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Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms. &nbsp, Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.

Convolutional neural network (CNN) has been widely applied to image recognition, especially Handwritten English Recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.

Bio-n-butanol is widely recognized as a renewable alternative biomass fuel, it is gradually used in internal combustion engines for scientific research to alleviate the energy crisis and environmental pollution. However, due to the difference of physical and chemical properties between n-butanol and gasoline, if n-butanol is directly applied to gasoline engines, it will certainly cause changes in engine performance. Therefore, it is of great significance to explore the optimal energy conservation potential of n-butanol application in gasoline engines. Driven by this fact, an experiment was conducted in a high speed, spark ignition (SI) engine with n-butanol blended ratio of 0% and 35% by volume to gasoline, and simulation model of GT-Power coupling with MATLAB/Simulink is built and calibrated based on the tested data. The synergistic optimization of multiple operating variables with the corresponding genetic algorithms (GA) optimization methodologies is carried out to reveal the optimal energy conservation potential of engine fueled with butanol–gasoline blends. Results show that, when the torque remains the same with respect to that of the original engine, the optimized brake specific energy consumption (BSEC) at the corresponding engine speed under full load are significantly lower than the original BSEC, the average improvement in percentage of BSEC is approximately 7.11%. By adopting the method of multi-objective genetic algorithm (MOGA), the power and economy of the engine fueled with butanol–gasoline blends can be balanced simultaneously. The engine operating parameters can be chosen that the BSEC may be small while the engine is optimally powered. Besides, it is found that the pumping loss is affected by the combined effect of intake timing and exhaust timing. The optimization procedure can be applied to calibrate the universal characteristics of engines, and the findings obtained from this study can provide guidance for better application of alternative energy in traditional thermal machines.

In the current work, a novel method is presented for generating rules for data classification as well as for regression problems. The proposed method generates simple rules in a high-level programming language with the help of grammatical evolution. The method does not depend on any prior knowledge of the dataset; the memory it requires for its execution is constant regardless of the objective problem, and it can be used to detect any hidden dependencies between the features of the input problem as well. The proposed method was tested on a extensive range of problems from the relevant literature, and comparative results against other machine learning techniques are presented in this manuscript.

A power system’s operating cost needs to be minimized by satisfying varying load demand while taking into account the prevailing constraints in a multiple unit electrical power system. In this study, by using genetic algorithms (GA), a short-term thermal unit commitment problem was solved and an economical generating unit schedule was made with the solution obtained. Taking into account the negative effects of emissions due to the use of fossil fuels, emission costs were added to the objective function together with fuel and start-up costs. The GA chromosome structure was formed by binary encoding, new generations were selected by roulette wheel selection mechanism and single point crossover was applied. The representation, formulation and the simulation results of the problem for a 5-unit test system during the scheduling hours of the period are presented. The number and the operating hours of the generating units to be committed were determined by satisfying the prevailing constraints. During the planning period, 13360 MW of power demand was met by 755 MW of spinning reserve. Total operating cost was calculated as $430330. Of the total operating cost, 32% consists of emission costs.

The timetable of a metro system is essential for trains running safely and efficiently. For the design of a timetable, the potential power interruptions of energy supplies from the substations to the trains should be borne in mind. In case potential power interruptions occur, the backup running plan should be completed responsively to accommodate the passenger and energy demands In this study, we propose an energy supplementation strategy utilizing regenerative energy from decelerating trains in the event of power interruptions and develop a tri-objective optimization model incorporating passenger travel time, potential power interruption, and energy consumption. Particularly, for calculating regenerative energy utilization, we suggest a many-to-many energy allocation mechanism between decelerating and accelerating trains based on the real-time energy demands and supplies. A heuristic algorithm is developed to obtain a regular and cyclic timetable for minimizing passenger travel time, the potential power interruptions, and energy consumption. The suggested model and algorithm are tested based on the smart-card data collected from a bidirectional metro line in Beijing (China). The results show that the suggested approach significantly improves energy efficiency, reduces passenger waiting time, and decreases power interruption risks, compared with the currently used scheduling method.

