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This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework's capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel method was proposed for accurate constitutive description and workability characterization of Mg alloy during hot deformation. • Combining the AT model with PSO-ANN can significantly improve its prediction accuracy and enhance the generalization ability when describing flow behavior. • The activation energy-processing map established using the PSO-ANN-AT model can exhibit superior predictive capability for microstructure evolution. Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation. [ABSTRACT FROM AUTHOR]

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach. • A novel KRC conceptual model is proposed. • A heuristic KRC identification approach based on MOGA is presented. • A KRC ranking approach based on extended RPN is proposed. • The proposed method is illustrated using an insulating base of subway locomotive example. [ABSTRACT FROM AUTHOR]

Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system. [ABSTRACT FROM AUTHOR]

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications. [ABSTRACT FROM AUTHOR]

The molds industry, being a highly competitive sector, is constantly driven by organizations looking to optimize their business processes to gain a competitive edge. To access advanced management tools, there has been a strong push for integrating Industry 4.0 technologies, which in turn has resulted in a significant increase in the amount of manufacturing process data, enabling more accurate and real-time analysis of processes. However, challenges persist due to the unpredictable and ad-hoc nature of this industry's business processes. As a result, effective resource management has become a crucial aspect that organizations are actively addressing. In this study, our objective is to leverage process mining algorithms and complement them with a genetic algorithm to generate efficient scheduling solutions to produce mold parts on factory floors. By analyzing existing data and identifying the workflows through process mining, we seek to gain a more realistic perspective on the mold manufacturing business process. Through this analysis, our aim is to uncover meaningful workflows and sequences for producing mold parts, ensuring that critical activities are appropriately incorporated while minimizing unnecessary repetitions. By identifying these purposeful sequences, we can provide the factory floor with schedules that optimize resource usage and enhance the overall efficiency of mold manufacturing processes. [ABSTRACT FROM AUTHOR]

To solve the position control problem of the induction motor with parameter perturbations, load disturbances, and modeling errors, a predefined-time position tracking optimization control method with prescribed performance is proposed. In practice, the rotor flux linkage of the induction motor can't be measured, and a predefined-time sliding mode observer (PTSMO) is applied to accurately estimate it. Additionally, predefined-time disturbance observers (PTDOs) are employed to identify the uncertainties in the motor system. The position and flux linkage controllers are then designed by integrating the predefined-time control approach with the prescribed performance function method, realizing accurate tracking control of the induction motor within a predefined time. Next, the adaptive genetic algorithm (AGA) and the improved particle swarm optimization (IPSO) technique are combined to optimize the designed controllers, enhancing the convergence rate and steady-state accuracy of the induction motor. Finally, comparative analyses through simulations and dSPACE simulated experiments validate the efficacy of the proposed control method, highlighting its applicability in practical motor systems. ● The predefined-time sliding mode observer is crafted to observe the rotor flux linkage, and the predefined-time disturbance observers are designed to estimate the system's uncertainties. This not only addresses the challenges of unmeasurable rotor flux linkage in practice, and the observation errors converge within a predefined time. ● This paper combines the predefined-time control and the prescribed performance method, ensuring both the transient and steady-state performances of the system, while also ensuring the motor states converge within a predefined time. ● This paper combines the AGA and the IPSO to generate the AGA_IPSO hybrid algorithm to optimize the primary controller parameters, which not only simplifies the tuning process of system parameters, but also enhances the system's convergence speed and steady-state accuracy.. [ABSTRACT FROM AUTHOR]

The last-mile delivery challenges and complexity are increasing simultaneously with the e-commerce growth, the e-commerce growth constitutes a threat to urban transport, especially for big cities and developing countries. This paper examines the economic impact of crowd-shipping based on public transport collaborated with the automated parcel locker and the impact of traditional delivery, as two different methods of last-mile delivery under the same circumstances. This paper aims to assess crowd-shipping as an alternative solution for last-mile delivery compared to traditional delivery. Data has been simulated based on Cairo reality, one of the largest cities in the world. The mathematical models were formulated for the problem, where the first model objective function minimizes the costs of vehicle possession and vehicle kilometers traveled, while the second model objective function minimizes the costs of establishing lockers, vehicle possession, and vehicle kilometers traveled. A genetic algorithm has been used due to the complexity of the problem. The results show that the economic impact of the crowd-shipping delivery system is better than the traditional delivery system under the conditions of large-sized and medium-sized demand, while the economic impact of traditional delivery is better than the crowd-shipping delivery system under the conditions of small-sized demand. [ABSTRACT FROM AUTHOR]

