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The brushless direct current (BLDC) motor is characterized by high torque, which made it widely used in many industrial applications. To improve the working performance of the BLDC motor, the addition of controllers such as proportional integral derivative (PID) is adopted. To obtain high-performance controllers, the design process is adopted to develop a suitable algorithm. The genetic algorithm (GA) was chosen to tune the PID controller and get suitable parameters for each of kp, ki and kd with self-tuning. The design process is based on BLDC motor control using the GA to tune the traditional PID controller. Simulations were carried out for three cases including the absence of controllers secondly, by using the traditional control unit and finally with the GA. The integral time absolute error (ITAE) type error control standard for BLDC motor control system was selected. After conducting the simulation, the results demonstrated the superiority of the GA over the traditional ones in terms of response speed, (stability, rise, and settling) time and percentage overshootas details will be mentioned the model in the subsequent paragraphs of the research, finally the simulation results indicate the development and improvement of BLDC motor operation and performance during real time.

Particle identification is an important feature of the future SPD experiment at the NICA collider. In particular, identification of particles with momenta up to a few GeV/c by their time-of-flight will facilitate reconstruction of events of interest. High time-resolution of modern TOF detectors dictates the need to obtain the event collision time t0 with comparable accuracy. While determination of the collision time is feasible through the use of TOF signals supplemented by track reconstruction, it proves to be computationally expensive. In this work we have developed a dedicated Genetic Algorithm as a fast and accurate method to determine the pp-collision time by the measurements of the TOF detector at the SPD experiment. By using this reliable method for t0 determination we compare different approaches for the particle identification procedure based on TOF-signals., Contribution to The 6th international conference on particle physics and astrophysics (ICPPA-2022). Results, Discussions and References are extended

This paper proposes a method for improving empirical models of complex technological objects with insufficient information about the input and output values of an object's parameters. It has been established that most methods for constructing empirical models require knowledge of the statistical characteristics of the input and output values of an object. When modeling complex non-reproducible stochastic processes that evolve over time, information about the parameters and structure of an object is usually not available. A method has been proposed where input and output values are treated as fuzzy quantities with a triangular membership function. Since at some points in the region, the triangular membership function is undifferentiated, it is inconvenient to use it in its typical form to solve the problem of optimal control. Therefore, it is proposed to approximate it with the Gaussian membership function. It is shown that such an approximation is reduced to finding one parameter, which is determined by the least squares method. Its value practically does not depend on the magnitude of the uncertainty interval while the value characterizing the accuracy of approximation is a monotonously increasing function that has a linear character. This makes it possible to define the main operations on fuzzy numbers and derive an empirical model for the case of a polynomial "base" model. The resulting model is linear in its parameters, so a genetic algorithm can be used to find them. It is shown that genetic algorithms can be used in the construction of empirical polynomial models when input parameters are interpreted as fuzzy numbers. Thus, it can be argued that when constructing an empirical model of an object that is affected by external disturbances that cannot be measured, it is advisable to approximate all input quantities with a triangular Gaussian membership function

The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.

Data encryption is being widely employed to secure data in order to provide confidentiality, authenticity and data privacy. In proposed method a novel method for (encryption, decryption) message through optimization using genetic algorithm (GA) is presented which aims to maintain the security of the message via increasing the complexity of the key used in the encryption. The proposed method is named stream cipher randomization using GA (SCRGA). It is characterized by its secrecy by hiding the statistical properties for the input message. The GA increases the key diffusion in order to eliminate the likelihood of using the statistical analysis and cryptanalysis techniques to obtain the original text. A key (k) will be supplied as input to pseudorandom bit generator and then it produces a random 8-bit output, The SCRGA evaluates the candidate keys and it would select the one that achieves the best value of the fitness function by maximization three criteria considered in matching the input message and the key. The test results were based on the comparison of the proposed method with two other state-of-the-art methods, that the SCRGA outperformed the other two methods in terms of the execution time (encryption, decryption) and encryption rounds key size.

