information coefficient analysis technique, which identified the most significant variables for inclusion in the training and testing sets of the predictive model. Predictions are then made using a support vector machine model that has been enhanced via genetic algorithm optimization. Furthermore, an online prediction model has been established, enabling real-time forecasting of the granularity pass rate and online model updates through root mean square propagation gradient descent optimization algorithm. This method leverages end-edge-cloud collaboration to provide a smart detection mechanism for the throughput rate of particles in high-pressure grinding mills. Experimental results demonstrate that, compared to traditional time-delay analysis, the improved time-delay analysis method proposed in this study is more effective and accurate. Simultaneously, the ɛ-GASVR granularity pass-rate prediction model proposed in this article achieved an R2 of 0.89.

Genetically synthesized supergain broadband wire-bundle antenna

High-gain antennas are essential hardware devices, powering numerous daily applications, including distant point-to-point communications, safety radars, and many others. While a common approach to elevate gain is to enlarge an antenna aperture, highly resonant subwavelength structures can potentially grant high gain performances. The Chu-Harrington limit is a standard criterion to assess electrically small structures and those surpassing it are called superdirective. Supergain is obtained in a case when internal losses are mitigated, and an antenna is matched to radiation, though typically in a very narrow frequency band. Here we develop a concept of a spectrally overlapping resonant cascading, where tailored multipole hierarchy grants both high gain and sufficient operational bandwidth. Our architecture is based on a near-field coupled wire bundle. Genetic optimization, constraining both gain and bandwidth, is applied on a 24-dimensional space and predicts 8.81 dBi realized gain within a half-wavelength in a cube volume. The experimental gain is 8.22 dBi with 13% fractional bandwidth. The developed approach can be applied across other frequency bands, where miniaturization of wireless devices is highly demanded.

MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

Energy, exergy, environmental (3E) analyses and multi-objective optimization of vortex tube coupled with transcritical refrigeration cycle

The present study deals with the thermodynamic investigation of vortex tube coupled with trans-critical vapour compression refrigeration cycle (TVTC), followed by environmental analysis and multi-objective optimization. In this research, effect of various operating and design parameters is studied on the performance of TVTC. Furthermore, a comparison is made between the outcomes of TVTC and simple trans-critical vapour compression refrigeration cycle (TVCR). Results show that the optimum gascooler pressure for TVTC is observed to be lower than that of TVCR. Also, the cooling capacity and COP of TVTC are observed to be 10.1 % to 21.1 % and 2.3 % to 11.3 %, respectively, greater than those of TVCR. Moreover, the exergetic efficiency of TVTC is 2.3 % to 11.3 % higher than that of TVCR for the investigated range of evaporator and gascooler exit temperatures. The environmental penalty cost (per unit cooling capacity) of TVTC is 3.5 % to 12.2 % lower than that of TVCR. Furthermore, the coefficient of structural bond is calculated in order to choose the most sensitive parameters for system's performance. Additionally, genetic algorithm-based multi-objective optimization has been performed, with the evaporator temperature serving as the primary determining factor in establishing the optimal solution. This finding can guide the development of TVTC-based systems for a wide range of applications.

Algorithm optimization of architectural form through sequential landscape evaluation

Designing architectural and urban spaces based on continuous human spatial experience throughout a sequence may lead to architectural and urban forms not bound to conventional concepts. These forms would be made possible through the combination of a quantitative sequential landscape evaluation and morphological optimization utilizing algorithmic processes. In this study, we introduce a method for evaluating sequential landscapes, in which the configuration of buildings, trees, etc., in view changes as pedestrians move along a particular path, using a genetic algorithm optimization of architectural forms to bring them closer to the designer’s ideals. The method’s validity was tested using two case studies, assuming the design of a sequential landscape in an actual city and park. The results present a more efficient, objective, and reproducible method for designing sequences in buildings, cities, and landscapes, compared to traditional methods.

Comparative analysis of steel bar structures of solar canopies composed of hyperbolic paraboloid units using genetic algorithms

Building integrated photovoltaics (BIPV) understood as an integral building component, is becoming more and more popular. In turn, the use of BIPV on curvilinear roofs is an interdisciplinary issue covering geometry, environmental engineering, architecture and construction. The article presents an approach to designing efficient steel bar structures of the modular solar canopies using genetic algorithms and their evaluation. Shaping effective forms of solar roofs was subordinated to the idea “form follows environment” in the original way as the roof form, and thus the shape of the structure depended on the amount of solar energy absorbed by the roof surface. The structures designed on a regular polygon plan were created from three, four, five, or six hyperbolic paraboloid (HP) modules arranged radially. Algorithmic definitions of their forms made it possible to obtain many variants of canopies with the adopted preliminary criteria, as well as to conduct genetic optimization, which helped to obtain the best results. For the best solutions in terms of geometrical forms, structural optimization was carried out due to the structures’ minimum masses. As a comparative criterion of structural effectiveness, a coefficient was used that specified the mass of the considered structure per square meter of the covered area. The proposed shaping method may constitute some guidelines for rational shaping of the steel bar structures with radially arranged modules, in which their effectiveness can be established already at the initial design stage. However, the best optimization results, that is, efficient and aesthetic canopies, can be ecological solutions to be used in practice.

