Micro-genetic Algorithm Research Articles

Utilizing MicroGenetic Algorithm for Optimal Design of Permanent-Magnet-Assisted WFSM for Traction Machines

With increasing worries about the environment, there is a rising focus on saving energy in various industries. In the e-mobility industry of electric motors, permanent magnet synchronous motors (PMSMs) are widely utilized for saving energy due to their high-efficiency motor technologies. However, challenges like environmental degradation from rare earth development and difficulties in controlling magnetic field fluctuations persist. To address these issues, active research focuses on the wound field synchronous motor (WFSM), known for its ability to regulate field current efficiently across various speeds and operating conditions. Nevertheless, compared with other synchronous motors, the WFSM tends to exhibit relatively lower efficiency and torque density. Because the WFSM involves winding both the rotor and the stator, it results in increased copper and iron losses. In this article, a model that enhances torque density by inserting permanent magnets (PMs) into the rotor of the basic WFSM is proposed. This proposed model bolsters the d axis magnetic flux, thereby enhancing the motor’s overall performance while addressing environmental concerns related to rare-earth materials and potentially reducing manufacturing costs when compared with those of the PMSM. The research methodology involves a comprehensive sensitivity analysis to identify key design variables, followed by sampling using optimal Latin hypercube design (OLHD). A surrogate model is then constructed using the kriging interpolation technique, and the optimization process employs a micro-genetic algorithm (MGA) to derive the optimal model configuration. The algorithm was performed to minimize the use of PMs when the same torque as that of the basic WFSM is present, and to reduce torque ripple. Error assessment is conducted through comparisons with finite element method (FEM) simulations. The optimized permanent-magnet-assisted WFSM (PMa-WFSM) model improved efficiency by 1.08% when it was the same size as the basic WFSM, and the torque ripple decreased by 5.43%. The proposed PMa-WFSM derived from this article is expected to be suitable for use in the e-mobility industry as a replacement for PMSM.

An artificial intelligence-based optimization framework for the optimal composition and thermomechanical processing schedule for specialized micro-alloyed multiphase steels

An artificial intelligence-based heuristic approach is presented to optimize the chemical composition and the thermomechanical processing schedule to obtain specialized micro-alloyed multiphase steels with desired mechanical properties, at minimal manufacturing cost. The optimization framework uses a modified form of genetic algorithm, called the micro-genetic algorithm (μGA), that uses a penalty-based cost function formulation operating on a multi-dimensional search space spanning 15 alloying elements, an average cooling temperature, an austenitizing temperature and eight time–temperature points from the cooling profiles of multiphase steels. With superior search speed and convergence rates to the traditional genetic algorithm, μGA uses a neural network-based reduced-order model to predict hardness. Additional correlation equations are used to determine the corresponding tensile strength and elongation. Microstructural analysis was performed using neurocomputing techniques to further validate the accuracy of the algorithm. The entire computational framework was validated using data from the literature, establishing its utility in steel design.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Optimization of hybrid platform for high-tech equipment and building vibration mitigation using evolutionary algorithms

In this paper, an ideal fuzzy logic control (FLC) algorithm is designed for micro vibration control of a hybrid platform which is placed on a second floor of the building subjected to traffic-induced ground vibrations. A hybrid platform is one that can be utilized to safeguard high precision equipment from vibrations caused by traffic, machinery, and natural hazards like earthquakes while it is situated with in the building structure. A fuzzy system is employed to obtain the appropriate control force of active actuators, and the fuzzy system is then optimized using four evolutionary algorithms (EAs), which are population-based metaheuristic algorithms that were inspired by the nature. A microgenetic algorithm (µ-GA), particle swarm optimization (PSO), differential evolutionary algorithm (DEA), and cuckoo search algorithm (CSA) are used to optimize the FLC knowledge base and rule base. To illustrate the capabilities of the proposed method in traffic-induced vibration control of a hybrid platform, a three-degree-of freedom (3-DOF) structure along with the platform is taken into account. Finally, utilizing the Bolt, Beranek and Newman (BBN)-vibration criteria and the absolute velocity of hybrid platform as the objective, the efficiency of the hybrid platform microvibration control is evaluated. The simulation results obtained from these novel EAs indicated that the actively controlled platform is efficacious in reducing microvibration of high-tech equipment. Based on the results, the PSO algorithm optimized fuzzy controller outperforms the other methods in terms of velocity levels of second floor of the building and hybrid platform.

