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This paper addresses the uncapacitated Facility Location Problem (UFLP), which involves determining the optimal subset of facilities to open from a given set of potential locations. The objective is to satisfy the demands of all customers while minimizing the sum of the opening costs and service costs. Due to its computational complexity, UFLP is classified as an NP-hard problem. To tackle this challenge, a metaheuristic algorithm is a suitable approach. Previous studies have proposed various metaheuristic algorithms to solve the UFLP. In this research, we employ a novel algorithm called the Whale Optimization Algorithm (WOA), which draws inspiration from the hunting behaviour of humpback whales. The performance of WOA is compared to that of the Genetic Algorithm (GA). The results demonstrate that GA is capable of finding near-optimal solutions, albeit at the cost of increased computational time. Conversely, WOA exhibits fast convergence to a solution [ABSTRACT FROM AUTHOR]

In any supply chain, the cost of inventory makes up around 30% of the value of the product, which makes it one of the most crucial aspects of supply chain management. Several heuristic and metaheuristic inventory optimization techniques are widely used in management of supply chains, but there are lot of uncertain factors for inventory optimization and many techniques can be improved or adopted for better management of inventory. Genetic algorithm is one of the commonly used algorithms to manage inventory. Since it can be easily adapted and can handle multiple criteria, but it does not guarantee optimality. A serial supply chain with a supplier, manufacturer, distributor, and a retailer are considered in this paper. The inventory for a single product is managed by deploying periodic review base stock policy at each member of the supply chain. Three metaheuristic algorithms namely Genetic Algorithm, Crow Search Algorithm and Adaptive G-best Gravitational Search Algorithm are used to optimize the decision variables with the objective of minimising total cost. Experimental analysis is performed on all the algorithms and results obtained from the experimental studies are compared. [ABSTRACT FROM AUTHOR]

Cryptography is the science that is classified into encryption and decryption. Encryption is the process of converting plaintext into cipher text. On the other hand, decryption is the process of ciphering text into plaintext. Key is an alphabetic or a number between the sender and the receiver. The Genetic Algorithm (GA) is an adaptable search heuristic method based on the evolutionary concepts of natural selection and genetics. In particular, the GA works by imitating the processes essential for evolution in normal systems, especially those that follow the survival of the fittest principle, which was initially stated by Charles Darwin. The challenges that were published include how to cryptanalysis cipher using only one method, which means that the analyzer can know the type of this method before analyzing the cipher text. This paper presents three breaking methods of ciphers using GAs to know the type of cipher security to encrypt plaintext, which is done through the fitness function values in these methods. The three methods include mono-, poly, and transposition ciphers. The frequency evaluation is utilized as part of the objective function. To evaluate our proposed approach, three methods with keys are used to test which one is used to encrypt the text. The text length used in this paper is 2000 letters. The methods used in this paper are evaluated to detect the key encryption according to the fitness value of each method. The fitness value was less than 75% in the monoalphabetic substitution cipher, while in the Vigener cipher, it was 89% and 35% in the transposon cipher after 90 to 100 generations in less than 700 msec. Hence, the key is encrypted in the Vigener cipher, and it is easy to cryptanalysis the cipher text. As a result, these methods demonstrate an intelligent use of random searching to solve issues within a given search field. [ABSTRACT FROM AUTHOR]

Cybersecurity utilizes Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) to distinguish malware from people. This paper proposes an Arabic-character CAPTCHA creation approach to improve security and inclusivity. Differential evolution and genetic algorithms optimize CAPTCHAs of various complexity. The technique builds a recursive population of alternative solutions using DEAP (Distributed Evolutionary Algorithms in Python) to increase CAPTCHA similarity to the intended text. Arabic letters enhance a character set, making computerized solvers harder and supporting Arabic-speaking users. The approach delivers robust and diversified CAPTCHAs, according to tests. A multi-algorithmic approach to multilingual CAPTCHA usability and security appears promising. [ABSTRACT FROM AUTHOR]

The Cisangkuy sub-watershed is part of the Citarum watershed and has a river length of ±300 km. It is wide enough to cover up to 10 sub-districts, and its size is in line with a high potential of water resources. However, its hydrological conditions have degraded over time due to changes in land cover from year to year that have caused an increase in river discharge as well as flooding during the rainy season. The present study aimed to find the optimal location for artificial recharge in an effort to maintain the water balance in the Cisangkuy sub-watershed. This location was determined using a combination of parameters of depth to groundwater level, net recharge, aquifer media, soil, topography, impact of vadose zone and hydraulic conductivity (DRASTIC) and a genetic algorithm (GA). DRASTIC were used as the input parameters for the GA. Furthermore, the GA was used as an optimization engine to determine the optimal artificial recharge location. The combination of DRASTIC and GA results revealed that the optimal location is in the Banjaran District in the northern part of the Cisangkuy sub-watershed. [ABSTRACT FROM AUTHOR]

The number of traffic fatalities in Indonesia rose every year. Frontal crash was the most frequent of all the crash direction. In order to reducing occupant deaths and injuries, structural crashworthiness recently has acquired paramount significance in the development of energy absorption crash boxes. For this purpose, the robust design crash box using multi objective optimization based on the MOGA method (Multi-Objective Genetic Algorithm) was carried out. Tri-tubular multi-cell crash box was designed based on a bionic structure. The selection of design variables, namely the thickness of the outer, middle, and inner walls, was made to achieve optimal energy absorption during crashes while also prioritizing lightweight design. Energy absorption crash box with different wall thickness were simulated under frontal crushing load. According to the computer simulation results, it is evident that the wall thickness ratio significantly influences the crashworthiness. An increase in the middle and inner wall thickness showed an increase in the absorption of energy. [ABSTRACT FROM AUTHOR]