The travelling salesman problem (TSP) and its variants have been studied extensively due to its wide range of real-world applications, yet there are challenges in providing efficient algorithms to deal with some of its variants. The multiple travelling salesman problem (MTSP), is the generalization of TSP, which aims to determine m − routes for ' m ' salesmen to cover a set of n − cities exactly once where each route starts and ends at a depot such that the total distance is least. In this, the number of cities in each route of the optimal solution may be distributed disproportionately. This paper presents, a bi-objective MTSP (BMTSP) with the load balancing constraint, where the first objective is to minimize the total travel distance and the second objective minimizes the total time. A metaheuristic based genetic algorithm with tournament selection (GATS) is designed by integrating with mixed strategies, such as flip, swap and scramble in mutation operation to obtain efficient Pareto solution for BMTSP. The computational experiments are carried out on different data sets, which are derived from the TSPLIB. The performance of GATS is compared with different genetic approaches and simulation results show that the proposed GATS obtained improved solutions on some of the benchmark instances. [ABSTRACT FROM AUTHOR]

The increased internet usage after the pandemic led the UN Forum to improve cybersecurity measures, with zero-knowledge proofs (ZKP) being a viable solution for securing confidential information. ZKP protocols can be demonstrated through the binary puzzle, an NP-complete logic puzzle with four specific constraints. The key contribution of this paper is its successful implementation of the genetic algorithm as a new method to solve the binary puzzle. The optimized fitness function determined the solution at an average of 1.33-2.33 generations for populations ranging from 100 to 500. Its quadratic property calculated the solution faster than the ordinary linear fitness function. [ABSTRACT FROM AUTHOR]

This paper presents the development of a web-based course timetabling system based on an enhanced genetic algorithm. The enhanced method utilizes a heuristic mutation which concentrates on mutating the infeasible genes to improve the algorithms' exploration and exploitation capability. The method was implemented using a free and open-source application and can be accessed online. Based on the actual datasets from Caraga State University, the enhanced method optimized the use of classroom resources by using a smaller number of rooms. The generated timetable is more efficient as it satisfies not just hard constraints, which are conflicting schedules, but also soft constraints. [ABSTRACT FROM AUTHOR]

Product distribution for companies in the industrial sector is an important issue as it is necessary to increase customer satisfaction and reduce costs. In this study, Nondominated-Sorting Genetic Algorithm II is used to solve Capacitated Vehicle Routing Problem in a case study dataset from the biggest fertilizer company in Indonesia. NSGA-II algorithm reduced the need for vehicles in product distribution with an advantage of 44.7%, while GWO algorithm yielded better results with an increase of time of 16.9%. [ABSTRACT FROM AUTHOR]

Sustainable management of water distribution networks (WDNs) is essential to ensure the continuous supply of water. However, the water pipes in WDNs often experience unprecedented failure, which causes disruption in services, flooding, increased maintenance costs, and reduced water quality. Although researchers have developed models to predict the failure of water pipes, the literature lacks fully optimized and robust models. Therefore, this study proposes a new methodology to develop optimized models for predicting the failure probability of water pipes by fusing logistic regression with genetic algorithms. The methodology was applied to the data of the Hong Kong WDN, and experiments were conducted to optimize the hyperparameters and features of logistic regression models. The performance of the proposed methodology is evaluated using five key metrics: accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). The results show significant improvement over conventional approaches, with the best model achieving an F1 score of 0.868 and an AUC of 0.944. These results show that the model can effectively predict the failure probability of water pipes. The relative contribution of each feature to the model's outcome was investigated using the SHapley Additive exPlanations. Additionally, a web application based on the proposed methodology in this study was developed for Hong Kong that other water utility management can benefit from, which can facilitate reliable decision-making for the management of WDNs. [ABSTRACT FROM AUTHOR]

