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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]

For the port injection hydrogen-fueled spark ignition (SI) engine modified from the Jialing JH600 gasoline engine, the hydrogen injection timing and ignition timing of the hydrogen SI engine are comprehensively optimized on the AVL-FIRE software based on the combination of design of experiment (DOE) and multiple population genetic algorithm (MPGA). Firstly, the effects of hydrogen injection timing and ignition timing on the performance of the hydrogen engine are investigated through the full factors design of experiment scheme, and then the optimum intervals of them are determined respectively. Secondly, the uniform design of experiments scheme is arranged, and the regression functions of indicated power, indicated thermal efficiency and emissions of the hydrogen engine under different working conditions are fitted. Then, the comprehensive optimization model is constructed. Finally, the optimum hydrogen injection timing and ignition timing of the hydrogen engine under different working conditions are solved based on MPGA. The results show that, compared with the standard genetic algorithm (SGA), the MPGA has faster convergence speed and higher convergence accuracy. In addition, as the load increases from low to high, the optimum hydrogen injection timing is advanced and the rate of variation increases from 0.6% to 1.1%, the optimum ignition timing is delayed and the rate of variation decreases from 29.1% to 17.8%. • The uniform design can substantially reduce experimental study effort and cost. • Optimization models obtained by regression analysis based on the uniform design. • Solving optimization models with MPGA instead of SGA. • Providing a new idea for optimizing hydrogen SI engine. [ABSTRACT FROM AUTHOR]

The satellite motion on the reference orbit (RO) with less energy consumption has always persuaded researchers to design optimal control systems. The nonlinear nature and time-varying equations of motion make this quest more challenging. The present study proposes a novel control system for satellite motion on the RO by considering a comprehensive model of its dynamics in orbit and a Nonlinear Model Predictive Controller (NMPC). The NMPC calculates the sub-optimal control inputs of satellite motion reference on the elliptic orbit by minimizing a convex cost function at each stage. Moreover, all weighting parameters of the cost function are optimized by the Genetic Algorithm (GA) to produce less perturbation and guarantee the best NMPC performance. Finally, the implemented NMPC has been compared to a Linear MPC (LMPC). The results show that not only can the NMPC resist against larger errors and perturbations, but it can also compensate for those errors by returning the satellite to its main orbit and maintaining it. [ABSTRACT FROM AUTHOR]

This work deals with the optimization of an existing industrial cryogenic air separation plant with respect to exergy efficiency and argon recovery. A previously validated simulation model of the plant was used, and the operating conditions and specifications of the process correspond to those reported in the historical records of an air separation facility in Colombia. The optimization is carried out considering the variation in the historical demand and the installed capacity of the plant, using an elitist nondominated sorting genetic algorithm (NSGA-II). As exergy efficiency and argon production goals are in conflict, a Pareto front of optimal solutions is obtained for the assessed variables. It is found that under optimal conditions of the pareto fronts, exergy efficiency and argon recovery can be enhanced up to 30 % and 23 %, respectively, with respect to recommended operating conditions from the plant manual. Also, obtained results indicate that the plant operated with larger air feed flowrates than required for the corresponding specific monthly demands. Under the optimal conditions, the plant could operate with a 58 % average reduction in the specific energy consumption (0.53 kW/Nm3O 2) and larger argon recoveries. This represents a major improvement in the profitability of the operation and its sustainability indicators. [Display omitted] • Multi-objective optimization of an industrial air separation plant was carried out. • Exergy efficiency and argon recovery were optimized under fixed N 2 and O 2 production. • A variable demand with respect to historical records of the plant was considered. • Exergy efficiency and argon recovery generates an optimal pareto front. • Optimal conditions enabled up to 60 % reduction in the specific energy consumption. [ABSTRACT FROM AUTHOR]