The performance of a network, including wireless sensor networks, is determined by various aspects, including the position and function of its nodes. The node placement methods, especially relay, are an interesting topic as they were the key to enhancing WSN communication ability. Relay placement is usually associated with energy efficiency, coverage area, connectivity, and communication costs. Dozens of recent surveys have even explored other aspects, including reliability, scalability, and security. As the optimization aspects become more complex, researchers consider that the multi-objective optimization (MOO) approach is the most promising solution. This research aims to implement a Multi-Objective Optimization (MOO) method of relay placement on a large scale to construct a WSN with minimum cost and fault tolerance ability. From the point of view of WSN, there are at least two novelties: the area modeling and the restriction of the hop number. This area modeling allows each sensor to connect to at least two different relays and construct multi-paths to the sink. We constructed this placement method over a modified genetic algorithm and combined it with the explorative character of swarm intelligence. In this research, some chromosomes of GA were decomposed as particles of PSO to extend the solution. The proposed method shows positive results that combine the good aspects of both algorithms in planning and designing a WSN. [ABSTRACT FROM AUTHOR]

In the last few years, there has been a growing interest in roof structural systems because of their wide applications in the structure of low-rise buildings. Former research indicates that the analysis of such systems plays a key role in the design of fasteners with higher performance against the likely damage and failure that occurred due to high wind events, especially at the connecting point between the fastener and purlin sheet. To find out a solution for improving the strength of the cladding fasteners, this study aims to present the three methods of geometry optimization for a cladding fastener that is made of steel austenitic 316. In the first method, two geometry parameters, namely the thread depth and the thread angle are chosen as the design parameters and manipulated by a genetic algorithm to minimize the maximum von Mises stress against applied tensile force. The same parameters are swept in the second method in order to generate a set of data for training a neural network and then the optimum values of the thread depth and angle will be predicted by the network versus the desired value of von Mises stress as an input. In the third method by proposing the mathematical model, the optimum geometry values of the fastener will be obtained by maximizing the pull-out force capacity. Finally, functional tensile tests and the results of the simulation will compare the pull-out force of fasteners designed by three methods. [ABSTRACT FROM AUTHOR]

In rural areas, the retrofitting of housing should be tailored to local characteristics due to the presence of numerous uncertain parameters. By employing sensitivity analysis (SA) on these parameters and multi-objective optimization (MOO), an appropriate reconstruction scheme for rural areas can be identified. Therefore, the objective of this article is to mitigate energy consumption and discomfort hours through envelope retrofitting in rural houses. Firstly, a SA is conducted on uncertainty parameters and envelope design variables in the process of retrofitting rural residential building to determine the parameters and design variables that have a significant impact on building performance. Secondly, the Pareto frontier is obtained by applying MOO using genetic algorithm (GA) to the envelope design variables. Finally, TOPSIS method is used to transform the multi-objective problem into a single objective, leading to identification of an optimal solution. The results show that this method can effectively obtain the optimal combination to achieve the goal. [ABSTRACT FROM AUTHOR]

Location-routing problem is a problem that integrates both facility location problem and vehicle routing problem. The problem is aimed to solve the allocation of customers to the established depots and optimize the distance between the open depot and the assigned customers. This study considers multi-objective where the first objective is minimizing the cost of operating depot and cost of travel distance, meanwhile in the second objective is maximizing the profit from customer's returns. As the problem is known as NP-hard problem and have the conflict objectives, metaheuristic method which is Multi-objective Harmony Search Algorithm (MOHSA) was proposed. The multi-local search neighborhood technique is applied in this algorithm. A numerical analysis is conducted in simulated dataset to find a set of Pareto front by non-dominated sorting genetic algorithm II (NSGA-II) in MOHSA using both fuzzy membership approach and the trade-off ranking method. The best method is determined by the evaluation of performance metric. The results show that the fuzzy membership approach can gives the better compromise solution as compared to the trade-off ranking method. [ABSTRACT FROM AUTHOR]

Nanotechnology invades all fields of the science to become the focus of attention of the researchers over the entire wild world. This technique allows the controlling of the atom and being able to move it freely and easily within an element or compound. Upon these operations, the properties of materials may change and it is possible to obtain new compounds that cannot be obtained by the available scientific methods. It also focuses on the processes of separating, combining, and reforming materials with a single atom or part. Artificial intelligence applications have played an important role in accelerating the space of nanoscale measurements of elements and various algorithms have been proposed in order to improve that. The neural networks, which are used for image recognition, fuzzy logic which is used for prediction and the Genetic Algorithm (GA) for optimization are the most known one. In addition, GA has the ability to improve a set of solutions in order to obtain the optimal one according to tuning of the specific parameters. This paper aims to propose an algorithm based on the original GA to enable reconfiguration of the particles of an element in order to obtain the largest possible space between the particles or atoms of the elements. The available space within an atomic element or molecule is estimated in nanometers. This gives the opportunity for the possibility of proper exploitation of such spaces in more clear and accurate industries manners according to the properties of the resulting material. [ABSTRACT FROM AUTHOR]

The travelling salesperson problem (TSP) is the most studied problem in optimization and computer science. A more challenging version of the Travelling Salesperson Problem (TSP) is the Multiple Travelling Salesperson Problem (MTSP). The goal is to find the shortest path, which is the shortest distance for each salesperson to travel from the depot to each city and return to depot. It is a NP hard problem and has various applications in routing and scheduling. There are various algorithms to solve MTSP such as exact solution method, bioinspired algorithms like genetic ant colony, evolutionary. In this paper we review exact algorithm and bioinspired algorithm. [ABSTRACT FROM AUTHOR]