The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with the freestream. An open-loop optimal periodic forcing is defined by the carrier frequency, the duty cycle and the phase difference between actuators as control parameters. The control laws are optimised with respect to the unperturbed TBL and to the actuation with a steady jet. The cost function includes the wall convective heat transfer rate and the cost of the actuation. The performance of the controller is assessed by infrared thermography and characterised also with particle image velocimetry measurements. The optimal controller yields a slightly asymmetric flow field. The LGAC algorithm converges to the same frequency and duty cycle for all the actuators. It is noted that such frequency is strikingly equal to the inverse of the characteristic travel time of large-scale turbulent structures advected within the near-wall region. The phase difference between multiple jet actuation has shown to be very relevant and the main driver of flow asymmetry. The results pinpoint the potential of machine learning control in unravelling unexplored controllers within the actuation space. Our study furthermore demonstrates the viability of employing sophisticated measurement techniques together with advanced algorithms in an experimental investigation., Comment: 20 pages, 13 figures

A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification.

U ovom radu opisan je razvoj računalnog agenta za igru zmija, čija pravila su objašnjena na početku rada. Agent je razvijen uz pomoć neuronskih mreža i genetskih algoritama, a arhitektura mreže i način odvijanja selekcije, križanja i mutacije opisan je u odgovarajućim poglavljima. Izrađeno je grafičko korisničko sučelje koje omogućuje igranje od strane čovjeka ili praćenje procesa treniranja agenta. Grafički su prikazani dobiveni rezultati u procesu treniranja i testiranja. This paper describes the development of a computer agent for the snake game, the rules of which are explained at the beginning of the paper. The agent is developed from neural networks and genetic algorithms, the architecture of the network and the methods of selection, crossover and mutation are described in the corresponding chapters. A graphical user interface has been created that allows playing by a human or monitoring the process of training an agent. The results obtained in the process of training and testing are shown graphically.

Copyright © 2023 The Authors. A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

In this research, a biped robot is programmed to learn to walk using genetic algorithms. The biped robot has the ability to rise after a fall. The system is made according to human genetics, where a child tries to learn to walk, takes a few steps, falls, and then stands up somehow after a fall. After this process, the system restarts and tries to walk again. The biped robot evolves after each generation, and after each generation it will have a more natural walking posture. The system is unpredictable, because the biped robot sometimes evolves faster and sometimes evolves slower, like in human genetics where some children are faster and other children are slower to walk. The biped robot is considered to have evolved enough after having a walking posture similar to that of a human and does not fall.

Genetic algorithm (GA) are based on the continuation of fitter ones’ lives considering the natural evolution. Data are coded as genes in the genetic algorithms. Optimal solutions can be achieved through the methods of crossing and mutation performed on these coded genes. Facade elements of the buildings with an architectural design in this study are independent of sustainability-related concerns, suggesting a great issue for the new buildings to be constructed in the traditional pattern. Accordingly, using the genetic algorithm method, proposals were presented for the new door and window typologies with genetic fitness for the architectural designing process of the buildings to be constructed in Suriçi Region, Diyarbakır, Turkey. Shape grammar, fractal and genetic algorithm, three generative designing systems, were used as the methods. Utilizing the genetic algorithm method, a field study was performed for the proposal of new door and window typologies with the fitness value. The field study was assessed through the plans and facade analyses regarding six Diyarbakır traditional houses with U plan type in Suriçi region of Diyarbakır. An identity card was created for the plan and facade data of the buildings and transferred to the table. Then, the door and window typologies of the exterior facade elements of each examined building were crossed within themselves with the GA method. As a result of the crossover, alternative joinery typologies with a total of 31 windows and 53 different door typologies with compatibility values were produced. Thus, the sustainability of the data of traditional joinery typologies for use in contemporary houses has been ensured. In conclusion, optimal alternative typologies were presented in regard to every chopping typology assessed with the genetic algorithm method. It is thought that this study should be a method that can be used in the production of exterior joinery typologies of contemporary houses to be built in many different cities of our country, especially in the historical texture. Thus, by using the GA method for the production of exterior joinery typologies of contemporary houses to be built in the region, different designers will be able to obtain various designs compatible with the traditional architectural texture while preserving their originality.

Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets.

