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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]