Partners generate an annual output of approximately 100 tons of rice seeds. The primary issue pertains to the elevated costs associated with distribution. Distribution route optimization seeks to identify the route that incurs the lowest distribution costs. This study employs a multi-traveling salesman problem (M-TSP) methodology to address the task of a salesperson who needs to visit multiple retailers and agents in various regions of East Java Province. The M-TSP problem is modeled as a graph with n vertices, which correspond to the locations that m sales assistants need to visit. This study employed a novel approach, specifically a modified genetic algorithm, to conduct route optimization. Another study utilized genetic algorithm development to enhance computational efficiency and achieve optimal solutions. This study utilizes and adapts the genetic algorithm's operator modification for the M-TSP problem. This study incorporated a novel variable referred to as time window (TW). The time window variable represents the operating hours for each destination. The evaluation of the M-TSP solution incorporates penalties that are determined by a predefined time window. The test results indicate that the optimal value for the generation variable is 800. The optimal parameter combination is the eighth combination, characterized by a CR value of 0.7 and MR value of 0.3. [ABSTRACT FROM AUTHOR]

Educational institutions frequently struggle to develop a workable lecture/tutorial schedule in a major universitydepartment. In this study, an evolutionary algorithm (EA)-based method is used to resolve the university scheduling problem. Throughout the process, a chromosomal representation tailored to the task is used. In order to generate workable timetables in a fair amount of computation time, heuristics and context-based reasoning have been applied. The strategy used to accelerate convergence is clever adaptive mutation. Real data from a large university has been used to validate, test, and debate the whole course scheduling system mentioned in this paper. Higher education institutions are currently concerned about students' attendance patterns. For the faculty to construct schedules automatically, a computer-based automatic timetable generator system is required. Our ideas for the "Automatic Time Table System," which consists of many applications, have been laid out. The application's timetable generation feature helps you save time. The system's automated Excel spreadsheet is utilized to maintain a record of the teachers' availability and scheduling. Our students will find this system helpful in the twenty-first century. It takes a lot of time and labor for educational staff in universities with huge student numbers to manually create a schedule. Due to this, it commonly happens that the same professor will give numerous lectures or competing classes. Automatically generating test and class schedules will be made simpler by the availability of a timetable generator. It will be produced by the system automatically, which will also help you save time. It prevents having to manually set up and manage a schedule. The purpose of this project is to develop a simple, functional, and user-friendly application that will facilitate the construction and distribution of schedules. The genetic algorithm is themain technique used to make schedules. It helps in the development of the ideal schedule by regulating all the rules. Conflicts in scheduling are not an issue for the faculty. With inputs such teacher name, data for the rooms, labs, and subject, this system will have a user-friendly, interactive, and less complicated interface. To store all the data entered as input, the system will have a carefully constructed database. The system will have an algorithm that manages all of the database's data while taking into account both hard and soft constraints. a timetable generation technique that seeks out the optimal solution using genetic algorithms. Instead of requiring time-consuming documents, the staff and students can easily view the timetable. [ABSTRACT FROM AUTHOR]

In this work, MPPT method for a solar PV array (SPVA) that is partially shaded is presented using the Genetic Algorithm (GA). It is difficult to detect the genuine peak in a PV array that is partially shaded because there are several peaks present, and this makes it difficult to trace the peak. Under these conditions of non-uniform insolation, conventional techniques of tracking are incapable of functioning as intended. Some of the strategies that have been provided within relevant literature monitor the worldwide peak (GP), but with certain restrictions. The aim of this research is to propose a GA-based MPPT with the end goal of increasing the power of SPVA. The suggested technique is evaluated using simulation in order to examine its performance for a variety of shading patterns. [ABSTRACT FROM AUTHOR]

In the healthcare industry, telemedicine has emerged as a game-changing technology that makes medical treatments accessible from a distance. However, the critical factors for delivering high-quality healthcare are the assurance of low latency and efficient resource utilization. This research study presents a new methodology to tackle these obstacles by incorporating Genetic Algorithms (GAs) into a Fog Computing framework for dynamic load balancing in telemedicine systems. By conducting a series of experiments, we conducted a comparative analysis between this approach and conventional cloud and fog computing configurations. The findings illustrate that our proposed strategy exhibits persistent superiority over alternative methods, resulting in a notable latency reduction and improved resource use optimization. Our load balancing technique, based on genetic algorithms (GA), enhances the quality of telemedicine services by effectively adjusting to varying workloads and network circumstances. This adaptation capability can increase patient care and ensure scalability for upcoming healthcare systems. [ABSTRACT FROM AUTHOR]

The ancient system of medicine Ayurveda, classifies humans into three major phenotypes known as Vata, Pitta, and Kapha. The descriptions from the classical texts imply that each individual has a specific Prakriti which contributes to their physical and mental health. It refers to an individual's inherent constitution or genetic makeup. The Prakriti of an individual is decided during birth and remains unaltered during the entire lifetime. Various research on different fields including the body constitution, metabolism, and even diseases have shed light on the relationship of Prakriti with genomics. Though the status is quite satisfactory, there are unexplored areas like the development of a Prakriti-based genetic algorithm in this field. The future scope of Prakriti as a diagnostic tool, in prophylaxis and treatment aspects need to be considered with utmost care. If combined with Pharmacogenomics and Ayurnutrigenomics, personalized medicines and diet plans can be introduced to individuals which can help in tailoring a healthy life. It can also act in preventing and treatment of several diseases. It is crucial to recognize the holistic nature of Ayurveda and consider all aspects of an individual's well-being when applying genomic information in an advanced field of research. [ABSTRACT FROM AUTHOR]

To optimize the use of control surfaces on fly-wing model aircraft, a Non-binary Genetic Algorithm (NSGA) simulation is proposed. This algorithm will help the pilot determine the percentage of flat, slat, and trust engines based on the desired airspeed so that the pilot can focus on directing the plane to its destination. In this simulation, an airspeed of 55 km/hour to 65 km/hour was tested at the real speed and the model has been created to take off. The simulation results also showed that under airspeed conditions of 60 km/hour, the iterations to get the best chromosome were less than 10 times. At airspeeds below 60 km/h, the iteration is repeated more than 10 times, even up to 85 times, to get the best chromosome. This is probably because all control surfaces in this condition are almost working optimally, to determine the optimal value we have to combine them up to the maximum value of each control surface. [ABSTRACT FROM AUTHOR]