Oscillating water columns (OWCs) are widely regarded as the simplest and most reliable type of wave energy converter. An economically viable OWC wave power plant requires maximizing the performance of the power take-off system, composed of an air turbine coupled to an electrical generator. Besides, the OWC power plant's inherent nonlinearities and modeling uncertainties must be dealt with. One solution is the use of fuzzy controllers since they capture both nonlinearities and uncertainties of the system, which can largely contribute to improving the overall efficiency. This paper shows how an inverse fuzzy model optimized by a genetic algorithm (GA) can improve the control of an OWC and, consequently, its performance. As a case study, the Mutriku power plant with a biradial turbine was addressed, considering 11 different sea states of the characteristic wave climate. Two optimization objectives were considered: maximizing turbine and generator output average powers. Compared with a well-known baseline control strategy, the GA-fuzzy control did not significantly contribute to increasing average turbine power. However, when maximizing generator yearly-accumulated power, there were improvements of over 9%. Ultimately, these research findings may play an essential role in control strategies for wave power plants. • An inverse model controller was proposed for wave energy control. • The inverse model was obtained with fuzzy modeling and optimized by genetic algorithms. • Focus on a real case study: An oscillating water column at the Mutriku power plant. • Comparison of the proposed algorithm with a well-known baseline strategy. • The proposed strategy showed yearly improvements of over 9% on average generator power. [ABSTRACT FROM AUTHOR]

In order to fully exploit the advantages of each energy source, prolong the lifetime of the composite energy storage system, which is composed of a fuel cell, battery, and ultracapacitor, and reduce the comprehensive operating cost of the vehicle, by analyzing the influence on the vehicle's energy economy and energy source life at different power supply sequences of energy sources, an adaptive rule control strategy based on vehicle operation state recognition is proposed for the vehicle with the composite energy storage system. This strategy can automatically select the optimal energy source supply sequence according to the current vehicle speed and acceleration. The effectiveness and superiority of the strategy are verified by comparing it with the traditional rule strategy. Additionally, the key parameters of the proposed strategy, which have a large impact on the comprehensive operating cost of the vehicle, are recognized based on sensitivity analysis and optimized by a genetic algorithm. The simulation shows that the comprehensive operating cost of the vehicle under the WLTP drive cycle is reduced by 10.94% after optimization. [ABSTRACT FROM AUTHOR]

Spiral tool path generation methods for ultra-precision diamond turning of freeform surfaces can significantly affect form accuracy and machining efficiency. However, there is no previous research treating the tool path generation as a multi-objective optimization problem. In this study, a multi-objective optimization method for tool path generation based on a novel surface analysis model was proposed for the first time. In this method, spiral tool path generation methods were uniformly described on the basis of the newly established surface analysis model. The relation between cutting point sampling details and the resulting form error of freeform surfaces was analyzed and simulated. Then, corresponding fitness function and constraint function models were established, and the non-dominated sorting genetic algorithm (NSGA-II) was used to solve the path optimization problem to obtain the pareto optimal solution of the cutting point sampling parameters. Next, based on the optimization results of tool path, machining simulations with experimental parameters were performed to evaluate the quality of optimized tool path. To demonstrate the effectiveness of the proposed method, turning tests of near-rotational freeform surfaces were attempted on single-crystal germanium using the optimization method and the measurement results demonstrated that the optimized tool path can achieve sub-micron form accuracy, which further verified the effectiveness of the proposed optimization method. The proposed optimization method has been further applied in the machining of larger diameter freeform optics. • A multi-objective optimization method for tool path generation based on a novel surface analysis model was proposed. • The relation between cutting point sampling details and the resulting form error of freeform surfaces was analyzed and simulated. • The multi-objective optimization method was firstly applied to tool path generation for freeform surfaces. • A comparative analysis of different tool path generation methods was carried out. • Turning tests of NRFS were attempted and the measurement results demonstrated that the optimized tool path can achieve sub-micron form accuracy. [ABSTRACT FROM AUTHOR]

Due to the explosive growth and dynamic change of user demand, an efficient power and bandwidth allocation algorithm is quite essential for multibeam satellites with flexible digital payloads. To suit the real-time use of the multibeam satellites communication, we build a novel deep reinforcement learning (DRL) architecture for dynamic power and bandwidth allocation. For minimizing unmet system capacity (USC), the proposed DRL architecture adopts proximal policy optimization algorithm to directly allocate the resource in continuous space. Under the proposed DRL architecture, the DRL strategy for joint power and bandwidth allocation (named as pbDRL) reaps the best USC performance in comparison with the separate power or bandwidth allocation method of pDRL and bDRL. Besides, by implementing the allocation decision determined by pbDRL into the initial population of the existing genetic algorithm (GA), we also develop another improved GA, namely drlGA. Numerical results verify that (i) pbDRL outperforms the existing GA and PSO; (ii) pbDRL achieves comparable USC performance within a significantly reduced computation time compared with the existing optimized GA method; (iii) in comparison with the other three related heuristic algorithms, drlGA derives improved USC performance within the same computation time. These results draw a conclusion that the developed pbDRL and drlGA approaches are capable to meet the requirements of high timeliness and desirable demand satisfaction, respectively. • Allocate power and bandwidth jointly and dynamically in multibeam satellites. • Use proximal policy optimization algorithm to directly allocate continuous resource. • Achieve comparable unmet system capacity with dramatically reduced computation time. • Also can be applied to separate only power or bandwidth allocation. • Also develop another genetic algorithm for further improved unmet system capacity. [ABSTRACT FROM AUTHOR]