Introducing an optimization approach for enthalpy exchangers for residential ventilation

Enthalpy exchangers recovering sensible and latent heat have an increasing share of use in residential ventilation. In this study, we introduce an optimization approach for membrane-based enthalpy exchanger (MEE). Therefore, we develop a system model, combine it with a genetic optimization algorithm and optimize the design regarding energy demand and comfort. The methodology proves promising as its application to a residential building system indicates less permeable (2.68 × 10−10 mol m−1 s−1 Pa–1) and thicker membranes (75 µm) are used for moderate climates compared to cold and dry climates (2.91 × 10−10 mol m−1 s−1 Pa–1, 20 µm).

Research on Multimodal College English Teaching Model Based on Genetic Algorithm

Analyzing College English texts is essential for quantitatively evaluating their grammar, phrases, and words to enhance their use in writing, conversation, and other contexts. The precise and clear use of College English words, phrases, and sentences is essential to knowledge-based and foundational learning systems. Text data analytics run into problems with data amount, data diversity, data integration and interoperability. It is challenging to accomplish human-computer interaction in spoken College English communication and to assist students with corrections using the conventional methodology of teaching College English. Therefore, this paper proposed the Genetic Algorithm-based intelligent English course optimization system (GA-IECOS) to handle the scheduling above issue of college English classes and optimize college English teaching courses. The results demonstrate that the conventional BP neural network's local scheduling optimization issue may be resolved using the multidirectional mutation genetic BP neural network method. Subsequently, a mix of formative and summative assessments will be used to establish a couple of groups to evaluate the effectiveness using a control population and a trial group of a GA-IECOS for English language classes using a multidirectional mutation genetic algorithm and an optimization neural network. The results demonstrate that the GA-IECOS algorithm is more effective in the classroom and may greatly improve students' English performance

Hybrid genetic optimization for quantum feature map design

Kernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for mapping non-linearly separable input data into a higher dimensional (feature) space, thus allowing the data to be linearly separable in feature space. Previous work has shown that quantum feature map design can be automated for a given dataset using NSGA-II, a genetic algorithm, while both minimizing circuit size and maximizing classification accuracy. However, the evaluation of the accuracy achieved by a candidate feature map is costly. In this work, we demonstrate the suitability of kernel-target alignment as a substitute for accuracy in genetic algorithm-based quantum feature map design. Kernel-target alignment is faster to evaluate than accuracy and does not require some data points to be reserved for its evaluation. To further accelerate the evaluation of genetic fitness, we provide a method to approximate kernel-target alignment. To improve kernel-target alignment and root mean squared error, the final trainable parameters of the generated circuits are further trained using COBYLA to determine whether a hybrid approach applying conventional circuit parameter training can easily complement the genetic structure optimization approach. A total of eight new approaches are compared to the original across nine varied binary classification problems from the UCI machine learning repository, showing that kernel-target alignment and its approximation produce feature map circuits enabling comparable accuracy to the previous work but with larger margins on training data (in excess of 20% larger) that improve further with circuit parameter training.

GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules.

A detailed understanding of ligand-protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand-protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand's pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand's conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.

Simultaneous design optimisation methodology for floating offshore wind turbine substructure and feedback-based control strategy

This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Методика балістичного планування місій низькоорбітального сервісного обслугову-вання з малою постійною тягою рушійних систем