Bundle AI is a marketing tool project motivated to provide solutions for manual product bundling, a popular marketing strategy that boosts sales of low-selling products and encourages mass purchases with promising customer satisfaction. It is a cost-effective solution as it does not depend on large volumes of transactional data, which is suitable for small businesses. The bundling of products was defined as a Multiple Constraints Knapsack Problem where the best solution is determined using Genetic Algorithm. Bundle AI delivers the fit product bundle with the incorporation of the value and reward system. [ABSTRACT FROM AUTHOR]

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

An Inertial Navigation System (INS) is a standalone system that provides continuous navigation information on the position, velocity, and attitude in various conditions. Gyroscopes and accelerometers are essential components of INS. However, these sensors are prone to errors that accumulate over time and degrade the accuracy of the INS. Therefore, enhancing the performance of INS is a challenging problem. This paper aims to address this problem by proposing a method to reduce errors in real-time processing. The method uses Lifting Wavelet Transform (LWT), a popular denoising technique that belongs to the wavelet family and has an advantage over the classic wavelets in terms of time and computation complexity. Moreover, the method employs an optimization technique based on the Genetic algorithm as an intelligent averaging for combining different multi-level LWTs, which significantly improves the accuracy and efficiency of INSs. Finally, a new and implementable structure is introduced for denoising inertial sensors. The results show that the proposed method achieves an 83% and 59% improvement in gyroscope and accelerometer data for dynamic data, compared with raw data. Besides, for static data, there are 71% and 36% improvements in gyroscope and accelerometer data, respectively. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

Partial differential equations (PDEs) are essential mathematical models for describing a wide range of physical phenomena. Numerically, Physic-Informed Neural Networks (PINNs), a variant of artificial neural networks, present a promising method for solving PDEs. However, due to limitation in accuracy and stability, various adaptive PINN variants have been proposed. We have designed a novel approach that adopted self-adaptive PINN (SA-PINN) with two optimization techniques: the genetic algorithm (GA) and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Self-adaptive PINN modifies the weights in the loss function to be fully trainable, enabling the ANN to learn and stabilize the PINN in approximating the difficult regions of the solution. GA initializes the population of ANN trainable parameters to optimize the training process with less number of iterations, while L-BFGS is used to find the best solution accurately. Our proposed approach, named SA-PINN-GA-LBFGS, is tested on solving several benchmark PDE problems including elliptic, parabolic, and hyperbolic types. We compare our results with state-of-the-art methods, demonstrating that SA-PINN-GA-LBFGS provides higher accuracy and greater efficiency. [ABSTRACT FROM AUTHOR]

The quadrotor represents a class of highly nonlinear dynamic systems and its controllability features are challenging. Hence, it serves as an ideal unmanned aerial vehicle platform to validate many artificial intelligence-based research investigations. Nonetheless, most of the offline tuning approaches devote efforts to find near optimal control gains to regulate individually the decoupled motion directions. This work adopts a multi-objective self-adjusting search mechanism to actuate the motions of a quadrotor via deciding the control gains of the interacting loops simultaneously. This algorithm employs a first order low pass filter transfer function as an accepting approach for the tunning mechanism. The proposed approach is compared with a Genetic Algorithm and another nonlinear Proportional-Integral-Derivative approach to highlight the usefulness of the proposed mechanism. It was founded that quadrotor follows the desired trajectory with a small tracking error of less than 2% in the X-Y plane and less than 1 % error tracking error in the altitude Z. Also, it is recorded that MONLTA can overcome the simulated wind disturbances of 0.1 N.m as a disturbance torque. [ABSTRACT FROM AUTHOR]

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods. [ABSTRACT FROM AUTHOR]

Recently, transformer models have gained interest with their varied range of natural language applications in several domains like education, healthcare, entertainment, and the like. Design of inquisitive bots which ask questions on a given topic of interest is one such application. To improve the quality of questions generated by such a bot while leveraging the power of transformer models, it is necessary to add a component to the pipeline that also considers the topic of discussion. This research examines transformer models pre-trained on natural language with the addition of topic context and subsequent optimization using genetic algorithms to generate more relevant questions. In our hybrid framework, we first use topic modeling for extracting context from the conversation history and then use these topics as input in the conversational training pipeline for generating questions on the given category. We attempt to fine-tune the framework to find the optimized weights using genetic algorithm. Our experiments are carried out using a large-scale dataset that is readily available to the public for the purpose of conversational question generation. We further evaluate the trained models on a dataset of Java passages performing qualitative analysis. Our analysis suggests that utilizing the genetic algorithm-optimized topic aware mechanism improves the quality of generated questions substantially in the conversational set-up. [ABSTRACT FROM AUTHOR]