The strips from region splitting method are critical premise of meta-task generation and task planning for satellite swarm. To achieve the regional observation with high coverage and low overlap ratio and satellites usage, a novel region splitting algorithm is designed based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In the hybrid algorithm of GA and PSO, these two algorithms are executed paralleled, and then exchange the best genes and individuals periodically, to overcome the disadvantage of GA's poor local searching ability and PSO's low convergence speed. The encoding method and parallel iterative optimal method are given in detail. Finally, a simulation scenario is designed and executed to verify proposed algorithm, and the effects of algorithm parameters are analyzed by simulation results. • A region splitting method is presented for satellite swarm. • A hybrid algorithm of PSO and GA is designed to improve planning performance. • The PSO and GA are paralleled and exchanged periodically to save excellent individuals. [ABSTRACT FROM AUTHOR]

• A generic Bayesian optimization framework for congestion pricing problems. • In-depth discussions on the instability of BO in the perspective of error analysis. • Several customized improvements on BO to deal with the instability. In this study, we proposed a generic Bayesian optimization (BO) framework to solve congestion pricing problems. In the BO framework, the Gaussian process (GP) serves as a surrogate model to approximate the highly nonlinear and expensive-to-evaluate objective functions. This study reveals that GP exhibits an instability phenomenon, which inherently limits the accuracy of BO. We investigate the sources and influences of instability from the perspective of error analysis, and then propose an improved GP (IGP) model to address the instability issue. The associated improvements are twofold: matrix inversion and matrix multiplication. A tailored preconditioner is developed to reduce the matrix inversion errors. To address multiplication errors, a tailored dot product algorithm in conjunction with a GP reformulation scheme is proposed. To validate the proposed models and methods, a link-based second-best congestion pricing problem is considered as an example. The results indicate that, in comparison to benchmark approaches (the sensitivity analysis method and genetic algorithm), the proposed BO framework shows higher computational efficiency and solution accuracy. With modifications on GP, the instability phenomenon is substantially mitigated in several instances, hence enhancing the accuracy of the BO framework. [ABSTRACT FROM AUTHOR]

FLASH dose rates >40 Gy/s are readily available in proton therapy (PT) with cyclotron-accelerated beams and pencil-beam scanning (PBS). The PBS delivery pattern will affect the local dose rate, as quantified by the PBS dose rate (PBS-DR), and therefore needs to be accounted for in FLASH-PT with PBS, but it is not yet clear how. Our aim was to optimize patient-specific scan patterns for stereotactic FLASH-PT of early-stage lung cancer and lung metastases, maximizing the volume irradiated with PBS-DR >40 Gy/s of the organs at risk voxels irradiated to >8 Gy (FLASH coverage). Plans to 54 Gy/3 fractions with 3 equiangular coplanar 244 MeV proton shoot-through transmission beams for 20 patients were optimized with in-house developed software. Planning target volume-based planning with a 5 mm margin was used. Planning target volume ranged from 4.4 to 84 cc. Scan-pattern optimization was performed with a Genetic Algorithm, run in parallel for 20 independent populations (islands). Mapped crossover, inversion, swap, and shift operators were applied to achieve (local) optimality on each island, with migration between them for global optimality. The cost function was chosen to maximize the FLASH coverage per beam at >8 Gy, >40 Gy/s, and 40 nA beam current. The optimized patterns were evaluated on FLASH coverage, PBS-DR distribution, and population PBS-DR-volume histograms, compared with standard line-by-line scanning. Robustness against beam current variation was investigated. The optimized patterns have a snowflake-like structure, combined with outward swirling for larger targets. A population median FLASH coverage of 29.0% was obtained for optimized patterns compared with 6.9% for standard patterns, illustrating a significant increase in FLASH coverage for optimized patterns. For beam current variations of 5 nA, FLASH coverage varied between –6.1%-point and 2.2%-point for optimized patterns. Significant improvements on the PBS-DR and, hence, on FLASH coverage and potential healthy-tissue sparing are obtained by sequential scan-pattern optimization. The optimizer is flexible and may be further fine-tuned, based on the exact conditions for FLASH. [ABSTRACT FROM AUTHOR]