The article proposes a genetic algorithm for optimization of short-term modes of power systems that include thermal and hydroelectric power plants (TPPs and HPPs), taking into account all simple, functional and integral constraints in the form of equalities and inequalities. In it, the constraints on the power balance in power system for each time interval of the considered planning period are taken into account by the allocation of a balancing power plant. Constraints on the minimum and maximum possible loads of the balancing power plant, as well as functional constraints, which in general cases have non-linear nature, are taken into account by penalty functions in exponential form. The constraints on water consumption in HPPs for the period under consideration are taken into account by quadratic penalty functions. The computational qualities of the proposed algorithm are researched on the example of optimal coverage of the load schedule of power system containing several TPPs and HPPs that are involved in optimization. The research results showed that the proposed algorithm is highly efficient. [ABSTRACT FROM AUTHOR]

The multirotor wind turbine systems have an advantage over others that they are small in size, due to their working places and operating conditions. Therefore, all turbine parts must have good engineering specifications in terms of size, mass, efficiency, and performance in relation to mechanical and electrical parts. In this research, the optimum design was carried out of the Permanent Magnet Synchronous Generator (PMSG) by using the Genetic Algorithm method. The appropriate fitness function has been determined to reduce the total mass of the generator. Design equations have been configured that include the following; the bore dimensions and an electromagnetic force, the active length of the generator, the air gap, the angular width of the magnet per pole, the slot depth trapezoidal and a stator phase's receipt of Find out the vacuum value of magnetic flux. When making the optimal design, and a number of generations were taken, optimal design analysis focuses on determining its value generations such as the slot depth to bore diameter ratio of the PMSG is 28, the the active length divided by the bore radius is 17, pool pairs number is 8 and of the bore half diameterratioto the span of action is 24. There are some conclusions, the air gap is thick, measuring reduced by about 0.09 mm and this decrease leads to an increase in the magnetic force and the number of pole pairs to about 8, This increase will lead to an increase in PMSGefficiency. [ABSTRACT FROM AUTHOR]

The axial forces' effect on the rotating shaft's performance is often harmful. So, this work is proposed to reduce their impact by using active thrust magnetic bearing for the Gas Turbine Model (ME9001) used in the power plant of south Baghdad station. The sub-optimal design was studied in this research to identify the ideal bearing dimensions in the suggested application. The genetic algorithm (GA) optimization approach with multi-objectives is used as part of the design process. Some of the results that had been reached were the current optimum design of TAMB (thrust active magnetic bearing), a shaft clearance radius of 3mm, and the operating gap varies from 0.5mm to 0.8 mm with a thrust force of 400KN. The saturation flux density of the saturated pure iron is approximately 2.3 Tesla, and the best straight part is from 0 to 1.5 Tesla. It was noticed that the value of flux density in the stator after optimization is approximately (1.7) Tesla, which does not exceed the saturation limit for pure iron flux density, which proves the validity of the design. The current density of the TAMB coil after optimization is approximately (2 A/mm2), which does not exceed the saturation limit. That proves the design validity and reduces the power loss of the TAMB coil. Also, the thrust collar thickness (disk) can be smaller than the back wall thickness of the actuator because the flux density in the back wall will reduce after passing from the actuator to the thrust collar through an air gap, and that gives the advantage in minimizing the thrust collar weight as well. [ABSTRACT FROM AUTHOR]

In this study a simplified bilinear capacity curve of a frame structure is reconstructed by means of a limit analysis strategy based on the implementation of genetic algorithms. The proposed procedure takes into account the second order effects considering a post-yielding softening branch. In order to consider the progressive damage in the frame, the pre-yielding branch is determined with reference to a linear model where rotational springs are introduced in some critical sections of the frame. The calibration of the stiffness of each rotational spring is performed considering the bending moments at the corresponding section when the first plastic hinge in the frame opens. The results obtained through the proposed simplified strategy are validated with well-established approaches based on nonlinear static analyses, showing the reliability and the computational efficiency of this methodology. [ABSTRACT FROM AUTHOR]

Search-based data analytics is a big area that requires great attention in many different areas. To address real-time issues, many methods and algorithms are utilised for search-based optimization. Many fields of study include design, accounting, finance, continuous imaging, and many more, but the most important are machine learning and deep learning. The experts are experimenting with a wide range of technologies and advancements, including open source, and have developed methods that enable them to achieve a greater degree of precision. When it comes to deep learning, it's inextricably linked to machine learning since it combines greater levels of execution and accuracy with a lower base error rate. Genetic Method, a high-performance algorithm for engineering optimization, is presented in the paper as use cases. For the purposes of data analytics and in particular for a multiprocessor scheduling strategy, the goal of this article is to provide the effective implementation of the famous evolutionary computing technique of genetic algorithm for genetic algorithms. Due to its better results on a variety of factors, the genetic algorithm described here may offer real and measurable results. There were no successful outcomes from the prior techniques' usage of greedy-based approaches, therefore researchers turned to genetic algorithm-based optimization. To design and simulate the implementation scenarios, we used MATLAB and Java-based development tools, and the results were very useful for optimizing. [ABSTRACT FROM AUTHOR]