In recent days, vehicles usage and speed are highly increased that leads to an increase in energy consumption, delay, and overhead in the network. In this paper, a novel trajectory is introduced to achieve maximum reliability namely trust and mobility-based evaluation for intelligent collaborative (TMIC)-UAVs assisted VANETs. Reactive multipath greedy routing protocol (RMGR) is the hybrid routing protocol and it is the combination of ad hoc on-demand multipath distance vector (AOMDV) with greedy geographic forwarding (GGF) which is used for routing in frequently changeable network topology. To protect the network from malfunctions, effective trust evaluation (ETE) is performed by calculating the direct trust and indirect trust. Finally, to achieve effective communication among the UAVs, hybrid optimization is performed which is the combination of the genetic algorithm (GA) and the crow swarm optimization (CSO) algorithm. For validation network simulator (NS3) is used and the results show that this approach achieves high energy efficiency, delivery ratio, and reduction in delay when compared with the earlier research.

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.

The objective of this article is to present an optimization problem formulation to reduce the total structural cost of prestressed doubly-symmetric and monosymmetric I-shaped steel simply supported beams with straight tendons. The optimization problem was implemented via MATLAB’s native Genetic Algorithm. The validation and evaluation processes adopted two examples from literature. The design method follows the Brazilian standard NBR 8800:2008 for the Ultimate and Serviceability Limit States. The best result was found for a monosymmetric case by up to 20.00% and 25.70%. Saving in material weight and installation of tendons, without exceeding the security limits, was also effective. Furthermore, the results presented an efficient alternative for structural engineering, providing a significant model for similar analyzes.

Production systems have been growing exponentially in size in recent decades. One of the problems in the manufacturing system is that a need of effective and efficient materials transportation between the workstations. Auto-mated guided vehicles (AGV’s) can provide a satisfactory solution to this issue. Because of this, AGV’s is a one of the most important material handling tool used in flexible manufacturing system (FMS). The designing, scheduling and routing of the AGV’s are the key issues for the reason that performance of AVG’s in flexible manufacturing system depends on these parameters. The failure of AGVs may significantly impact the operation and efficiency of the entire system, and it is a critical issue. In this paper the aims to answer How the reliability of individual AGVs in the system assessed? , and How AGVs affects the performance of the whole FMS system?. It was also discussed that optimizing techniques to improve the performance of AGV in FMS. This paper discussed the details studies of approaches and optimization techniques used for optimizing AGV’s in FMS including design and their control.

Preventive maintenance activities are often the cause of downtime of technical multi-component systems. To minimize maintenance costs and maximize productivity, maintenance tasks are often grouped and carried out simultaneously. We consider the problem of obtaining an optimal maintenance schedule when the multi-component system is also a networked system and can be modeled as a directed graph, where nodes represent machines or workers, and edges represent the exchange of material, information, or work between these nodes. To find efficient maintenance schedules, we formulate a bi-objective optimization problem, which considers the limited availability of maintenance personnel, and we propose an algorithm that finds a set of maintenance schedules, which are a good approximation of the Pareto front in terms of costs and productivity. Through sensitivity analysis we show the extent to which adding maintenance personnel improves system productivity at the expense of increased maintenance costs and idle time of some resources. Besides solving the Pareto-optimal schedules, we show how the developed model is useful in maintenance personnel planning, and we outline limitations and future developments of the present work.