Spaceborne global navigation satellite system reflectometry (GNSS-R) is an innovative bistatic radar remote sensing technique utilizing low Earth orbit (LEO) based GNSS-R instruments to acquire GNSS L-band opportunistic signals for measuring geophysical parameters. A GNSS-R LEO constellation with an optimization design for its specialized missions is very significant and necessary. However, the constellation design involves multi-parameter and multi-objective optimization, and the classical analytic solution is not capable of such a complicated issue. This study proposes a multi-objective LEO constellation design method with a genetic algorithm (GA) and presents a framework for designing two GNSS-R LEO constellations, termed "lower-latitude constellation" for typhoons and hurricanes observation in the tropics and "global constellation" for global geophysical parameter measurements. Then, the observation capability of both designed constellations is evaluated in terms of the number of reflection points, spatial coverage density, and revisit time to verify the GA efficiency in LEO constellation design. Results show that the two designed LEO constellations with high fitness function values possess optimal orbit parameter set configuration and outperform the existing CyGNSS constellations in observation performance. Compared with CyGNSS, the number of reflection points observed by the lower-latitude constellation and the global constellation increases by 38% and 45%, as well as the spatial coverage density increases by 28% and 36%. The revisit time for the lower-latitude constellation is reduced by 0.29 h, whereas the revisit time for the global constellation increases by one hour. [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]

The effects of the wave-shaped flow channel structure and a groove in the gas diffusion layer (GDL) on the net power density of proton-exchange membrane fuel cells (PEMFCs) are investigated. A three-dimensional, two-phase, steady-state, and isothermal numerical model of PEMFCs is established. The amplitude and the number of wavy cycles play an important role in improving the performance of fuel cells, and the optimization of the wave-shaped flow channel to obtain the optimal flow channel structure is performed using the genetic algorithm. Furthermore, regarding the optimal wave-shaped channel, the mass transfer effect can be improved by introducing a groove structure into the GDL for gas diffusion. Thus, two parameters, i.e., groove position and depth, are optimized, and the output power and power consumption are also considered. According to the results, the wave-shaped flow channel with a groove in the GDL exhibits high efficiency. The optimal flow channel has an amplitude of 0.643 mm, the number of wavy cycles of 9, and the groove position and depth of 9.5 and 0.05 mm, respectively, and the net power density can be increased by 2.64%. • Wave-shaped flow channel with a groove was firstly considered in the GDL of PEMFCs for its design and optimization. • The amplitude of wave-shaped flow channel has a greater effect than the number of wavy cycles. • The diffusive oxygen flux is influenced by the position of a groove in the gas diffusion layer. • A groove in the gas diffusion layer can enhance the mass transfer effect and improve the fuel utilization. [ABSTRACT FROM AUTHOR]

Complex cascade refrigeration cycles are typically used for production of liquefied natural gas (LNG). These include the propane precooled mixed refrigerant (C 3 MR) cycle, the dual mixed refrigerant (DMR) cycle, and the Phillips Cascade cycle. These are highly energy-intensive processes, resulting in many studies focusing on improving their energy efficiency by reducing their shaft power demand. However, their relative performance remains unclear. Modelling assumptions and natural gas feed differ across the published literature. This work models and evaluates these three commercial cascade cycles on a common basis of modelling assumptions, natural gas feed and process complexity (e.g. number of compression stages). However, the performance of the cycles cannot be compared meaningfully without first optimising the designs. The optimisation strategy proposed combines a genetic algorithm, which includes regeneration of the population to improve diversity, with a nonlinear optimisation. A case study demonstrates the effectiveness of this strategy. This work provides a meaningful comparison of the relative efficiencies of these commercial cascade cycles, to provide a basis for benchmarking novel cascade cycle options in a study to follow. • C 3 MR, DMR, Phillips Cascade cycles on a common basis: gas feed, model assumptions. • Optimisation uses a genetic algorithm with population regeneration followed by SQP. • DMR cycle is the most energy-efficient; 10.4 % higher efficiency than C 3 MR cycle. • Performance of the three cascade cycles analysed from the perspective of their process configuration. • These results serve as benchmark to assess the performance of other cascade cycles. [ABSTRACT FROM AUTHOR]