To solve the problems of poor stability and low monitoring precision in the online detection of rice moisture in the drying tower, we designed an online detection device for rice moisture at the outlet of the drying tower. The structure of a tri-plate capacitor was adopted, and the electrostatic field of the tri-plate capacitor was simulated using COMSOL software. A central composite design of three factors and five levels was carried out with the thickness, spacing, and area of the plates as the influencing factors and the capacitance-specific sensitivity as the test index. This device was composed of a dynamic acquisition device and a detection system. The dynamic sampling device was found to achieve dynamic continuous sampling and static intermittent measurements of rice using a ten-shaped leaf plate structure. The hardware circuit of the inspection system with STM32F407ZGT6 as the main control chip was designed to realize stable communication between the master and slave computers. Additionally, an optimized BP neural network prediction model based on the genetic algorithm was established using the MATLAB software. Indoor static and dynamic verification tests were also carried out. The results showed that the optimal plate structure parameter combination includes a plate thickness of 1 mm, plate spacing of 100 mm, and relative area of 18,000.069 mm2 while satisfying the mechanical design and practical application needs of the device. The structure of the BP neural network was 2-90-1, the length of individual code in the genetic algorithm was 361, and the prediction model was trained 765 times to obtain a minimum MSE value of 1.9683 × 10−5, which was lower than that of the unoptimized BP neural network with an MSE of 7.1215 × 10−4. The mean relative error of the device was 1.44% under the static test and 2.103% under the dynamic test, which met the accuracy requirements for the design of the device.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.

We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.

This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future.

Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. Five-phase motors for propeller propulsion will face various complex sea conditions during actual use, and five-phase motors for electric vehicles will also face various complex road conditions and operating requirements during use. Therefore, as a propulsion motor, its speed control system must have strong robustness and anti-disturbance performance. The use of traditional PI algorithms has problems, such as poor adaptability and inability to adapt to various complex working conditions, but the use of an active disturbance rejection controller (ADRC) can effectively solve these problems. However, due to the significant coupling between the variables of induction motors and the large number of parameters in the ADRC, tuning the parameters of the ADRC is complex. Traditional empirical tuning methods can only obtain a rough range of parameter values and may have significant errors. Therefore, this paper uses ADRC based on genetic algorithm(GAADRC) to tune the parameters of the control and design an objective function based on multi-objective optimization. The parameters to be adjusted were obtained through multiple iterations. The simulation and experimental results indicate that GAADRC has lower startup overshoot, faster adjustment time, and lower load/unload speed changes compared to the empirically tuned PI controller and ADRC. Meanwhile, using a genetic algorithm for motor ADRC parameter tuning can obtain optimal control parameters while the control parameter range is completely uncertain; therefore, the method proposed in this paper has strong practical value.

A system for low-frequency noise control in small, enclosed sound fields is proposed, using compound sound sources optimized by a genetic algorithm (GA). It is the integration of the developed low-Bl driver compound sources with a GA computer program in the Python language, aiming to control the modal field. The lack of appropriate free space in small rooms is critical for positioning the secondary sound sources; therefore, the proposed system has been designed to adapt to any available position. Two quadrupole topologies of the secondary compound source are applied and examined in a room. The convergence of the algorithm to the optimal solutions is attained through parametric configuration. The spatial radiation of the compound source at a single fixed position is adapted to couple with the modal noise field and attenuate it. The experimental results indicate that the proposed system can successfully control resonances of different low frequencies down to 50Hz at multiple positions. The tonal noise attenuation reaches up to 32dB at 100Hz, confirming the applicability of the small subwoofer loudspeaker configurations for low-frequency control. This new method offers a practical and effective alternative to the typical abatement techniques that use distributed monopole sources in limited spaces.

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An $\epsilon $ -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.

The Fellini Museum is an exhibition hall dedicated to the Rimini film director Federico Fellini, included by the Ministry of Culture of Italy among the great national cultural projects. It was inaugurated on 19 August 2021, and it is the first worldwide exhibition hall dedicated to the famous film director. The museum, intended as a widespread museum center, is divided into three separate places in the historic center of Rimini: Sismondo Castle (built at the behest of Sigismondo Malatesta, at that time Lord of Rimini and Fano, starting from 1437 AD), Fulgor Palace and Malatesta square. The goal of the present paper is double. In the first part, as a case study, the innovative integrated system and installations planned for the optimal functioning and management of the Fellini Museum of Rimini in Italy is illustrated, showing its related complexity, due to its extension and articulation through different environments and due to the respect for architectural/historical heritage. In the second part, as dedicated and linked research, a proper Genetic Algorithms-based method, studied and applied for the optimization of the design of the wired network of the integrated system, the electrical power network and the air conditioning network is illustrated. It guarantees a decrease of realization costs, considering also the typical vincula and restrictions of already existing historical buildings, such as the considered one.