Cluster analysis of human-generated seed data exhibits inefficiency and lacks robust validation measures. This research focuses on identifying crucial cluster attributes through a combination of feature selection, Genetic Algorithm, and subspace clustering techniques. In today's data-driven landscape, clustering plays a pivotal role in grouping data items into meaningful clusters, optimizing likeness within a cluster while reducing it among the clusters. Traditional algorithm for clustering employs the entirety of the feature space dimensions, often leading to suboptimal results due to irrelevant and redundant attributes. Author introduces two novel methods: GA-FS Clustering (utilizing Genetic Algorithm for feature selection) and COSA-Clustering (clustering based on a subset of attributes) tailored for clustering high-dimensional data. The performance of these methods is rigorously analyzed, aiming to enhance the efficiency and accuracy of cluster analysis in complex datasets. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms, such as genetic algorithms and particle swarm optimization, are optimization and search algorithms inspired by the process of natural selection. These algorithms evolve a population of potential solutions over multiple generations to find optimal solutions to a given problem. In many optimization problems, there can be multiple optima, which may be isolated from each other in the search space. The evolutionary algorithm might converge prematurely to a single optimum, missing other potentially better solutions. Niching strategies aim to address this issue by encouraging diversity within the population. This work presents novel variants of three popular evolutionary algorithms Genetic Algorithm, Binary Differential Evolutionary Algorithm and Binary Particle Swarm Optimization Algorithm with niching using multiple populations to obtain multiple optima. These algorithms have been applied on a set of 71 fiducial ECG features to obtain multiple optimized subsets. The optimization criteria are to maximize accuracy of SVM classifier and at same time minimize the number of features in the subset. 100 unique optimized subsets are obtained with each algorithm. [ABSTRACT FROM AUTHOR]

Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network. [ABSTRACT FROM AUTHOR]

In this paper, an optimization approach is brought forward for optimal energy scheduling in a modern smart home. After the development of smart devices and their integration with smart grid, the end consumers have the chance to schedule their home appliances in order to decrease the overall electricity price and allaying the power Peak to Average ratio (PAR). The application pattern of home devices is observed at first for optimal energy scheduling. The home gateway (HG) obtains the demand response (DR) showing the real-time electricity price (RTEP) and later sent to energy management controller (EMC). With the Demand Response, the EMC results in the essential energy scheduling that can be sent to each electric appliance by the Home Gateway. The next feature is integrating RTEP with IBR, because if only RTEP is considered the appliances tend to be scheduled at lowest electricity price slot, this may lead to blackout during that time period. By executing this optimization method, our proposed approach could gradually reduce boththe electricity price and PAR, thereby, strengthening the stability of the entire Grid system. The optimization technique we used to solve this problem was Genetic Algorithm (GA) as the optimization problem was nonlinear. [ABSTRACT FROM AUTHOR]

One of the most crucial requirements for any power system is optimal power flow (OPF). The relevant optimisation problem thus attempts to create new distributions of active and reactive power while respecting the necessarytechnological and financial limitations. Considering this as an optimisation problem that entails minimising a target function while considering practical and budgetary constraints, an attempt is made in this work to redistribute both active and theoretical power, an approach that necessitates modification and the use of several control variables in the system. The purpose of this research is thus to discuss the OPF problem based on four target functions: real power losses, fuel value, voltage enhancement, and imaginary power losses, recruiting the assistance of a particle swarm optimisation)PSO(technique. After resolving the OPF issues, the PSO outputs were thus compared to those for various other techniques, including genetic algorithms and linear programming. The proposed PSO was demonstrated to achieve lower costs and losses and an improved voltage profile in case studies involving IEEE-30 buses when implemented in MATLAB environment R2018a. PSO also reduces the target function voltage profile or voltage deviation from the initial state of 0.48378 p.u to the optimal state of 0.199181 p.u. while reducing the target function's fuel cost from a starting condition of 901.9 $/hr to an ideal state of 800.636 $/hr. [ABSTRACT FROM AUTHOR]

Inadequate safety, image degradation, low latency, low-key sensitivity, diffusion and strong correlation are various major challenges and issues with the traditional image encryption approach. Consequently, a better picture encryption algorithm based on scrambling and genetic algorithm is proposed in this work. In the beginning, image scrambling performs transformations in the position of every pixel without altering values of the pixels. Its statistical features do not change. Therefore, the proposed algorithm of two-level image encryption makes extensive use of a mix of Chaotic Scrambling and Arnold transform along with genetic encryption to increase image encryption more secure. Conclusively, to evaluate the durability of the established two level techniques, the decrypted image is exposed to brute force attack along with comparable key attack. The method offers certain benefits in robust performance, statistical efficacy in addition to this, it also gives high performance in key sensitivity test, picture security and real-time performance, according to experimental results. [ABSTRACT FROM AUTHOR]

Power systems facing dynamic and nonlinear characteristics require effective optimization techniques to ensure stability, efficiency, and controllability. This paper proposes a method for optimizing power systems using a genetic algorithm that takes into account complex relationships in the power infrastructure. The genetic algorithm provides a flexible and versatile means for global optimization, providing robustness to local minima and adaptation to system dynamics. The method was applied to optimize the energy balance, minimize costs and take into account the nonlinear characteristics of the power system. Experimental results validated the effectiveness of the proposed method, demonstrating energy balance and high accuracy while accounting for complex system characteristics. However, further research is required to improve the method, including a more in-depth analysis of the influence of algorithm parameters and consideration of additional uncertainty factors. This research provides valuable insights into the field of power system optimization and provides a basis for the development of more effective control techniques in complex and dynamic energy environments. [ABSTRACT FROM AUTHOR]

The application of Traveling Salesman Problem can be seen in various areas of life including the problem of determining the shortest route in the distribution of rice. The problem of choosing the optimal route for rice distribution in this study was taken using data on rice refineries in the Simalungun area. So many agents have become customers of the rice refinery. Currently, these agents are spread almost throughout the simalungun regency. Therefore, the refinery must optimize performance in distributing rice. However, due to the large number of agents available and the lack of consideration of distance from one point to another, it will have an impact on the time and operational costs of the industry. For this reason, an application for determining the shortest route is needed so that it makes it easier for couriers to distribute rice to these rice agents using genetic algorithms with the crossover partially mapped crossover method and mutation techniques, namely swapping mutation. In this study, testing was carried out with data on 25 agent points with a crossover probability value of 0.5 and a mutation probability of 0.01. [ABSTRACT FROM AUTHOR]