Effective project planning is crucial for the successful execution of complex industrial projects, as it can significantly reduce costs and enhance overall efficiency. However, applying precise methods for obtaining optimal project plans is often limited to small activity graphs, rendering them impractical for real-world scenarios. To address this issue, heuristic and metaheuristic algorithms have been the focus of extensive research over the past few decades. This study explores an extended problem formulation known as the Multiple Contractor-Resource Project Scheduling Problem (MC-MRCPSP). This formulation extends beyond the basic problem structure, incorporating various constraints typically encountered in industrial project scheduling. To tackle this challenging optimization problem, we propose a novel hybrid algorithm that combines a fine-tuned genetic algorithm with an advanced tabu search. This approach strikes an optimal balance between exploration and exploitation of the solution space, leading to enhanced project plan optimization. Through comprehensive experiments and evaluations, we demonstrate the effectiveness of our proposed approach. Compared to a purely genetic algorithm, our hybrid solution achieves a 38% improvement in project plan optimization. These results underscore our study's significant contribution to project planning and lay the groundwork for future advancements in MC-MRCPSP solutions. Ultimately, our research brings us closer to improved resource utilization, cost reduction, and enhanced efficiency in managing complex industrial projects. [ABSTRACT FROM AUTHOR]

Fast Flux Botnet (FFB) is an advance method developed by cyber criminals to perpetrate distributed malicious attacks. The major problems of existing FFB detection systems are the vulnerability to evasion mechanisms, long detection time, and high dimensionality of the feature set. In this study, an improved FFB detection architecture called Bot-FFX was developed to address some of these problems. The developed Bot-FFX consists of four modules: extractor, filter, resolver, and detector. The extractor module is responsible for Domain Name System (DNS) queries on domains. The filter module can classify the incoming domains as either blacklist or whitelist and sends the unclassified domains to the resolver. The resolver extracts all IP addresses associated with the domain at its Time-To-Live (TTL) within a time frame of 10 min. The detector module uses a rule-based Genetic Algorithm (GA) and K-Nearest Neighbor (KNN) for botnet detection. The detector computed the Standard Deviation of Round Trip Time (SDRTT), Average Google Hits (AGH) and Genetic Threshold Value (GTV) for all IP addresses associated with the domains. The detector, built on a decision tree rules and the K-Dimensional (KD) tree KNN algorithm, classified the domains using the set of IP addresses, SDRTT, AGH, and GTV. The Bot-FFX was implemented on a dataset of 2,000 benign domains and 1,630 botnet domains. The dataset was split into 50% training and 50% testing sets. The evaluation results on the same datasets showed that Bot-FFX is an effective FFB detection system with accuracy, false positive, and false negative of 99.178%, 0.8%, and 0.8% respectively. [ABSTRACT FROM AUTHOR]

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

Packaging design, as an important element in product appearance, can directly affect customers' sensory perception of the product. Many universities even offer packaging design majors, which mainly use natural science and aesthetic knowledge to promote product sales. However, many old brands remain complacent and their packaging design still adopts traditional thinking, which to some extent affects their sales. Therefore, this article decided to use genetic algorithms as a tool to parameterize the 3D fractal images in packaging design, aiming to create more creative and eye-catching packaging designs. At the end of this article, an experiment was conducted on two branches of a certain brand. Branch 1 tried out the new design provided in this article, while Branch 2 continued to use the original design. After Branch 1 fully adopted the design, sales skyrocketed, from the original daily sales of 50-60 units to 70-85 units. Branch 2 remained unchanged, with a sharp contrast. [ABSTRACT FROM AUTHOR]

With the widespread use of substation intelligent simulation systems, existing substation intelligent simulation systems have problems such as platform redundancy, system dispersion, and information sharing, which can lead to the insecurity of substation intelligent simulation systems. Therefore, this paper proposes a security operation analysis of substation intelligent simulation system based on improved genetic algorithms, aiming to improve the security of substation intelligent simulation system by using improved genetic algorithms. This paper experimentally tests that the number of vulnerabilities in a substation intelligent simulation system using improved genetic algorithms is between 0 and 20 within a month, while the number of vulnerabilities in a traditional substation intelligent simulation system is between 30 and 50, indicating that improved genetic algorithms do have the effect of improving the safety coefficient of the substation intelligent simulation system. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