Nitro-pollution in a confined aquifer may originate from its recharge area, e.g., agricultural sites, animal feedlots, septic tanks, and other waste disposal sites or from treated wastewater recharge wells. The latter case is studied. Existing water supply pumping wells should be protected for a given period. Instead of typically investigating optimal pump-and-treat or hydrodynamic pollution control management/remediation strategies, a novel combined pollution control and pump-and-fertilize (PAF) approach is proposed: protect existing wells with additional wells, considering pumped nitro-polluted groundwater as profitable reusable fertilizer rather than a pollutant to be remediated; convey pumped polluted water to an irrigation reservoir, considering nitrogen (N) uptake by irrigated crops in nearby farmlands and proportional decrease in fertilizer application, meaning profit. Optimization entails the operation of optimally located additional pumping wells with optimal flow rates, minimizing the sum of (i) annual pumping cost, (ii) pipe network (connecting additional wells and reservoir) amortization cost, and (iii) profit from retrieved N reuse. Modflow simulates a 3D flow field and advection-dispersion mass transport, while Genetic Algorithms (GAs) handle optimization. Various scenarios are simulated concerning crops’ retrieved N root uptake percentage, fertilizer, and energy market prices. The paper provides a data-ready optimization/decision support tool that creates a pool of alternative (sub)optimal management solutions/strategies.

In deregulated electricity markets, accurate load and price prediction play an essential role in the Demand Response (DR) context. Although electrical load and price demonstrate a strong correlation which is not linear, price prediction may be a task much more challenging than load prediction due to several factors. The volatility of electricity price compared to load makes price prediction a complex procedure. To perform purchasing decisions commercial consumers may rely on short term price and load prediction. A system which combines Adaptive Neuro-Fuzzy Systems (ANFIS) which predict Load Marginal Prices (LMPs) and electricity consumption is presented in this study. Furthermore, the Virtual Cost (VC) concept, which is the sum of the products between the predicted hourly consumption values and their respective predicted LMPs is introduced. Virtual Cost is assessed with a Fuzzy Decision Tree (FDT) compared to a threshold set by the customer. If needed, the amount of electrical energy that a healthcare facility must purchase at every hour of the day may be scheduled using Genetic Algorithm (GA) to meet the threshold criterion. This hybrid model proved economically beneficial for the facility, which is of great importance since the saved resources may be utilized to improve its infrastructures or for other purposes with social impact. The novelty of the proposed method is the utilization of ANFIS, Fuzzy Decision Trees and Genetic Algorithms combined as tools to improve the hospital’s energy and economic efficiency, achieving a reduction of the electricity costs up to 21.95 percent. The contribution of the study is to provide a reliable decision-making tool to everyone who participates in the electricity market in order to perform profitable energy scheduling automatically and accurately.

Finding the right time for weaning from ventilator is a difficult clinical decision. Several systems based on machine or deep learning are reported in literature. However, the results of these applications are not completely satisfactory and may be improved. An important aspect is represented by the features used as input of these systems. In this paper we present the results of the application of genetic algorithms to perform feature selection on a dataset containing 13688 patients under mechanical ventilation characterizing by 58 variables, extracted from the MIMIC III database. The results show that all features are important, but four of them are essential: ‘Sedation_days’, ‘Mean_Airway_Pressure’, ‘PaO2’, and ‘Chloride’. This is only the initial step to obtain a tool to be added to the other clinical indices for minimize the risk of extubation failure.

This paper addresses the lack of attention paid by the scientific community to the optimization of timber structures, specifically in the context of large-span agro-industrial constructions. The study focuses on the optimization of a three-dimensional roof composed of GL32h glulam regular double-tapered beams and purlins. Firstly, MATLAB was employed to develop a calculation software and then run the proposed optimization model based on genetic algorithms, in order to optimize the different geometries of the construction elements based on cost, as well as the optimum arrangement of the purlins and the number of beams. Moreover, statistical analyses were carried out on more than 200 optimization data points to uncover the influence of different variables in the optimization process. It was found that the snow load and span have a significant influence on the prediction of the height and width of beams and purlins, as well as the purlin spacing; while, as expected, the roof length also influences the optimal number of beams. All these findings could promote the use of timber structures; thus, achieving more sustainable and efficient construction practices.