Nowadays, because of the high demand for car transportation, not everyone has the purchasing power of cash, and people sometimes pay for it on credit. ACC is a finance company that acts as a facilitator for car loan financing. ACC collects car loans through collectors who are assigned an area in Medan City and also the address of the customer who will be billed in installments. To maximize the installment collection process, the collector's journey needs to be optimized. A metaheuristic algorithm can solve the collector-collection trip optimization problem. The metaheuristic algorithm used to solve this problem is the Steepest Ascent Hill Climbing algorithm and the genetic algorithm to find the optimum route from the collector billing path. After the two algorithms are implemented and tested, the final result of this research is the complexity of the Steepest Ascent Hill Climbing algorithm is θ(n) with a running time of 429.1936 ms on a 5-point test, while the complexity of the genetic algorithm is θ(n3) with a running time of 8921.8021 ms on a 5-point test. [ABSTRACT FROM AUTHOR]

The control device of a vehicle's anti-lock braking system (ABS) most commonly implements two-position control. In modern automation systems, many other control laws are used, which in certain cases are much better than the two-position control. This article discusses the development of a fuzzy PID controller for the control of an ABS. The fuzzy PID controller is tuned using the optimization method, which employs a genetic algorithm to determine the optimal solution. A comparison is made between the transient processes in the ABS control device with a two-position controller, a PID controller and a fuzzy PID controller, and the robustness of the system is evaluated. The 14 s braking time for the two-position controller decreases to 13 s when operating with a PID controller and to 11 s when operating with a fuzzy controller, which represents a 25% reduction in braking time, and therefore a shorter braking distance. The developed fuzzy controller also provides a better system robustness and will reduce pressure fluctuations in the pipes. [ABSTRACT FROM AUTHOR]

Vehicles are one of the modes of transportation that play an essential role in the distribution of goods. However, transportation costs are affected by vehicle type, loading and unloading capacity, and transportation distance. This study aims to determine vehicle type allocation based on capacity priorities and routes that minimize distance and transportation costs. The Clarke Wright saving heuristic is used to determine retail vehicle allocation, and the genetic algorithm determines the best route for each vehicle. Based on a real-world application at one of Indonesia's LPG distributor companies, both methods can provide distance savings of 58.25% and transportation cost savings of 22.82%. [ABSTRACT FROM AUTHOR]

An autonomous Surface Vehicle (ASV) is an unmanned vehicle that can be controlled by remote control (manual) or automatic (autopilot). In carrying out its mission, the ASV must be able to maintain its position and stay on track. One of the important capabilities of a ship is maneuvering. This paper modeled the ASV maneuver using the Nomoto model and designed a closed-loop control system with Sliding Mode Control, where the parameters are optimized by genetic algorithms. Based on the results obtained, the genetic algorithm is proven to be able to properly optimize the Sliding mode control parameters. The results of the response time for the maneuver with the Nomoto model were able to be stable and reach the desired set point, with a settling time of 5.5 s, 3.7 s Rise time, 0% Overshoot, and 0% steady-state error. [ABSTRACT FROM AUTHOR]

The power density and fuel efficiency of a diesel engine can greatly be improved using a compressed natural gas-diesel dual fuel (CNG-DDF). At low loads, however, the effect of DDF causes a number of issues, including decreased engine performance and an increase in pollutant output (i.e., CO and NOx) during operation. As a result, a combined approach using backpropagation neural networks (BPNN), genetic algorithms (GA), and particle swarm optimization has been developed to predict and optimize the maximum CNG-DDF engine parameter outputs (cylinder pressure, maximum thermal efficiency, and minimum carbon monoxide) (PSO). To begin with, the DDF engine parameter input was carried out in order to generate experimental data by varying the values of the pilot injection time (Tpi), CNG injection timing (TiCNG), supercharger voltage (Vsp), and injection timing (Ti). Next, a three-replication orthogonal array L25 was used to generate seventy-five experimental data points. Then, using training, testing, and validation, BPNN created a fitness function using the experimental data. The best DDF engine performance was then determined by GA and PSO using the fitness function developed by BPNN. The optimization findings indicated that PSO outperformed GA, with the best cost of PSO being 25% more expensive than the GA outcome. [ABSTRACT FROM AUTHOR]

An approach to obtain autoregressive models for generating synthetic time series in a single point with a pre-defined von Kármán spectrum is presented. Statistical stationarity is assumed. Under the premise of the proposed approach a theoretical expression for the power spectral density of the autoregressive model is used to obtain the regression coefficients. This approach is proposed to avoid the aliasing effect which may appear when the autoregressive model is obtained to reproduce a given target autocovariance function. A genetic algorithm is used to achieve an optimal autoregressive model. This approach is compared to the approach proposed by Gallego-Castillo et al. where the autocovariance function of the von kármán model is employed as a target. The theoretical autoregressive spectrum obtained using the proposed approach is not affected by aliasing effect, however, it can also present overestimation of the spectrum values at high frequencies in certain circumstances explained in the present work. The proposed approach could facilitate the use of autoregressive models to generate synthetic wind velocity time series when the target statistical information is defined in the frequency domain, as it is the usual case with atmospheric turbulence models. [ABSTRACT FROM AUTHOR]