To achieve the goal of exploiting all the advantages and avoiding the disadvantages, this paper presents a genetic algorithm-based development to integrate the spider colony algorithm for teaching optimization of English data recommendation methods. This algorithm first takes advantage of the genetic algorithm to extract the pheromone required by the ant colony algorithm, and then obtains a good solution through the fast and comprehensive integration mechanism of the global optimization of the ant colony algorithm. Therefore, combining the two algorithms can increase the processing time and efficiency. According to the experimental results, compared to using the genetic algorithm or the ant colony algorithm, the violation of the soft parameters of the training data based on the genetic ant hybrid algorithm is reduced to a minimum, and the feasibility and quality of the training data are good. alone. The power of our algorithm is directly proportional to the number of assignments. The security of the algorithm seems to be stable when the number of scales reaches one. For the same scale, the power of the hybrid genetic colony algorithm is the highest, the ant colony algorithm is the second, and the genetic algorithm is the last. When the class setting is 500, when the motor reaches 300, the algorithm stops working and the test is repeated 10 times. The genetic ant colony hybrid algorithm needs the shortest time to reach the fitness value. Conclusion: this algorithm is much better than genetic algorithm or spider colony algorithm. [ABSTRACT FROM AUTHOR]

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path. • A method of simultaneous trajectory generation and path optimization is proposed. • Jerk limited acceleration profiles describe the trajectory. • The NSGA II is used to find the trade-off between time and energy consumption. • The best trade-off solution achieves time reduction and energy savings of 10.05% and 2.10%. • Optimized path motion with variable velocities reduces maximum error by 76.6%. [ABSTRACT FROM AUTHOR]

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

Hyper-heuristics can be applied to solve complex optimization problems. However, they need a substantial number of fitness function evaluations to discover a good approximation to the global optimum, especially for large-scale problems. Recently, surrogate-assisted algorithms have drawn increasing attention, and have shown their potential to deal with expensive complex optimization problems. This paper aims to use surrogates to approximate HHGA's (Ahmed Bacha et al., 2019) fitness functions, an efficient hyper-heuristic for solving the permutation flowshop problem, one of the most important scheduling types in modern industries. The objective is to approximate, in an online approach, the fitness function, reducing considerably the execution time of HHGA while maintaining its quality. The proposed online surrogate model is mainly designed to capture the details of the fitness function to enhance the accuracy estimation. The experimental results on Taillard's widely used benchmark problems show that the proposed fitness approximation-assisted HHGA is able to achieve competitive performance on a limited computational budget. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the results of two hyper-heuristics HHGA and HHabs that generate per-instances genetic algorithms for the permutation flow shop problem. They are competitive with literature approaches for most of instances of the benchmark of Taillard. Nevertheless, they are not effective enough for some difficult instances. For this purpose, we propose a workflow to analyse GAs configurations and their results in order to detect which components influence the most on generated GAs quality in order to enhance the quality of these hyper-heuristics. [ABSTRACT FROM AUTHOR]

In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time. [ABSTRACT FROM AUTHOR]

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations. [ABSTRACT FROM AUTHOR]

Granting credit involves certain risks and thus financial institutions try to minimize them using various methods. The aim of this article is to develop a method of selecting borrowers' characteristics for credit risk assessment. The article uses non-genetic algorithms (such as CFS, ReliefF or Lasso), that perform calculations for all features, with only financial institutions granting the loan selecting the significant features to be assessed. The Holland and NSGA II genetic algorithms also begin with all features and gradually reduce their number in the consequent iterations without compromising the accuracy of feature selection. Only the GAAM genetic algorithm gives the lender the ability to choose the number of features entered into the calculations. During the operation, this algorithm increases the accuracy of feature selection by reducing their number. This significantly shortens the time to analyse loan application while maintaining acceptable accuracy, which results in making the right decision quickly. The features selected by the algorithms are additionally classified by the LDA classifier to demonstrate the correct selection of features by a given algorithm compared to the benchmark, which is the actual percentage of credits granted and then repaid. [ABSTRACT FROM AUTHOR]