Water Distribution Network Partitioning (WDNP), which is the partitioning of the existing Water distribution Network (WDN) into smaller and more homogeneous portions called District Metered Areas (DMAs), is an effective strategy that allows water utilities to improve network management through water balance, pressure control, water loss detection, and protection from contamination. The partitioning is realized physically, closing the pipes between two different districts, or virtually, installing flow meters which measure the districts inflow and outflow. Pipe closures lead to a considerable network performance worsening, reducing minimum pressure, resilience, and redundancy; on the other hand, flow meters allow us to avoid these issues but involve a higher investing cost. Hence, the DMAs’ definition could become a hard task because both network performance and maximum investing cost must be respected. This paper presents the application of an optimization approach, based on complex network theory, coupled with an optimization technique based on genetic algorithms (GA). The methodology, implemented in Python environment, consists of a clustering phase carried out with two different algorithms (Girvan–Newman and spectral clustering) and a dividing phase which defines whether a gate valve or a flow meter should be installed in a pipe. The last phase is fulfilled with the GA which allows us to optimize one or more objectives in order to minimize the cost and maximize the network performance. The methodology has been applied on the Udine water distribution system, whose hydraulic model has been calibrated with a recent measure campaign. The results produced with the different clustering algorithms and objective functions have been compared to show their pros and cons.

In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in order to balance exploration and exploitation effectively. Second, the search method needs to be distributed to take advantage of parallel computing resources. This paper presents BLADE (BLAnket Distributed Evolution) as an approach to achieving both goals simultaneously. BLADE uses blankets (i.e., masks on the genetic representation) to tune the evolutionary operators during the search, and implements the search through hub-and-spoke distribution. In the thesis, (1) the blanket method is formalized for the (1 + 1)EA case as a Markov chain process. Its effectiveness is then demonstrated by analyzing dominant and subdominant eigenvalues of stochastic matrices, suggesting a generalizable theory; (2) the fitness-level theory is used to analyze the distribution method; and (3) these insights are verified experimentally on three benchmark problems, showing that both blankets and distribution lead to accelerated evolution. Moreover, a surprising synergy emerges between them: When combined with distribution, the blanket approach achieves more than n-fold speedup with n clients in some cases. The work thus highlights the importance and potential of optimizing evolutionary computation in practical applications.

The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed to identify both the number of stations to be deployed and their respective locations that minimize the total cost by utilizing Genetic Algorithms. This is implemented by combining these components into a linear objective function aiming to minimize the overall cost of deploying the charging network and maximize service quality to users by minimizing the average travel distance between demand spots and stations. Several numerical and practical considerations have been analyzed to provide an in-depth study and a deeper understanding of the model’s capabilities. The optimization is done through commercial software that is appropriately parametrized to adjust to the specific problem. The model is simple yet effective in solving a variety of problem structures, optimization goals and constraints. Further, the quality of the solution seems to be marginally affected by the shape and size of the problem area, as well as the number of demand spots, and this may be considered one of the strengths of the algorithm. The model responds expectedly to variations in the charging demand levels and can effectively run at different levels of grid discretization.

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science The aim of this thesis is to investigate the possibility of creating content using the Genetic Algorithms. To this end a simple system of interconnected algorithms were developed using concepts from Role Playing Games, specifically Dungeons and Dragons to create game content as characters, quests, and encounters. To be able to produce context, subsystems of map, character, quest, and encounter generators were created. These systems or engines not only define the game space to be populated, but they also provide each other input to create maps, quests, locations, animals, and events that are sensible and coherent. Randomness of the generation was essential as such a variety of noise maps and random number generation were added to every engine in the system. Layered or singular noise maps allowed for logical assumptions to be made, like seeing camels in a location with no rain and high temperatures. With the base truth coming from a random noise map such as danger, civilisation, faction etc., each system built on top of each other can get more complex. There are several Genetic Algorithms with custom operators within the system. These algorithms take their inputs and individuals from the respective engines and tie them all to each other through their physical coordinates in the gaming space. The most impactful part of these algorithms is the Fitness Functions defined with concepts from literature or CGI. The proposed system can populate a game space with elements of desired attributes given the constraints. The output produced consists of coherently tied story beats with some attributes already set. Even in this simple level, this can allow not only game designers but anyone who wants to build any kind of fictional work.