Early production in oil fields can cause problems related to oil specifications in the existing process unit, especially in the stabilization tower. This study aims to improve the performance of a crude oil treatment unit for an Iranian oil field. The process involves a two-stage desalting unit followed by a stabilizer tower. First, the desalting process was modeled using the population balance equation in MATLAB. Then, the whole process was simulated by linking the numerical code and simulator. The effective parameters of the desalting process were determined by sensitivity analysis. In this research, the desalting and stabilization units were simultaneously optimized due to the existing interactions between the desalters and the stabilizer tower. The goal is to maximize the oil production, optimizing the variables of pressure drop of the mixing valves, electric field intensity, wash water flow rate of the first and second desalters, and the operating temperature of the first desalter. The maximum allowable salt and water contents in the oil entering the stabilizer are 10 PTB and 0.1 %, respectively, considered as optimization constraints. Optimizing the whole process by Genetic Algorithm, the oil production increased by 413 bbl/d (0.68 %) compared to the existing condition. • Improving the performance of a crude oil surface production facility. • Extending a numerical model using population balance to predict desalting efficiency. • Sensitivity analysis of significant parameters on desalters and stabilizer column. • Optimization of the effective process parameters to maximize the produced oil. [ABSTRACT FROM AUTHOR]

The Mars rotorcraft can help the Mars rover to optimize the driving route and explore more efficiently. However, the thin and cold Martian atmosphere can heavily increase the consumed power of the rotorcraft blades and reduce the generated thrust, leading to the reduction in power loading. The aerodynamic force generated by the Mars rotorcraft blade can be significantly influenced by the blade structure, which has several main structural characteristics and needs to be well designed. A multivariable optimization design method of blade structure is proposed based on neural network, genetic algorithm, and computational fluid dynamics simulations. Experiments conducted in the Martian atmosphere simulator indicate that the Mars rotorcraft blades with the hyperbolic chord length distribution and twist have higher power loading in the constant collective pitch angle. • A model was built to predict rotor hover performance at low-Reynolds-number. • Established an optimization model of the planform of the Mars rotorcraft blade. • The optimized blade consumes 29% less power than the conventional blade. • The optimization result were verified experimentally in Mars simulated environment. [ABSTRACT FROM AUTHOR]

The accurate measurement of the temperature of coal spontaneous combustion is a key technology for coal spontaneous combustion and fire prevention and is important to realising safe coal mining and reducing energy losses. A soft sensor model based on a hybrid-kernel-function support vector machine with an improved genetic algorithm is proposed for the nonlinear relationship between the concentration of a characteristic gas of coal spontaneous combustion and the temperature of the coal body. The natural oxidation process of the coal body is simulated using a coal-spontaneous-combustion oxidation warming experiment bench. The gas concentration and maximum temperature of the coal body at different moments in an oxidation warming experiment are taken as the model input data. Various soft sensor models are automatically optimised by a genetic algorithm in the training process, and the performances of the models are compared and evaluated in the field. The results show that the evaluation indexes of the hybrid-kernel-function model are better than those of the single-kernel function. In the training process, the evaluation indexes of the model with hyperparameters automatically optimised by the genetic algorithm are better than those other models, further verifying the nonlinear relationship between the gas concentration and coal temperature in the process of coal spontaneous combustion and oxidation. The use of the improved support vector machine model basically results in no deviation between the soft sensor results and actual temperature data in the field test, indicating that the soft sensor model has high accuracy, generalisation ability and applicability. The improved support vector machine model has a mean absolute percentage error of ≤ 1.616%, mean absolute error of ≤ 0.533 ℃, and root-mean-square error of ≤ 0.608 ℃. These results indicate that the soft sensor problem of coal spontaneous combustion can be considered a typical small-sample regression method. The proposed method can be further applied to similar problems in the energy industry. [ABSTRACT FROM AUTHOR]

To ensure the high reliability of the pipeline system, it is necessary to take group maintenance actions. This work proposes an optimal group maintenance approach based on a heuristically genetic algorithm to attain the system-performing goal while minimizing overall costs with assured system reliability. Multi-stage corrosion defects and multi-level risk areas are considered in this novel approach. The proposed method can sufficiently avoid pipeline failures by considering the multi-level probability of failure constraints. The methodology is illustrated by taking the 60-segment pipelines with multiple corrosion defects as an example. Numerical studies show that the proposed methodology can find an optimized maintenance strategy with a lower cost compared with the unified batch maintenance strategy. In addition, an optimal dynamic maintenance model is proposed and compared with the static model when the environment of the pipelines varies. Sensitivity analyses of the physical parameters of the phenomenon and the tuning parameters of the data-driven model show that the method has good effectiveness, compatibility, and universality. [ABSTRACT FROM AUTHOR]