The string site-removal problem (SSRP) originates from the field of computational biology and is known to be NP-hard. The problem is defined on the set of strings of equal length and an integer k, and its goal is to find the largest subset of the strings that become identical after removing at most k sites (columns). This paper proposes two new approaches to SSRP. The first is an exact algorithm that uses the quadratically constrained program (QCP). The second approach combines QCP and genetic algorithm (GA). In a series of experiments, we showed that our exact program performs faster on average than the state-of-the-art method (Integer Linear Programming, ILP). Regarding heuristic approaches, our new proposal gives the identical results as the state-of-the-art reference method (Large Neighborhood Search, LNS) with the same CPU time budget. All source codes and benchmark instances we made publicly available via GitHub. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) systems have, over the past decade, shifted from using rule-based techniques to using machine learning-based algorithms. This has led to the development of different architectures and models for different tasks. Some of these architectures include models like the transformers, the CNN and the RNN, which have now become ubiquitous in NLP. However, designing these neural network architectures usually requires in-depth analysis and knowledge of multiple domain areas involved with the problem at hand. In our work, we evaluate an alternative solution to this problem in the domain of text classification. Here, we suggest using the Genetic Algorithm with gradient descent (GAGD) and NeuroEvolution of Augmenting Topologies (NEAT) to search for an optimal neural architecture for the Reuters-21578 and 20 Newsgroups datasets. We evaluate and compare the results of the two algorithms against the current state-of-the-art architectures and provide insight into their performance. [ABSTRACT FROM AUTHOR]

The performance and power of Wavelet Neural Networks (WNNs) rely on the proper structure of the WNN. In this study, a hybrid approach is suggested to build a Wavelet Neural Network. We design a network based on the genetic algorithm (GA), the Gram-Schmidt Selection (GSS) method, and the multilibrary wavelet function (MLWF). The initialization of the WNN is performed by the GSS method, which aims to select the candidate library wavelet functions (MLWF) to develop the WNN. The GA is used to solve the structure and the learning of the WNN and the GSS algorithm is applied to select the important wavelets. The GA was used to calculate the suitable values of the Network parameters, to solve the structure and learning of the WNN. In this research, the GSS method is used to determine a set of best wavelets whose centres and dilation parameters are used as initial values for subsequent training. Experimental tests proved that the proposed approach is very efficient and accurate. [ABSTRACT FROM AUTHOR]

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application. [ABSTRACT FROM AUTHOR]

This paper proposed an improved jellyfish Search (ImpJS) technique for torque ripple minimization on CUK converter based BLDC motor. In this paper, crossover and mutation operator are utilized to improve searching behavior of the JS algorithm. Hence, it is named as improved jellyfish algorithm (ImpJS). At first, BLDC motor is considered along with the Cuk converter is improved through the switched inductor. Simultaneously, the execution of the BLDC motor operation includes speed and torque control strategy is also analyzed. In order to improve these two strategies, the proposed ImpJS system is introduced. The best gain parameter is tuned to upgrade the controller operation considering the objective function. Finally, the proposed technique-based BLDC motor is performed on MATLAB/Simulink platform in order to analyze the performance is compared with other existing system to determine the effectiveness. The existing techniques like particle swarm optimization (PSO), ant lion optimizer (ALO) and salp swarm algorithm (SSA). In case 1 and case 2, the MSE for proposed technique achieves the value of 0.01093 and 0.01095. In case 1 and case 2, the voltage deviation for the proposed system achieves the value of 2 and 2 • An improved technique for torque ripple minimization in cuk converter based BLDC motor. • Crossover and mutation operator are utilized to improve the JS algorithm. • Execution of the BLDC Motor operation includes speed and torque control. • Best gain parameter is tuned to upgrade the controller operation. [ABSTRACT FROM AUTHOR]