The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment’s fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer’s personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer’s needs was obtained by performing the routine of “design–fit evaluation–pattern adjustment–comprehensive evaluation”, iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment.

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.

Electric aviation is being developed as a new mode of transportation for the urban areas of the future. This requires urban air space management that considers these aircraft. Flight routes need to be determined that avoid no-fly areas, and minimize flight time, energy consumption and added noise. Yet, no method currently exists for optimizing urban flight routes under multiple conflicting objectives while avoiding three-dimensional restricted areas. In our work, this research gap is overcome by optimizing 3D-routes with the multi-criteria optimization technique called Non-dominated Sorting Genetic Algorithm II. We propose a novel procedure in the optimization process to incorporate geographical representations. Furthermore, we include a seeding procedure for initializing the flight routes and repair methods for invalid flight routes that may arise during the optimization process. We apply the optimization to a case study in Manhattan (New York City) for two different aircraft types, the Lilium Jet (vectored thrust) and the EHANG 184 (wingless multicoptor), under three objectives concerning flight time, energy consumption and added noise. Compared to a least-distance path, flight routes were obtained with maximum improvements of 38% in added noise, 65% in flight time and 52% in energy consumption for the EHANG 184. For the Lilium jet, maximum improvements of 43% in added noise, 47% in flight time and 47% in energy consumption were obtained. Still, the obtained noise addition levels by the aircraft in New York City exceed 5 dB, which is considered as long-term noise annoyance. We illustrated that minimizing the added noise requires high search effort compared to the other two objectives. Upon further analysis of the optimization results, we conclude that the Lilium jet as representative of the eVTOL type vectored thrust is more sensitive to flight route changes than the multicoptor EHANG 184. This information may help in air taxi type choice for a certain region as well as in flight route planning.

The article presents a proposed customized genetic algorithm (CGA) to find the Pareto frontier for a bi-objective integer linear programming (ILP) model of routing in a dynamic network, where the number of nodes and edge weights vary over time. Utilizing a hybrid method, the CGA combines a genetic algorithm with dynamic programming (DP); it is a fast alternative to an ILP solver for finding efficient solutions, particularly for large dimensions. A non-dominated sorting genetic algorithm (NSGA-II) is used as a base multi-objective evolutionary algorithm. Real data are used for target trajectories, from a case study of application of a surveillance boat to measure greenhouse-gas emissions of ships on the Baltic sea. The CGA’s performance is evaluated in comparison to ILP solutions in terms of accuracy and computation efficiency. Results over multiple runs indicate convergence to the efficient frontier, with a considerable computation speed-up relative to the ILP solver. The study stays as a model for hybridizing evolutionary optimization and DP methods together in solving complex real-world problems.

This study aims to analyze the effect of genetic algorithm parameters on the distribution mileage of the largest logistics service provider in Central Jakarta using a general full factorial design and ANOVA test. This study revealed that all factors and three types of interactions were statistically significant in influencing the distribution mileage. Moreover, the combination of population size, crossover probability, mutation probability, and the number of iterations = (90, 1.00, 0.010, 800) generated the lowest mean value and was significantly different from other combinations. This study provides a different approach to analyzing those factors and interactions that influence finding the shortest route.

Durability is considered as one of the main technical obstacles to the large-scale commercialization of proton exchange membrane fuel cells (PEMFCs), which can be e.ectively improved through prognostics prediction techniques. This paper proposes a stacked echo state network (ESN) based on the genetic algorithm (GA) to predict the future degradation trend of PEMFCs. By alternately using the projection layer and the encoding layer, the proposed method can make full use of the temporal kernel property of the ESN to encode the multi-scale and multi-level dynamics of the stack voltage, thereby obtaining more robust generalization performance and higher accuracy than existing methods. Specifically, a stack voltage time series of PEMFCs is projected into the high-dimensional echo state space of the reservoir. Then, an auto-encoder projects the echo state representation into the low-dimensional feature space. After that, the genetic algorithm is utilized to optimize the hyperparameters of the developed model. Based on two open-source datasets of PEMFCs with di.erent accelerated test conditions, this paper systematically tested the proposed degradation prediction methods based on di.erent model structures. Test results demonstrate that the proposed method is superior to traditional prediction methods in terms of accuracy and generalization performance.

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®.