Chemical plants face safety hazards and security threats that may induce catastrophic scenarios. Safety and security barriers are employed widely to protect chemical plants from accidental and intentional undesired events and mitigate consequences. Managing safety and security barriers effectively and economically is a research topic with practical significance. The analysis of undesired event scenarios, including both accidental and intentional adverse scenarios, and assessing associated safety and security barriers are critical regarding cost-efficient barrier maintenance. This study proposes a novel approach for optimizing safety and security barrier maintenance strategy considering economic constraints. This approach consists of three steps: scenario building and barrier identification, barrier modeling, and determining optimal barrier maintenance intervals. In the proposed approach, accident scenarios in terms of safety and physical security are constructed using the extended bow-tie diagrams. After associated safety and security barriers are identified, a system simulation model is developed to conduct barrier modeling based on MATLAB/Simulink simulations, in which the barrier maintenance, the impacts of human and organizational barriers, and the correlations between barriers caused by shared components are considered. Finally, a combination of cost-effectiveness analysis (CEA) and genetic al-gorithm (GA) is employed to support the decision-making on barrier maintenance optimization. An illustrative case is employed in this study to validate the feasibility of the proposed approach.

One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm http://dx.doi.org/10.25130/tjps.23.2018.039

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

One-level optimization methods have been proposed to optimize a single user’s load profile or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one case considering technical requirements (case 1) and another considering economic criterion (case 2). In the upper level, the supplier optimizes the objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on Genetic Algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulation results illustrate that both cases improve the demand profile and increase the retailer profit concerning an unscheduled case. However, when the supplier tries to maximize the profit, some users receive benefits to detriment of others, concluding that the technical approach is preferable to the economic one.

In this paper, a novel integrated SHE-NLC control is proposed for the mitigation of unwanted lower-order harmonics in the cascaded H-bridge multilevel inverter. The proposed algorithm has been developed by hybridizing the NLC and SHE methods, in an attempt to keep the merits of both the individual control. The switching angles and nearest levels are calculated by applying the Genetic Algorithm (GA). The proposed technique reduces the calculation time and implementation complexity, thus can be a viable alternative to the real time implementation for SHE. Integrated NLC-SHE control is tested by varying the modulation index and load dynamics. This technique resulted in the reduction in Total Harmonic Distortion (THDs) in load voltage and current. Comparative analysis of NLC, SHE and Integrated NLC-SHE technique is also performed on the CHB nine-level inverter. There is significant reduction in voltage and current THD values and power losses. Efficiency of the inverter is increased. The efficacy of the proposed control CHB nine-level is tested on MATLAB Simulink environment and further validated by experimental results. Publishers version

Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.

Elektrik güç sistemlerinin en önemli parametrelerinden biri olan gerilim değerinin korunması modern güç sistemleri için hayati önem taşımaktadır. Otomatik gerilim regülatörleri güç sistemlerinde gerilim değerinin istenilen değerde korunmasını sağlarlar. Bu çalışmada otomatik gerilim regülatör sisteminde kontrolör yapısı olarak literatürde en yaygın kullanılan PID kontrolör yapısı kullanılmıştır. Bu çalışmada kontrolör parametre değerlerini ayarlamak için Genetik algoritma tekniği kullanılmıştır. Kullanılan tekniğin başarısı literatürde PID kontrolör kazanç değerlerini ayarlamada en yaygın kullanılan tekniklerden biri olan Ziegler-Nichols yöntemi ile karşılaştırılmıştır. Sunulan tekniğin başarısını göstermek için hem zaman domeni analiz yöntemleri hem de frekans domeni analiz yöntemleri bu çalışmada kullanılmıştır., Voltage level protection, which is one of the most important parameters of electrical power systems, is critical for modern power systems. Automatic voltage regulators make ensure that power systems' desired voltage level is maintained. The automatic voltage regulator system's controller structure in this study is based on the PID controller structure that has been utilized the most extensively in the literature. In this research, the controller parameter values are modified using a genetic algorithm technique. The Ziegler-Nichols approach, one of the most popular methods in the literature for modifying PID controller gain levels, is utilized to compare the effectiveness of the strategy used. Both time domain analysis methods and frequency domain analysis methods are employed in this study to show the effectiveness of the proposed strategy.