The dichromatic number of a digraph D is the minimum number of colors of a vertex coloring of D such that D has no monochromatic cycles, the dichromatic number is an extension of the chromatic number to the class of digraphs. The Hajós join is a tool to obtain r -chromatic graphs, using the dichromatic number, J. Bang-Jensen et. al. extended the Hajós join to digraphs, and thus obtained a tool to obtain r-dichromatic digraphs. J. Bang-Jensen et. al. posed in 2020 the problem of how to obtain the symmetric cycle of length 5 from symmetric cycles of length 3. We recently solved this problem by applying a genetic algorithm. In this article, we generalize the construction of the symmetric cycle of length 5 and determine the computational complexity as Θ(n ln(n)). [ABSTRACT FROM AUTHOR]

Machine learning and stochastic optimization approaches are commonly used in wire electrical discharge machining (WEDM) of specific Ni-based superalloys and required separate optimization approaches for materials within the same group of superalloys, which is a time-consuming process due to the complicated parametric correlations. The most likely solution is a multi-material model that can deal with multiple Ni-based superalloys, which is uncommon in published literature. The current study aims to develop genetic algorithm (GA) and particle swarm optimization (PSO) integrated multi-objective multi-layer neural network (MOMLNN) models for multi-material optimization to achieve efficient strategies for multi-material systems. Six process variables, seven compositional elements and four response variables are used to generate MOMLNN with 13–300–300–4 architecture integrated with GA and PSO techniques to identify the optimum parameters for simultaneous WEDM of IN625 and IN718. It is observed that the GA integrated model has a slightly higher accuracy than the PSO integrated model. The material removal rate (MRR) and kerf width (KW) increases with increase in T on and A on. The surface roughness (Ra) decreases with decrease in A on and increase in WF. The recast layer thickness (RLT) increases with increase in A off. Changes in Cr, Mo, and Si among the alloying elements have a positive impact on MRR, KW, and Ra, while changes in Fe, Nb, Ti, and Al mainly govern the RLT. The optimum responses, obtained from GA integrated model are validated with experimental results and the error fraction is generally less than 5%, indicating good predictive accuracy of the model. [Display omitted] [ABSTRACT FROM AUTHOR]

Redundant structure has been widely deployed to improve system reliability, as when one unit fails, the system can continue to function by using another one. Most existing studies rely on the similar assumption that the heterogeneous units are subject to periodic inspections and identical in terms of their aging situations and the numbers of resisted shocks. In practice, it is often adequate to trigger a unit individually in the event of a single shock, which intensifies the degradation of that unit, accordingly, requiring a sooner inspection to ensure its safety. In this study, the stochastic dependency among units is addressed firstly by introducing a novel activation sequence. Secondly, an adaptive system-level inspection policy is proposed by prioritizing the unit with a worse state. Finally, we take advantage of Monte Carlo methods to simulate the whole process and estimate two objectives, referring to the average system unavailability and maintenance cost, in a designed service time. It is found that the two objectives are contradictory through numerical examples. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, therefore, has been employed to find the optimal solutions in system unavailability and cost, which provide clues for practitioners in decision-making. • Modeling and quantifying the dependence of units in redundant structure caused by the activation sequence in withstanding random shocks. • Merging the unit-level inspection and system-level inspection into an adaptive and aperiodic testing policy. • Multi-objective optimization in seeking the optimal inspection and maintenance strategies. [ABSTRACT FROM AUTHOR]

Distracting while driving is a serious issue that causes serious direct and indirect harm to the society. To avoid these problems, detecting dangerous drivers' behaviour is very important.This research focuses on detecting driver behaviour with a combination of artificial deep learning and machine learning models with genetic algorithm. Most of the previous works have focused on using convolutional neural network as deep learning model or support vector machine as machine learning model for actions detection of drivers from input images. The proposed structure will use genetic algorithms to first choose the structure of feature extractor from famous CNN models such as VGG19, ResNet50, and DenseNet121. After mentioning feature extractor, proposed framework contains two layer of dense layer for classification as a deep learning model. On the machine learning side K nearest neighbor, random forest, support vector machine, and extreme boost algorithms have been used as classifiers. Genetic algorithms will specify number of neurons and activation functions of these layers for deep learning and hyperparameters such as number of estimators for machine learning models. Proposed model has been developed with the use of state farm dataset that contains information of 1 safe driving class and 9 dangerous behaviours such as texting while driving, talking with passengers, drinking, etc. Experimental results indicate 99.80% accuracy for classification of the state farm distracted driver detection with combination of genetic algorithms and deep neural networks. Compared to similar research, the proposed approach has shown superior results for classification of state farm distracted driver detection. Proposed approach chooses the feature extraction model and hyperparameters of the classification layer automatically. Thus it can be used for driving behaviour classification with seeing new situation too. Proposed framework can be used as a real time driver's distraction detection to decrease car traffic accidents and alleviate corresponding damages to the drivers. [ABSTRACT FROM AUTHOR]