Optimization of fire sprinkler design for uniform water flux distribution using a micro-genetic algorithm

The fire sprinkler head design was optimized using a micro-Genetic Algorithm to produce a uniform distribution of droplet size and water flux. The optimization process consisted of two steps. The first step optimized the injection pattern at a radial distance of 0.05 m from the center of the sprinkler head to produce a uniform water flux. In the second step, the sprinkler head design which produces the optimized injection pattern was derived. The optimized injection pattern was a sinusoid pattern with a large amplitude and wide width. Also, the water is targeted upward in the tine stream. The large amplitude of the injection pattern helped to achieve a uniform distribution of droplet sizes by delivering large droplets directly to the region near the sprinkler head at the floor level. Also, the water flux in the radial direction showed a more uniform distribution in the optimized design. The final optimized design shows that the section angle should be larger, the tines should be more protruding, and the slots should be more concave to improve the water flux distribution. Caution must be exercised in applying these results to conditions different from those used in this study.

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.

Developing a public EV charging infrastructure in a DSO's perspective: Hosting capacity assessment and grid reinforcements on a real case study

The widespread increase of electric vehicles sales forces Distribution System Operators to evaluate and improve the capability of the distribution system to host distributed charging infrastructures. This paper suggests a procedure to identify grid reinforcements to increase the capability of the system as well as to evaluate the current hosting capacity. The developed procedure is based on a micro-genetic algorithm and its solution identifies the optimal grid reinforcements to be planned on a Medium Voltage network in order to increase the number of charging stations that can be connected. The procedure requires the model of the network, the yearly load profiles of the secondary substations and the algorithm output takes into account an envisioned budget to be spent, avoiding network violations and an iniquitous distribution of the charging stations. The procedure was firstly validated considering a 70 nodes test network. Furthermore, the real case study of the distribution system of the city of Terni was assessed focusing on one of its main subnetwork with 471 nodes; the procedure was exploited to assess the current hosting capacity and to estimate that on average 0.01 M€ has to be spent on grid reinforcements in order to further increase hosting capacity.

Retrofit optimization of building systems for future climates using an urban physics model

To guide building system retrofits for reducing building’s operational carbon footprint, a micro-genetic optimization algorithm has been applied. The algorithm is integrated into the Vertical City Weather Generator (VCWG) urban physics model. The model is applied to low-rise residential houses of Toronto, a cold Canadian city, for annual estimations and reductions of electricity/gas consumption and retrofit cost from 2020 to 2100, every decade, under two Representative Concentration Pathway (RCP) climate change scenarios. The building system configuration utilizes a solar thermal collector, photovoltaic collector, wind turbine, building thermal energy storage, and heat pump. Fifteen building variables have been optimized. Compared to a base building, the proposed retrofitted system reduces the electricity consumption by up to 61.71 [%] and the gas consumption by up to 82.67 [%]. The annualized retrofit cost for a 20-year time horizon is about 10-15 thousand Dollars. Some optimized variables are sensitive to the climate change scenario over a long time horizon until 2100, which relate to the thermal energy storage system, phase change material, solar thermal collectors (and the associated working fluid flow rates), solar heat gain coefficient, and roof albedo. Other variables, relating to the ventilation/infiltration rates, building envelop thermal resistance, glazing ratio, wind turbine size, and photovoltaic collectors, do not show such sensitivity. The optimization process is computationally fast, and the solution obtained provides evidence for successful building system retrofits toward energy and cost savings for the climate of Toronto.