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data is offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

This work presents the use of a high-fidelity neural network surrogate model within a Modular Optimization Framework for treatment of crud deposition as a constraint within light-water reactor core loading pattern optimization. The neural network was utilized for the treatment of crud constraints within the context of an advanced genetic algorithm applied to the core design problem. This proof-of-concept study shows that loading pattern optimization aided by a neural network surrogate model can optimize the manner in which crud distributes within a nuclear reactor without impacting operational parameters such as enrichment or cycle length. Several analysis methods were investigated. Analysis found that the surrogate model and genetic algorithm successfully minimized the deviation from a uniform crud distribution against a population of solutions from a reference optimization in which the crud distribution was not optimized. Strong evidence is presented that shows boron deposition in crud can be optimized through the loading pattern. This proof-of-concept study shows that the methods employed provide a powerful tool for mitigating the effects of crud deposition in nuclear reactors.

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

Searching for an available parking space is a major cause of traffic congestion and pollution. Besides, getting a free spot constitutes a primary worry for drivers on the road, which results in their daily discomfort and stress. Therefore, well-designed parking management tools are needed to effectively allocate parking spaces to drivers, especially in dense urban areas. In this perspective, we propose two linear integer programming models for assigning parking spaces to all road users (private vehicles and carriers) while maximizing parking occupancy. The first combinatorial problem attributes to each driver only one parking space while the second one assigns two parking spaces if no space is suitable for that motorist. Feasible solutions are computed in an exact way by Improved primal simplex method and in an approximated manner by two metaheuristics (Genetic algorithm and Taboo search) for larger scale applications. However, parking problems still persist when parking requests are not properly balanced between a city's zones, a local gridlock problem arises where certain areas are over-used and others under-used. For this reason, we first redistribute parking demands over urban areas before dedicating one or two spaces to each driver. To do this, we consider both solicitation rates in these areas and coverage ratios of received parking demands. Our parking space allocation solution is just one component of a transportation system heavily backed by information and communication technologies. This system consists of effectively managing a city's parking spaces by predicting and understanding their occupancy patterns. The suggested approach is applied to Casablanca city with several parking scenarios in order to evaluate its effectiveness and test its ability to minimize total walking distance, parking costs as well as unmet demand percentages. Experimental results show that our mathematical models can effectively rule out parking problems. The related published methods satisfy at most 75% of received parking requests. However, with our proposal, it is possible to find parking spaces for 87.3% of drivers without any noticeable effort, even an increase of 16.4%. The ordinary walking distance is reduced by 40% if our solution is adopted. Yet, other research works report a decrease of this distance not exceeding 30%. The increase in parking demand satisfaction rates demonstrates the effectiveness of our system, especially in the eyes of urban stakeholders. The reduction in walking distance encourages drivers to use our solution and consequently increases their loyalty.

To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

Recent technological breakthroughs have allowed unmanned aerial vehicles (UAVs) to be utilized in a broad range of new operations. Among these various applications, herein, we focus on the use of UAVs for search and rescue missions in emergency and postdisaster scenarios. In this context, self-charging technologies for drones create new challenges in the routing of UAVs with charging stations. We present a variant of the vehicle routing problem (VRP) to address the integrated use of UAVs and mobile charging stations and define the VRP with synchronized networks (VRPSN), a new class of VRPs involving the routing of UAVs whose recharge platforms can travel to different locations during an operation. This leads to two networks within the VRP that must be integrated and synchronized. This research develops a mixed-integer linear program model for the VRPSN that considers the use of UAVs and mobile charging stations in a synchronized manner. To overcome the computational limits of the MILP model, this research presents a construct-and-adjust heuristic method integrated with a genetic algorithm. As a numerical example, we test the proposed model on the Corrego do Feijao Mine located in Minas Gerais, Brazil, where a dam recently collapsed, killing many workers. Numerical tests show that the new methodology is an attractive planning method for providing efficient and rapid responses in search and rescue missions.

Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Loadbalanced Genetic Algorithm (CLGA), respectively. Хмарні обчислення відіграють значну роль у способі життя кожно- го, щільно пов’язуючи спільноти, інформацію та торги по всьому світу. Розпі- знавання оптимального рішення для планування робочих процесів у хмарі є складною сферою через його NP-жорсткий характер. Запропоновано гібрид- ний метаевристичний економічно ефективний збалансований за навантажен- ням підхід до планування робочого процесу в гетерогенному середовищі. Мо- дель ґрунтується на генетичному алгоритмі, інтегрованому з прогнозом найбільш раннього часу фінішу (PEFT), щоб мінімізувати makepan. Замість призначення завдання випадковим чином на віртуальній машині застосовуємо жадібну стратегію, яка відводить завдання на віртуальну машину з найменш завантаженим. Після завершення операції мутації перевіряємо обмеження за- лежності замість кожної операції кросовера, що дає кращий результат. Запро- понована модель включає в себе дисперсію продуктивності віртуальної маши- ни, а також затримку придбання, яка поступається мінімальній вартості makepan і computing. Одним з найбільш приголомшливих аспектів економічно ефективного гібридного генетичного алгоритму ( CHGA ) є його здатність пе- редбачати, створюючи оптимістичну таблицю витрат ( OCT ), зберігаючи ква- дратичну складність часу. На основі результатів ретельних експериментів над деякими показниками робочого процесу в реальному світі та всебічного аналі- зу деяких нещодавно успішних алгоритмів планування отримано висновок, що продуктивність запропонованої CHGA є мелодійною.

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients WOS:000871801500001

Copyright (c) 2022 In this paper, we have looked at how easy it is for users in an organisation to be given different roles, as well as how important it is to make sure that the tasks are done well using predictive analytical tools. As a result, ensemble of classification and regression tree link Neural Network was adopted for evaluating the effectiveness of role-based tasks associated with organization unit. A Human Resource Manangement System was design and developed to obtain comprehensive information about their employees’ performance levels, as well as to ascertain their capabilities, skills, and the tasks they perform and how they perform them. Datasets were drawn from evaluation of the system and used for machine learning evaluation. Linear regression models, decision trees, and Genetic Algorithm have proven to be good at prediction in all cases. In this way, the research findings highlight the need of ensuring that users tasks are done in a timely way, as well as enhancing an organization’s ability to assign individual duties.

In this paper, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e. makespans) and costs in fog-cloud environments

In this article, a novel underactuated finger exoskeleton is designed to assist grasping tasks for the elderly with weak muscle strength. In mechanical design, the human finger’s phalanges and joints are considered as part of the kinematic chains to realize the human-robot kinematic compatibility. The proposed finger exoskeleton achieves the finger preshaping and grasps objects with generic shapes. The proposed exoskeleton is both actively and passively backdriveable. Moreover, the weight of the wearable part of the proposed exoskeleton is 127 g, and the overall weight is 476 g, which indicates the proposed exoskeleton is lightweight and portable. To improve the grasping performance, the multiobjective genetic algorithm is implemented to optimize contact forces, which is to maximize the sum of the forces exerted on the index finger phalanges by the exoskeleton and to minimize the difference between the contact forces. After optimization, the sum of contact forces is risen by 15%, and the difference between forces is decreased by 53%. Furthermore, the admittance control is applied to make the proposed finger exoskeleton more compliant in the preshaping phase, and the admittance control is also implemented to achieve the fingertip grasping force control in the grasping phase. Finally, experiments have been conducted to verify the range of motion, grasping forces, and feasibility of the proposed index finger exoskeleton. The effectiveness of the control algorithm has also been verified by experiments.