Novel pathway optimization methods are presented using the 'Global Calculator’ model and webtool1 to goal-seek within a set of optimization constraints. The Global Calculator (GC) is a model used to forecast climate-related develop pathways for the world’s energy, food and land systems to 2050. The optimization methods enable the GC’s user to specify optimization constraints and return a combination of input parameters that satisfy them. The optimization methods evaluated aim to address the challenge of efficiently navigating the GC's ample parameter space (8e70 parameter combinations) using Monte Carlo Markov Chains and genetic algorithms. The optimization methods are used to calculate an optimal input combination of the ‘lever’ settings in the GC that satisfy twelve input constraints while minimizing cumulative CO2 emissions and maximizing GDP output. This optimal development pathway yields a prediction to 2100 of 2,835 GtCO2 cumulative emissions and a 3.7% increase in GDP with respect to the “business as usual” pathway defined by the International Energy Agency, the IEA’s 6DS scenario, a likely extension of current trends. At a similar or lower ambition level as the benchmark scenarios considered to date (distributed effort, consumer reluctance, low action on forests and consumer activism), the optimal pathway shows a significant decrease in CO2 emissions and increased GDP. The chosen optimization method presented here enables the generation of optimal, user defined/constrained, bespoke pathways to sustainability, relying on the Global Calculator’s whole system approach and assumptions.

International audience; In this paper, we consider a buffer allocation problem in manufacturing flow lines with series-parallel network structure where nodes correspond to buffers of finite capacity, and arcs correspond to the machines. The machines are supposed to be unreliable, their time to failure and repair time are assumed to be exponentially distributed. Different machines may have different production rates and the production rates of all machines are assumed to be deterministic. The buffer allocation problem is to determine the capacities of all buffers with respect to a given optimality criterion, which is a function of the average production rate of the line, the buffer acquisition and installation cost and the inventory cost. In search for the optimum, the tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with the local search and genetic algorithms. It turns out that the “massif central” or “big valley” structure of the fitness landscape is present but only partially: The fitness of the local optima is negatively correlated with the distance to the best found solution, yet the set of local optima can not be encompassed by a ball of relatively small radius. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The symmetries of the fitness function are discussed and suggested as the possible cause of the local optima clustering. Finally the performance of genetic algorithms is bfiefly discussed with respect to solutions clustering.

Real-valued environments are challenging for learning systems because of a significant increase in the input space size of the problem. This work demonstrates that Anticipatory Learning Classifier Systems (ALCS) can successfully build sets of conditional rules foreseeing the consequences of executed actions. Three major classes of Learning Classifier Systems - Anticipatory Classifier System (ACS), ACS2, Yet Another Classifier System (YACS), alongside the traditional Dyna-Q algorithm implementations were adapted to handle real-valued input signal discretization. Aspects like the ability to capture all possible interactions, model generalization capabilities, size of the solution of relative execution times were compared in four different problems using probabilistic modelling, providing unbiased judgments. Results proved that the examined ALCS are capable of solving selected problems. Despite increased input size, all possible environmental transitions were learned latently, without obtaining any explicit incentives. Such an internal representation provides a more compact solution representation and can optimize learning speed further by executing imaginary environmental interactions or performing action planning for a new set of potential problems

Distribution systems continue to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to solve overcurrent relay coordination problems; however, the implementation of these methods is time-consuming. Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this paper presents a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on particle swarm optimizer and genetic algorithms performance, alternatively on the behaviour of overcurrent relays. Optimization algorithms aim to minimise overcurrent relay time multiplier settings and accomplish optimal protection coordination. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that 30% crossover, 2% mutation, and smaller population size yield faster convergence rate and optimise fitness function, which improves genetic algorithms performance. Sensitivity analysis results are verified by comparing the performance of particle swarm optimization with the genetic algorithms which show the former parameter setting outperforms the latter. The relay operational speed is reduced by 15% for particle swarm optimization and system selectivity is maximised. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and efficiency.