Given the optimization problem of agriculture products logistics distribution path in the city, the demand of retail terminals is uncertain because consumer demand is affected by environmental and social factors, and the agriculture products urban distribution optimization model based on the determined demand cannot effectively solve the distribution problem with uncertain demand. Based on the demand determination model, this paper constructs a robust optimization research model based on scenario sets and reallocates distribution paths according to the change of demand. A robust optimization model of the agriculture products urban distribution path under multiple scenarios is built on the basis of meeting the mixing time window and using the objective function of total cost agriculture products urban distribution optimization model. To solve the problem, an improved genetic algorithm was designed by introducing elite retention strategy and migration strategy, which improved the defect of the traditional genetic algorithm which was easy to precocious convergence. Compared with the genetic algorithm, the proposed algorithm achieved the minimum total cost in all scenarios, the average total cost was reduced, the average convergence time of the algorithm was reduced by 5%, and the average total vehicle running distance was reduced by 3%. [ABSTRACT FROM AUTHOR]

The signature is a behavioral-based human characteristics that is extensively used as legal evidence of identification on bank checks, credit cards, and wills. Developing a good offline signature verification (OSV) system is essential to avoid fraud. Due to the plurality of signature styles and sample contexts, feature extraction (FE) is challenging for OSV systems. Current signature verification methods present promising results in distinguishing between genuine and forged signatures, but they are far from satisfactory. Regarding feature selection (FS), stable and appropriate features substantially impact signature verification. The issue with current approaches is that they apply combination features without addressing correlation and discriminant. A genetic algorithm (GA) is popularly deployed as a method for optimization to identify the best features. Regrettably, GA has drawbacks when the search dimension is expanded, such as a lack of comprehensive exploitation, difficulties in achieving good convergence, and therefore increased computation. This study addresses the aforementioned challenges by introducing multifeature fusion and discriminant FS using a novel quantum inspired GA (QIGA). QIGAs are constructed using quantum computing concepts such as qubits (Q-bits) and superposition of states. QIGA is able to express a linear superposition of solutions with the objective of increasing gene diversity by utilizing the qubit as a representation. Quantum rotation computing (QRC) enhances population diversity and tackles computing complexity and early convergence issues in GA. The superposition concept in QRC utilizes both mutation (divergence) and crossover (convergence) procedures to expand diversity and improve population selection. QIGA performs better in a parallel structure because of its quick convergence and robust global search capabilities. This implies that QIGA embodies features of both exploration and exploitation. The studied approach is applied to three different databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Consequently, the proposed OSV based on the QIGA improved equal error rate (EER) between 10% and 20% over the GA without influencing computation complexity. [ABSTRACT FROM AUTHOR]

This paper proposes the application of control by state feedback using the linear quadratic regulator (LQR) optimized by metaheuristics to damp low-frequency electromechanical oscillations in electrical power systems. The current sensitivity model was used to represent the single machine infinite bus (SMIB) system in the time domain. The weighting matrices of the LQR were adjusted using four different algorithms: (i) the genetic algorithm, (ii) the differential evolution algorithm, (iii) the particle swarm optimization algorithm, and (iv) the gray wolf optimization (GWO) algorithm. In the cases considered, disturbances were applied to the electrical power system and, then, performances comparisons associated with each metaheuristic were statistically analyzed, in which the number of iterations, error, and time to achieve convergence of each algorithm were compared. From the results, it was possible to conclude that the algorithms were efficient in adjusting the weighting matrices of the LQR, providing additional damping to the poles of interest of the system. It was also possible to conclude that the GWO algorithm presented the best performance, accrediting it as a powerful tool in the study of small-signal stability for the analyzed case. • Design of an LQR for small-signal stability analysis of power systems. • Implementation metaheuristics for adjusting the parameters of the LQR. • Comparison of the performance of evolutionary and swarm-based algorithms. [ABSTRACT FROM AUTHOR]