This work investigates the stabilization problem of uncertain stochastic Markovian jump systems (MJSs) under communication constraints. To reduce the bandwidth usage, a discrete-time Markovian chain is employed to implement the stochastic communication protocol (SCP) scheduling of the sensor nodes, by which only one sensor node is chosen to access the network at each transmission instant. Moreover, due to the effect of amplitude attenuation, time delay, and random interference/noise, the transmission may be inevitably subject to the Rice fading phenomenon. All of these constraints make the controller only receive the fading signal from one activated sensor node at each instant. A merge approach is first used to deal with two Markovian chains; meanwhile, a compensator is designed to provide available information for the controller. By a compensator and mode-based sliding-mode controller, the resulting closed-loop system is ensured to be input-to-state stable in probability (ISSiP), and the quasisliding mode is attained. Moreover, an iteration optimizing algorithm is provided to reduce the convergence domain around the sliding surface via searching a desirable sliding gain, which constitutes an effective GA-based sliding-mode control strategy. Finally, the proposed control scheme is verified via the simulation results.

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl-Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications.

Exploiting energy-saving potentials while obtaining ideal processing results is highly sought for in fiber laser welding (FLW) applications. However, little is known about the correlation between energy consumption and bead geometry of FLW, which is largely determined by processing parameters. In this study, to find the optimal processing parameters of FLW, an ensemble of variable neighborhood search-based gene expression programming (VNS-GEP) and black-box metamodels (EGBM) is presented, combined with non-dominated sorting genetic algorithm (NSGA-II) to form the proposed optimization methodology. Firstly, the optimal weight coefficients of VNS-GEP and two black-box metamodels (Kriging and SVR) are determined considering the leave-one-out generalized mean square error. Then, the EGBM is used to establish the relationship between processing parameters (laser power, welding speed and defocus distance) and response results (energy consumption and bead geometry). Additional experiments are then performed to validate the accuracy of EGBM. Analysis of variance (ANOVA) is carried out to study the main effects of the processing parameters on response results. After that, NSGA-II is employed based on EGBM to approximate the Pareto front of processing parameters with minimal total energy consumption (TEC) and maximal depth-to-width ratio (DWR). Finally, experimental validations show that the obtained solutions can achieve ideal DWR and significant reductions in TEC. In conclusion, the proposed hybrid methodology, EGBM-NSGA-II, can facilitate obtaining optimal welding processing parameters and provide a reliable empirical basis for low-energy laser manufacturing.

This paper combines the tabu search process with a genetic algorithm by employing a new partial opposed-based as the population initialization technique to minimize makespan. Flow shop is a prominent variety in scheduling with various applications in numerous disciplines, including production. Its difficulty makes many researchers develop algorithms to solve it since it is categorized as an NP-hard problem. Some approaches have been developed to resolve production scheduling, especially evolutionary algorithms. The genetic algorithm becomes the most used algorithm in evolutionary to address production schedule because of its capability to produce good, fast, and efficient results to explore complex solution space (global search). However, it provides ineffective results when doing exploitation (local search) that is easily stuck in the optimum local area. Contrarily, tabu search has superiority in local search to help genetic algorithms not be trapped in a local optimum. By combining these two algorithms with the initialization scheme, a new algorithm that balances searches in resulting higher quality solutions is obtained. The proposed algorithm was tested using 120 problem instances to carry out the algorithm performance. The empirical results state that the developed algorithm can improve 115 solutions out of 120 instances than the six other hybrid algorithms.

An ultrasound sparse array consists of a sparse distribution of elements over a 2-D aperture. Such an array is typically characterized by a limited number of elements, which in most cases is compatible with the channel number of the available scanners. Sparse arrays represent an attractive alternative to full 2-D arrays that may require the control of thousands of elements through expensive application-specific integrated circuits (ASICs). However, their massive use is hindered by two main drawbacks: the possible beam profile deterioration, which may worsen the image contrast, and the limited signal-to-noise ratio (SNR), which may result too low for some applications. This article reviews the work done for three decades on 2-D ultrasound sparse arrays for medical applications. First, random, optimized, and deterministic design methods are reviewed together with their main influencing factors. Then, experimental 2-D sparse array implementations based on piezoelectric and capacitive micromachined ultrasonic transducer (CMUT) technologies are presented. Sample applications to 3-D (Doppler) imaging, super-resolution imaging, photo-acoustic imaging, and therapy are reported. The final sections discuss the main shortcomings associated with the use of sparse arrays, the related countermeasures, and the next steps envisaged in the development of innovative arrays.