Manufacturing industries are business driven and profits are generated by increasing annual revenue and reducing total manufacturing costs. The cost involves multiple resources such as raw materials, manpower, equipment and even manufacturing time. Thus, every manufacturing process from the frontline to the backline must run up to the maximum capacity and effectiveness without compromising products' yield and quality. End milling is one of the crucial processes to produce geometry shape products mainly in the automotive and aerospace industries. Therefore, this paper aims to obtain optimum conditions of ethe nd milling process for three cutting inserts with multi-objective parameters using a combination of mathematical modelling and genetic algorithm. The responses studied are surface roughness, cutting temperature, cutting force and flank wear. The target is to obtain the lowest value of all the responses studied by considering both input and response parameters simultaneously at one time. The process involved multi parameters and responses, thus in this study, multi-objective optimization genetic algorithms (MOGA-II) were applied. The optimization process parameters of end milling were obtained using response surface methodology, mathematical models and the MOGA-II approach. The optimum parameters were determined according to the design flow, constraints value and mathematical algorithm. Based on MOGA-II analysis, every workflow generated 1600 feasible solutions for optimization that meet the design space requirement. However, only a final solution was selected according to the multi-objective optimization of each insert used in the experiments. Subsequently, multi-criteria decision-making is required to choose the final optimization of the machining performance. Based on the parallel coordinates plot in MOGA-II and the multi-criteria decision-making approach, the final iteration number representing a single combination of optimum parameters was obtained for each cutting insert. The results of end milling process parameters with optimised machining conditions are presented and discussed. In the confirmation analysis, all the results are less than 10% of marginal error, thus, indicating that the model that was developed for the response studied is reasonably accurate. All the actual values are within a 95% prediction interval. Therefore, it can be concluded that the process was optimized which regards the lowest value obtained for the responses studied. In addition, the process was enhanced significantly with a combination of the MQL technique and the application of tri-hybrid nanofluids in end milling even for the low-cost cutting insert like uncoated tungsten carbide. For future study, other methods or algorithms can be applied in other machining processes to obtain optimum machining parameters. [ABSTRACT FROM AUTHOR]

As the human population grows and demands more resources, environmental protection is becoming a priority. To support the achievement of sustainable design and eliminate environmental problems, the decision of designers on the selection of the right materials and joining methods are two crucial processes in product design. This is important to ensure the product can be easily recycled at the end of its life. The aim of this study is to develop an eco-design tool for co-selection of materials and deciding the best approach for joining methods considering the evaluation of environmental impact. The designed tool was developed by employing genetic algorithms (GA) to optimize the process of material and joining method selection. A case study was performed to validate the applicability of the designed tool. The result showed that the developed design tool can be used to select both identical and dissimilar materials and joining methods. This article also discusses the study's consequences and recommendations for designers to accelerate the product design process considering the environmental impact. [ABSTRACT FROM AUTHOR]

The robotic manipulator system has demonstrated its efficacy in a variety of fields. As a result, it is essential to design an efficient controller for managing the end position of a robotic arm. Due to the rising nonlinearity and complexity of robotic manipulator systems, a typical proportional-integral-derivative controller has proven ineffective. In recent years, intelligent techniques such as fuzzy logic, neural networks, and optimization algorithms have emerged as a potent tool for managing extremely complex nonlinear processes with uncertain dynamics. In this study, a PID Controller and a Fuzzy Proportional-Integral-Derivative controller with parameters generated by a Genetic Algorithm using Absolute Error Integral as the goal function are created. In this paper, simulated output results of plants controlled by a Fuzzy ProportionalIntegralDerivative controller are presented, and the superiority of the implemented controller is demonstrated through a comparison with a conventional Proportional-Integral-Derivative controller using the Genetic Algorithm optimization technique. [ABSTRACT FROM AUTHOR]

The best switching angles are chosen in this study with Artificial Bee Colony (ABC), Genetic algorithms (GA), and Gray wolf algorithm (GWO), slime moiled algorithm (SMA), Whale optimization algorithm (WOA). These angles are selected according to the lowest total harmonic distortion of output load voltage from reduced switches multilevel inverter. These algorithms are used in a hybrid method to solve the transcendental equations for determining switching angles. A 27-level inverter fed by isolated unequal PV panel as DC sources with reduced switches and sources is chosen for this study. Theoretical analysis and Simulation are accomplished using Matlab/Simulink for 27 level reduced switches multilevel inverter. The simulated results validated the practical outcomes. [ABSTRACT FROM AUTHOR]

An algebraic relationship between functions and their derivatives is all that a differential equation (DE) is. These mathematical constructs give scientists the ability to comprehend a variety of intricate occurrences. In this essay, we provide an overview of a few techniques for solving differential equations. The Turing Machine, Genetic Algorithm, and Quantum Computing are the techniques used. [ABSTRACT FROM AUTHOR]

NOMA that means Non-Orthogonal Multiple Access is technique which considered promising for next-generation communication systems due to its high spectrum efficiency. In the uplink architectures based on NOMA, an optimization issue for maximizing the possible sum rate while ensuring users' Quality of Service (QoS) and restricting total power is addressed. This study proposes an energy allocation strategy utilizing a genetic algorithm (GA) to handle the nonlinear power allocation problem. By employing various target functions and optimization criteria, multiple power allocation coefficients can be generated. The focus is on maximizing the SE-EE (Spectral Efficiency and Energy Efficiency) tradeoff for multi clusters, multi-user applications in systems. In this work, the power allocation optimization considers each user's minimum rate requirements and power budget. The subsequent analysis aims to optimize each cluster's SE-EE tradeoff. Simulation outcomes demonstrate that our proposed strategy outperforms previous solutions in terms of the SE-EE tradeoff. [ABSTRACT FROM AUTHOR]

The detection of image edges is a major challenge in image processing and computer vision, since it needs to dispense of unnecessary pixels while preserving significant ones which indicate the image's structure. An optimized algorithm for image edge detection is introduced in this study, which uses a Genetic Optimization (GO) Algorithm to seek out sources of the maximum pixel value to optimally allocate the image edges. The algorithm simulates the evaluation of various values and consists of three operations: selection, crossover operations, and thresholding the optimized value. The community is made up of these evaluations which are conducted at each stage to select the fittest individuals and eliminate the less fit ones. By repeating this process, GO eventually identifies the image edges and eliminates non-edge pixels. The results of the study show that the proposed algorithm successfully addresses the challenges associated with image edge detection. The limitations of the research are also discussed, and potential areas for future research are suggested. This algorithm is expected to be useful for solving complex problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose using the wave finite element method and genetic algorithms to design periodic waveguide structures with optimal stop-bands. Instead of modelling waveguides using the standard finite element method, we use the wave finite element method to model the waveguides. The wave and finite element method is based on using periodic structure theory and the finite element model of a unit cell of the waveguide to describe the waveguide's motion in terms of wave amplitudes rather than in terms of physical degrees of freedom. This results in a numerically efficient model of the waveguide which can be used in optimisation studies. In particular, the focus of this paper is on the optimisation of stop-bands in the waveguides which leverage this property of periodic structures. Genetic algorithms will be used to optimise the stop-bands. A numerical example where analytical models can be obtained will be used to demonstrate the efficacy of the proposed approach and its potential. [ABSTRACT FROM AUTHOR]