Old bridges present several seismic vulnerabilities and were designed before the emergence of seismic codes. In this context, partial seismic isolation has given a special attention to improve their seismic performance. In particular, elastomeric bearings are the simplest and least expensive mean for this, enabling to resist both non-seismic actions and earthquake loads. In order to assess the initial structural performance and the improvement done by the isolation, this paper attempts to combine multi objective optimization using genetic algorithms with linear and non-linear analysis using FE program OpenSees. A prior screening of the columns states is settled and then a multi objective optimization of a population of standard sized bearings meeting non-seismic and stability requirements is established to optimize the linear and non-linear behavior of the structure, finding the best compromise between displacements and forces at the columns

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations. CC BY-NC-ND 4.0Corresponding author: Amir NourmohammadiEdited by Emanuele Carpanzano, Claudio Boër, Anna ValenteThis study is funded by the Knowledge Foundation (KKS), Sweden, through the VF-KDO and ACCURATE 4.0 projects at the University of Skövde, Sweden. VF-KDO ACCURATE 4.0

These investigations are to find the numerical solutions of the nonlinear smoke model to exploit a stochastic framework called gudermannian neural works (GNNs) along with the optimization procedures of global/local search terminologies based genetic algorithm (GA) and interior-point algorithm (IPA), i.e., GNNs-GA-IPA. The nonlinear smoke system depends upon four groups, temporary smokers, potential smokers, permanent smokers and smokers. In order to solve the model, the design of fitness function is presented based on the differential system and the initial conditions of the nonlinear smoke system. To check the correctness of the GNNs-GA-IPA, the obtained results are compared with the Runge-Kutta method. The plots of the weight vectors, absolute error and comparison of the results are provided for each group of the nonlinear smoke model. Furthermore, statistical performances are provided using the single and multiple trial to authenticate the stability and reliability of the GNNs-GA-IPA for solving the nonlinear smoke system.

The use of metaheuristic, nature inspired algorithms for solving complex optimization problems with non-linearity and multimodality, has become a popular tool in the field of science and engineering. Presently, there has been much development of such tools, with academics creating new nature-inspired algorithms that could potentially be more efficient and effective. One such algorithm is the Flower Pollination Algorithm (FPA) developed by Xin-She Yang in 2012. This metaheuristic algorithm is modelled after the evolutionary process of flowering plants and has been useful in many fields of science and engineering, particularly in electrical power systems. With the rapid expansion of industries and population growth, engineers are faced with the arduous task of satisfying the growing electrical demand. To improve electrical power systems and reduce running costs, FPA and its variants have been implemented to determine optimal solutions to complex combinatorial optimization problems, with little computational effort, and has proven to be more effective than other popular metaheuristic algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Owing to its balanced global and local search capabilities, and better convergence characteristics, numerous studies have been conducted to utilize FPA and its variants in real-world optimization problems. Thus, this paper aims to provide a comprehensive review of FPA and its variants to successfully determine optimal results to non-deterministic polynomial-time hard problems in electrical power systems and other fields of science. To accomplish this, an in- depth description of FPA and its parameters that influence its performance are discussed, including the various modifications and hybridizations of the algorithm. In addition, an in- depth review of applications using FPA and its variants in electrical power systems and other fields of science, is provided.

Academic studies on the Automatic Voltage Regulator (AVR) have focused on a linear mathematical model which lacks protective features. A more accurate model of the AVR system includes protective features as described in IEEE standards. The AVR models without protective features, namely limiters, result in less accurate control performance since the outputs of real controllers are always bounded. In the present study, the controller outputs are limited between -0.9pu and 1.0pu, and the upper bound of exciter output is limited by 3.1pu with the direction of IEEE standards. The effect of limiters on the controller performance is investigated. Two controllers with a novel sliding surface function are proposed based on a mathematical model of the AVR and its approximate reduced-order model. The proposed controllers having only two parameters are optimized with improved particle swarm optimization (PSO) algorithm. After optimization, the robustness of the controllers is compared with the results of various operating conditions identical to the previous studies. Although controller inputs and exciter outputs are limited, the maximum overshoot is measured as less than 0.1% at no-load conditions. Nominal time constants of the AVR constituents are perturbed from -50% to 50%, and ±10% load disturbance is applied to the output. The robustness of the controllers against parameter uncertainties is measured with an average overshoot at the output. Another superiority among the reported results in the literature is obtained from the proposed controllers, where the minimum average overshoot rates are obtained. In addition, when ±10% load is applied to the output of the AVR, the proposed controllers generate accurate control inputs to reject the load disturbance successfully. Furthermore, the proposed controllers keep the output within the ±5% band if there is a monotonic change at the output. All the results show that the proposed controllers with the improved PSO have drawn the best performance from the perspective of time-domain specifications in comparison with the recently reported controllers.