This research work deals with the modelling, control and simulation of a wind turbine based on the doubly fed induction generator (DFIG) in the current sensor’s failure event. We present in the first time the model of the wind energy conversion system based on the DFIG and the control by active disturbances rejection control (ADRC) optimized by genetic algorithm. Particular focus is directed towards on a technique for detection, identification, isolation and reconfiguration of current sensor signals after failure. The combination of the two preceding techniques makes it possible to have a fault tolerant control to sensor faults which ensures continuity of service in all circumstances. The robustness of the proposed technique is tested under the MATLAB/Simulink environment.

Transmission systems are prone to contingency conditions that would either occur using a generator outage or line outage. Placing and sizing the flexible ac transmission systems (FACTS) devices appropriately can reduce the effects of the contingency condition. This paper optimally locates FACTS devices in a transmission system under the N-1 contingency condition. The genetic algorithm (GA) technique is used to locate different, multiple FACTS devices (thyristor-controlled series capacitor and static VAR compensator) optimally in a power system. This optimization technique is used to locate FACTS devices on the IEEE 9 bus system. MATLAB simulation is developed and checked for both single and multiple FACTS placements. Simulation results obtained for generator outage and line outage are tabulated with the type of FACTS device/rating, location, and generation cost with line loss reduction. The optimized results observed for the costoptimized FACTS placement problem are found to be satisfactory. The results obtained in the IEEE 9 bus system have shown improvement in a decrease of generation cost and system loss component while placement and sizing of both the FACTS devices.

This article presents an innovative implementation of the plus-type secondgeneration current conveyor (CCII+) using BiCMOS technology, which combines the benefits of bipolar and CMOS technologies. The optimization problem of minimizing the X-port input resistance and maximizing the current cut-off frequency value was solved using genetic algorithm (GA). The proposed approach allows the optimization of the BiCMOS CCII+ to achieve better performance compared to previous designs. As a practical application, a second-order band-pass filter using the optimized BiCMOS CCII+ was successfully realized. The performance of the proposed design was evaluated using SPICE simulations and compared with previously published works. This study shows that the BiCMOS CCII+ can be effectively optimized using GA to improve its performance, which has potential applications in various analog and mixed-signal circuits.

Nova-C is a lunar lander developed by the private company Intuitive Machines to deliver commercial payloads to the Moon. The IM-1 mission set for 2023 will launch and land the Nova-C near the Moon's south pole. In this research, various navigation techniques are explored to determine the lander's position and velocity during key segments. This process is studied for key mission events including trajectory correction maneuvers (TCMs), lunar orbit insertion (LOI), and descent orbit insertion (DOI). Each mission segment, referred to as an Orbit Determination Segment (OD), is analyzed with three different navigation techniques: Monte Carlo Analysis, Linear Covariance Analysis, and Dilution of Precision Analysis. The results are matched against each other to validate consistency. The quicker navigation techniques, specifically Linear Covariance and Dilution of Precision, are implemented into a genetic algorithm to determine optimal ground station tracking schedules. Said optimal schedules are optimal with respect to various objectives, including final position errors and operational costs. Operational costs and navigation errors are combined to form a new metric, referred to as Nav-Dollars, that analyzes the trade off between real costs and estimation performance.

A new approach to optimize the design of a shell and tube heat exchanger (STHX) is developed via a genetic algorithm (GA) to get the optimal configuration from a performance point of view. The objective is to develop and test a model for optimizing the early design stage of the STHX and solve the design problem quickly. GA is implemented to maximize heat transfer rate while minimizing pressure drop. GA is applied to oil cooler type OKG 33/244, and the results are compared with the original data of the STHX. The simulation outcomes reveal that the STHX's operating performance has been improved, indicating that GA can be successfully employed for the design optimization of STHX from a performance standpoint. A maximum increase in the effectiveness achieves 57% using GA, while the achieved minimum increase is 47%. Furthermore, the average effectiveness of the heat exchanger is 55%, and the number of transfer units (NTU) has improved from 0.475319 to 1.825664 by using GA.