Radial basis function neural network (RBFNN) is an artificial feedforward neural network that uses radial basis functions as activation functions in the hidden layer. The output of the RBFNN is a linear combination of the outputs of the hidden layer. This paper presents a multi-objective model of radial basis function neural networks for training. This model aims to satisfy two objectives: the first is the sum of all distances between the input vector and the corresponding center for the selected neurons in the hidden layer and the second is the overall error of the RBFNN, which is defined as the error between the computed output and the expected output. To solve this model, we will use an approach based on genetic algorithms, which allows us to determine an appropriate partitioning of the input data and the optimal weight of the output layer that gives us a good generalization. Numerical results show the performance of the theoretical results presented in this paper, as well as the advantages of the new modeling. [ABSTRACT FROM AUTHOR]

Vehicle routing problems deal with determining the routing of a fleet of vehicles under a set of constraints to serve geographically dispersed customers. The problems belong to a subset of combinatorial optimization problems and are widely studied due to their academic appeal and numerous applications. The classical version of this subject usually minimizes a single objective in total distance or total cost. However, given that many real-life problems are inherently multi-objective, a variant that considers multiple objectives is getting more attention nowadays. The latest review in this area was in 2008 and more than a decade has now elapsed with the absence of similar study. The objectives of this paper are to summarize the selected new research of multi-objective vehicle routing problems that span beyond 2008 and to propose a taxonomy that can be used to categorize the studies in this area. The proposed taxonomy includes eight criteria covering identification and characteristics of the papers. The findings from the review suggest tendencies toward certain scopes such as time windows formulation (VRPTW), the development of population-based algorithms especially the genetic algorithm and elitist non-dominated sorting genetic algorithm (NSGA-II), and the use of Solomon benchmark instances in the numerical experiment. [ABSTRACT FROM AUTHOR]

Optimization algorithms, in Autonomous Driving (AD), have been proven to be effective in decision-making on most plausible safety-critical scenarios. However, state-of-art AI systems fail to minimise the loss below the critical safety protocol. Genetic Algorithms (GAs) are state-of-art optimization algorithms to efficiently solve a search problem. In this paper, we discuss and review previous works on applying GAs to AD's optimization problems such as path estimation, collision avoidance, synthesizing test data etc individually. Additionally, we attempt to propose end-to-end GAs to optimize the whole pipeline of AD research and production at scale. The end-to-end GA optimization of AD, through efficient ensemble technique, would be capable of delivering better results both in simulation and in the field since an end-to-end approach can take advantage of the availability of complete data throughout the pipeline. This approach could streamline the individual tasks into an automated process reducing the number of activations between the processes. Earlier, efforts have been made majorly on confined tasks such as path estimation, collision avoidance and etc which are related to a single ultimate problem of learning to drive. Hence, an end-to-end approach enables models to learn the supreme task of safe navigation which includes path estimation, collision avoidance, and efficient applications of sudden breaks. A novel technique named "real-time reward-based" training of AD cars is proposed to resolve the issue of AD systems trained in the lab, failing to generalize upon deployment. [ABSTRACT FROM AUTHOR]

The performance of real-time data models and their associated machine-learned trained databases are dependent on the complexity of fluid-thermal-mechanical interactions for material process applications. Additionally, sophisticated multi-dimensional search spaces for various chemical, thermal and mechanical material parameters are promoting critical challenges for the quick predictions and suitable decision making. The purpose of this research work is to investigate the performance of real-time models for thermal-mechanical material processes in terms of accuracy and generality. The objectives are to study the issues of optimized data structures and best algorithms to determine the best data training and query optimization techniques including neural network (NN) and genetic algorithm symbolic regression (GASR). The formation of effective snapshot matrices and their size\quality for real-time models being created are also considered for the case of best stored information. These matrices are designed to capture the most important aspects of the processing parameters including rate of changes, initial conditions and equipment's limits. Furthermore, the accuracy and reliability of these real-time models for additive manufacturing and extrusion processes are investigated using real-world case studies. [ABSTRACT FROM AUTHOR]

Mathematics is one of the sciences whose application can be used in solving distribution problems. The study of graph theory, especially on the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) material, has a special problem, namely that demand and return of goods are carried out simultaneously. The focus of this article is the study of the VNS-GA Hybrid algorithm used in solving VRPSDP. The main stage consists of two stages, namely the initial stage of forming a solution using the Genetic Algorithm and the stage of improving the solution using the Variable Neighborhood Search (VNS) Algorithm. The implementation of the algorithm uses the Borland Delphi 7.0 programming language. Program inputs are vehicle capacity, population size (popsize), crossover probability (Pc), mutation probability (Pm), number of iterations, maximum number of iterations of the disturbance phase. The output produced by the program is the route formed along with the total distance and graphic visualization. The program has been tested using 8, 20, 40, 75 customers and a standard dataset, namely 100 customers (c101 and r101) and 200 customers (c1_2_1 and r1_2_1). Among the standardized datasets, the smallest gap value is dataset c101 with a gap value of 2.8 % from the Tabu Search algorithm solution. In this case, the VNS-GA Hybrid algorithm approaches the optimal results using a standard dataset. [ABSTRACT FROM AUTHOR]