Multi-Objective Optimization of High-Power Microwave Sources Based on Multi-Criteria Decision-Making and Multi-Objective Micro-Genetic Algorithm

Multi-Objective Optimization of High-Power Microwave Sources Based on Multi-Criteria Decision-Making and Multi-Objective Micro-Genetic Algorithm

On the Characterization of Viscoelastic Parameters of Polymeric Pipes for Transient Flow Analysis

The behaviour of polymeric pipes in transient flows has been proved to be viscoelastic. Generalized Kelvin–Voigt (GKV) models perform very well when simulating the experimental pressure. However, in the literature, no general indications on the evaluation of the model parameters are given. In the present study, the calibration of GKV model parameters is carried out using a micro-genetic algorithm for experimental tests of transient flows in polymeric pipes taken from the literature. The results confirm that the higher the number of Kelvin–Voigt elements, the better the reproduction of experimental tests, but it is difficult to search for general rules for parameter characterization. Assuming a Kelvin–Voigt (KV) model with a single element, it is shown that the retardation time is related to the oscillation period that can be obtained from the elastic modulus and from easily evaluable pipe characteristics. A simple procedure is then proposed for the characterization of the viscoelastic parameters that can be used by manufacturers and technicians. Considering the limits of such a model, the procedure has to be considered as a first step for the characterization of the viscoelastic parameters of more complex models.

Gas Release and Solution as Possible Mechanism of Oscillation Damping in Water Hammer Flow

Water hammer flow is examined, putting into evidence that unsteady friction cannot be fully responsible for observed oscillation damping. The measured piezometric head oscillations of water hammer flow experimental tests carried out for very long time (about 70 periods) are presented and compared with the numerical results of a quasi-two-dimensional (2D) flow model. The hypothesis is made that the energy dissipation could be partially due to the process of gas release and solution. An equation for the balance of gas mass is taken into account, already successfully used to improve the comparison between numerical and experimental head oscillations for transient gaseous cavitation. The models are based on a particular implementation of the method of characteristics (MOC-Z). The calibration of the empirical parameters of the models is carried out with a micro-genetic algorithm (micro-GA). The better performance of the proposed model is quantified with comparison of the mean absolute errors for three experimental tests at different Reynolds numbers, ranging from 5300 to 15,400. The corresponding ratios between the mean absolute errors of the models with and without gas release range between 47.3% and 17.7%. It is also shown that different turbulence models give very similar results. The results have some relevance in water hammer research, because sometimes dissipation that is not due to unsteady friction is attributed to it. However, the hypothesized mechanism has to be deepened and validated with further studies.

Hazard-informed optimization of seismic networks for earthquake early warning—the case of the Lower Rhine Embayment (western Germany)

Earthquake early warning (EEW) systems can serve as a viable solution to protect specific hazard‐prone targets (major cities or critical infrastructure) against harmful seismic events. Using the example of the Lower Rhine Embayment (western Germany), we present a novel approach for evaluating and optimizing seismic networks for EEW purposes. The network optimization is applied to simulated earthquake scenarios from a hazard-compatible stochastic catalog, which represents a realization of the seismicity in the target area over a given period of time. We propose a densification of the existing network in the area by pre-selecting a number of potential sites with an optimal station configuration using a microgenetic algorithm, minimizing an appropriate cost function associated with the network layout. We show that the new decentralized network significantly improves the warning time and the accuracy of the warnings for levels of shaking for threshold levels of at least 0.02 g. Although the accuracy of the alerts for other cities outside the target area varies depending on their location, we demonstrate that the updated network layout will also improve the warning times for neighboring cities.