The current stage of space exploration is characterized by an increased interest in the development, deployment, and operation of low-orbit satellite constellations (LOSC) for Earth and near-Earth space remote sensing for military and civilian purposes and for global and regional satellite communications. Reusable space launch vehicles have significantly reduced the orbital injection cost. As a result, satellite operators are developing and deploying large-scale LOSCs of various orbital structures with a large number of spacecraft. According to current estimates, more than 70% of all the operating satellites operate in low-Earth orbits (LEOs) at altitudes between 160 km and 2,000 km. Since LEO satellites are generally much cheaper than satellites in geostationary orbits, the possibility of their on-orbit servicing (OOS) has not been the focus of research. However, the use of LEO OOS has prospects for growth. Techniques for ballistic planning of LEO OOS missions have been and are being developed. The disadvantages of approximate techniques include the use of simplified flight dynamics models. Most of the existing exact techniques are based on the use of full mathematical models of flight dynamics and the shooting method to solve the boundary value problem of an orbit transfer. Using the shooting method requires a sufficiently accurate initial guess, which is difficult to determine. To obtain a second approximation, use is mainly made of optimization methods, which do not always find a global minimum. In this regard, there is a need to develop new techniques that would be free from the above disadvantages. The goal of the article is to develop a ballistic planning technique for low-orbit servicing missions with low constant thrust propulsion systems. The technique includes the identification of LEO areas promising for OOS, a mathematical model of the dynamics of perturbed OOS orbit transfers in modified equinoctial orbital elements, and a solution algorithm for the boundary value problem of determining the control parameters of perturbed OOS low-orbit transfers. The problem is solved using methods of statistical analysis, flight dynamics, shooting, genetic optimization, and mathematical simulation. The novelty lies in the identification of LEO areas promising for OOS and the development of a mathematical model of orbit transfer dynamics in modified equinoctial orbital elements and a solution algorithm for determining the control parameters of perturbed OOS low-orbit transfers. The results of the work may be used in the justification and planning of LEO OOS missions and the formulation of requirements for LEO OSS mission propulsion systems.

Model-predictive space heating control for energy flexibility – a case study using a long short-term memory neural network surrogate model and a genetic optimization algorithm

This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.

A genetic algorithm‐based virtual machine scheduling algorithm for energy‐efficient resource management in cloud computing

SummaryTo address the unbalanced resource load of a virtual machine cluster, the author proposes an energy‐saving virtual machine scheduling algorithm based on resource management cloud computing technology. This article analyzes the current cloud computing and virtual machine scheduling research in the cloud computing environment. It discusses the concept, characteristics, classification, application scenarios, and key cloud computing technologies. A genetic algorithm is used to solve the problem of high energy consumption in the data center. The test results show that in the same original configuration scheme, the migration times based on the greedy algorithm adopted by GA2ND are about 1000, and the migration times of GA1ST are between 200 and 500. The GA2ND migration scheme requires fewer virtual machines. In the result analysis, the experiments compare the proposed algorithms—DVFS, IMC, GA1ST, and GA2ND—with a focus on energy consumption and virtual machine migration. Notably, DVFS serves as a reference for energy efficiency, IMC represents the proposed algorithm without genetic optimization, GA1ST denotes the genetic algorithm under a heterogeneous model, and GA2ND signifies the enhanced genetic algorithm introduced in this article. The comparison aims to assess the energy efficiency and virtual machine migration performance of each algorithm in the context of a simulated cloud computing environment. Therefore, the algorithm proposed in this article can effectively reduce energy consumption and avoid frequent migration of virtual machines.

Trajectory Optimization for Adaptive Deformed Wheels to Overcome Steps Using an Improved Hybrid Genetic Algorithm and an Adaptive Particle Swarm Optimization

Two-wheeled mobile robots with deformed wheels face low stability when climbing steps, and their success rate in overcoming steps is affected by the trajectory. To address these challenges, we propose an improved hybrid genetic and adaptive particle swarm optimization (HGAPSO) algorithm to optimize the deformed wheels’ trajectory for overcoming steps. HGAPSO optimizes the maximum and minimum values of the inertial weight and learning factors of the adaptive particle swarm algorithm utilizing the region-wide search capabilities of the genetic algorithm, which substantially improves the convergence speed and adaptability. Furthermore, the analysis of the motion of the deformed wheel overcoming the steps and the examination of the potential interference during the operation are used to construct a wheel’s center-of-mass route based on fifth-order Bézier curves. Comparative simulation experiments of the trajectories optimized using different optimization algorithms under the same working conditions are designed to demonstrate the efficacy of the proposed HGAPSO algorithm in optimizing the trajectory of the deformed wheel overcoming the step. Simulation experiments were conducted using the HGAPSO algorithm to optimize the trajectories of deformation wheels for overcoming steps of various sizes. These optimized trajectories were then compared to unoptimized ones. The results showed that the HGAPSO-optimized trajectories significantly improved the success rate and stability of the mobile robot in overcoming steps.

Approach for sizing by optimization of an electrical drive considering a multiphysics and multidynamics behaviour

PurposeThis paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.Design/methodology/approachThe electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.FindingsThis paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.Research limitations/implicationsThis paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.Practical implicationsThe methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.Originality/valueThe approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.