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period How to improve the performance and generalization of the monitoring model is a critical but challenging task This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’services, such as patient-care, disinfection, garbage disposal, etc We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs) By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic IEEE

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones.

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader's side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers' side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs' energy load in comparison to the existing state-of-the-art scheme.

In-situ tool condition monitoring (In-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture and the labor-intensive tuning of hyper-parameters. Network-based deep transfer learning (NDTL) partially solves the problem of limited labeled data. However, the time-consuming architecture design and the tuning of hyper-parameters also pose a great impact on the schedule of real in-situ TCM projects. In this paper, a new NDTL method is proposed, which is automatically designed by the genetic algorithm (NDTL-GA). A degradation monitoring experiment is conducted employing edge computing devices under multiple working conditions, where the texture of the machined surface is collected during the whole life cycle of milling cutters. The experimental results suggest that the proposed method possesses competitive performance evaluated by both the robustness metrics (e.g., the area under the curve of precision-recall (AUC-PR)) and the efficiency metrics (e.g., multiplyaccumulates (MACC)), where the trial-and-errors are reduced significantly by the automatic architecture design and the selection of hyper-parameters.

By the recent improvement of the internet of things (IoT), the need to implement wireless networks is increasing. It is a challenge to balance between battery lifetime of the different sensors and network lifetime. Many studies proved the importance of using clustering and mobile data collectors (MDCs) to extend the operating time of sensor nodes. A mobile data collector is used to gather the data recorded by the nodes over a short transmission range. The proposed approach aims to decrease the energy consumption of each sensor node by using the Genetic Algorithm (GA) and mobile data collector. So, we suggest a clustering algorithm to find suitable Cluster Heads and form clusters. Then, we employ the Genetic algorithm to construct an optimal data gathering path for MDC. Computer simulation proves that the proposed approach outperforms existing ones.

In recent history, Convolutional Neural Networks have attained breakthroughs in addressing many intractable problems in the domain of image processing. But its performance builds upon its chosen hyper parameters and it is a tedious job to manually fine tune these hyper parameters. Hence, in this research, an Automated Hyper-parameter Optimized Convolution Neural Net is proposed which is further applied to uncover the class of skin cancer. The method has utilized a Grey Wolf Optimization algorithm for optimizing the hyper parameters of CNN, by adopting a proper encoding scheme. The effectiveness of the model is verified by comparing it with the performance of Particle Swarm Optimization and Genetic algorithm based hyper-parameter optimized CNN applied on the International Skin Imaging Collaboration skin lesion multi class data set. Simulation results infer that the proposed model is able to produce a testing accuracy up to 98.33% which is around 4% and 1% more compared to PSO and GA based models respectively. Similarly with the proposed model, the testing loss realized is around 0.17% which is 39.2% and 15% less compared to PSO and GA based models respectively. The experimental results clearly demonstrate that the proposed model performs competitively compared to other reported models.

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

Methods for optimizing and evolving spiking neural P systems (in short, SN P systems) have been previously developed with the use of a genetic algorithm framework. So far, these computations, both evolving and simulating, were done only sequentially. Due to the non-deterministic and parallel nature of SN P systems, it is natural to harness parallel processors in implementing its evolution and simulation. In this work, a parallel framework for the evolution of SN P Systems is presented. This is the result of extending our previous work by implementing it on a CUDA-enabled graphics processing unit and adapting CuSNP design in simulations. Using binary addition and binary subtraction with 3 different categories each as initial SN P systems, the GPU-based evolution runs up to 9x faster with respect to its CPU-based evolution counterparts. Overall, when considering the whole process, the GPU framework is up to 3 times faster than the CPU version. Junta de Andalucía P20_00486