Application of micro-genetic algorithms to optimize piston bowl geometries for heavy-duty engines running on diesel and 1-Octanol fuels

This study presents the developments in piston bowl geometry and renewable fuels for a heavy-duty diesel engine to approach the CO2 emission targets set by the European Union. 3D computational fluid dynamics were performed within the framework of a micro-genetic algorithm which was used as a global optimization method to evolve piston bowl geometries. Furthermore, this paper discusses using 1-Octanol as a renewable substitute for fossil diesel fuel. 1-Octanol’s oxygenated nature and straight-chain structure demonstrate high potential in improving combustion and engine efficiency.The results of the numerical investigations show that the optimized piston bowl geometry from a micro-genetic algorithm for diesel fuel improved the indicated specific fuel consumption and reduced indicated specific CO2 emissions by up to ∼5% and ∼2% respectively. This is achieved due to improved air utilization with the optimized piston bowl geometry. The optimized piston bowl geometry results in increased air utilization in the squish region and reduced loss in spray movement due to bowl design. 1-Octanol as fuel demonstrates up to ∼1.8% increase in indicated thermal efficiency and ∼2% reduction in indicated specific CO2 emissions when compared to diesel fuel for base piston bowl shape. The oxygen content and lower C / H ratio in 1-Octanol fuel lead to faster and improved combustion, which helps increase indicated thermal efficiency compared to diesel fuel. The diesel-optimized piston bowls for 1-Octanol show minor improvement in fuel consumption, air utilization, and emissions.

Intelligence in Finance and Economics for Predicting High-Frequency Data

Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time.

Memetic micro-genetic algorithms for cancer data classification

Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.

Preliminary Evaluation of Crustal Medium Parameters in Western China

The establishment of attenuation relation of ground motion is always an important research content in engineering earthquake field. In seismology-based ground motion attenuation relationship and ground motion simulation using the stochastic method, regional parameters are necessary. These parameters involve stress drop Δσ, two segmentation points R1 and R2 of the geometric spreading item, Q0 and η of the inelastic attenuation item and the near-surface high-frequency decay parameter κ0, which reflect the regional crustal medium and site characteristics and have little relation with the earthquake size. In this paper, we take Sichuan, Yunnan and Xinjiang regions in western China as examples and we inverse the parameters for each region by micro-genetic algorithm. The inversion ranges of crustal media parameters in the three regions were determined respectively. Finally, we get the inversion results of every parameters and find that the parameters of the three regions have a great difference.

MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing

MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing

Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes – snow cover, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0 %, 6.2 %, and 3.3 % in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2 % and 31.0 %, respectively.

A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisioning. In the cloud, a variety of metaheuristic algorithms are helpful for process allocation. Large-scale datacenters utilise a lot of electricity, which has an influence on the environment and the economy. Using a Micro-Genetic Algorithm, a stable combined process workload allocation method with Cat Swarm Optimization (MG-CSO) is introduced by addressing pre-convergence problems and optimal resource management. For optimal computing efficiency, the resources are dynamically consolidated and clustered. Additionally, the MG-CSO Algorithm produces positive outcomes when compared to other well-known algorithms, such as the Genetic Algorithm (GA) and the Bat Algorithm (BA).This research's focus is to reduce cost, time, Service Level Agreement(SLA), energy usage, and afford good Quality of Service(QoS). Our final scientific results were achieved with 91%.

Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency

Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to provide the best trade-offs between two conflicting objectives: maximization of the effectiveness of the cooling towers and minimization of the overall power requirement of the refrigeration system. The optimization process respects the constraints to guarantee the correct and safe operation of the equipment when the evolved solution is implemented. In this work, we apply three evolutionary multi-objective algorithms: Non-dominated Sorting Genetic Algorithm (NSGA-II), Micro-Genetic Algorithm (Micro-GA) and Strength Pareto Evolutionary Algorithm (SPEA2). The results obtained are analyzed under different scenarios and models of the cooling system’s equipment, allowing for the selection of the best algorithm and best equipment’s model to achieve energy efficiency of the studied refrigeration system.