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 algorithm (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. [ABSTRACT FROM AUTHOR]

Space robots play a significant role in on-orbit capture, space structure construction, and assembly tasks. Since the robotic arms are attached to a free-floating satellite, the motion of the manipulator in such tasks and the satellite are coupled. Multiple-arm space robots can perform complex cooperative tasks and are superior to single-arm space robots. Current work proposes a reactionless manipulation algorithm for a multi-robotic arm based on the iterative Newton–Euler method for space robots with many task and balance arms. The present work demonstrates two tasks and one balance arm to perform a reactionless handshake maneuver in space. This maneuver is presented in detail for a planar and spatial case. The planar case uses 3 DoF robotic arms, while the spatial case uses 6 DoF robotic arms. In addition, the balance arm has been designed considering the efficient usage of energy satisfying reactionless manipulation concept. The design procedure focuses on minimizing energy used during the motion of the balance arm for a known motion of task arms using a genetic algorithm. Moreover, computational experiments are conducted to validate the use of the genetic algorithm for optimization. The results of proposed reactionless manipulation algorithm have been validated with the results available in the literature for the spatial case that uses a different method. In the future, an energy-efficient balance arm will be designed to handle tumbling objects. [ABSTRACT FROM AUTHOR]

Estimation of mixed heat transfer coefficients on complex CFD-simulation models of 5-axis machine tools/ machining centers poses a whole new set of challenges. The enclosure around the machine which protects the user from chips and machining-process is also responsible for creation of an additional environment which has coolant supply, chips and moving air due to temperature changes and oil-emulsion exhaust. This results in a highly complex CFD model which has inner and outer environments along with multiphase fluid domains. The computational effort required is extremely large. The split-simulation approach divides the simulation model into internal and external environments. The natural convection is emphasized for external environment model and forced convection for internal multiphase environment model. This division significantly reduces the computational efforts involved by neglecting uninfluential solid bodies, cavities etc. and thereby the model size. The external and internal influences are mapped separately onto natural and forced convection coefficients using neural networks, which can be used to predict the combined heat transfer coefficients for any load cases within the training boundaries. The temperature fields obtained from split-simulation model are validated with thermography and temperature sensor readings taken from the machine in the form of a use-case study. [ABSTRACT FROM AUTHOR]

Today's technologically advanced connected world is mostly reliant on the Internet of Things (IoT)-enabled smart gadgets and easy connectivity. These smart gadgets are more susceptible to malicious practices found in network traffic, which is one of the biggest challenges in cyber security domain. As a result, many systems and end-users are adversely affected by this practice. However, intrusion detection systems (IDS) are often applied to guard against cyber-attacks. Since, IDS plays a key role in detecting and preventing cyber-attacks in IoT-enabled networks, but design of an efficient and fast IDS system for cyber-attack detection is still a challenging research issue. Moreover, IDS datasets contain multiple features and to design an efficient and fast IDS, feature selection (FS) is an essential mechanism to remove the irrelevant and redundant features from large IDS datasets. Thus, this paper has proposed a hybrid feature selection scheme in which statistical test-based filter approaches such as Chi-Square (χ^2) , Pearson's Correlation Coefficient (PCC), and Mutual Information (MI) are combined with a Non-Dominated Sorting Genetic Algorithm (NSGA-II)-based metaheuristic approach for optimization of features. In the proposed scheme, filter-based methods are employed to rank the features for guided population initialization in NSGA-II for faster convergence towards a solution. Performance evaluation of the proposed scheme is evaluated using the ToN-IoT dataset in terms of number of selected features and accuracy. Experimental outcomes are compared with some latest state-of-art techniques. Result analysis confirms the superior performance of the proposed scheme with minimum number of optimized features (only 13 out of 43 features) and maximum accuracy (99.48%). [ABSTRACT FROM AUTHOR]

The narrowband amplitude demodulation of vibration signals is widely used to extract components carrying information about rolling bearing faults for condition estimation. However, precisely selecting the frequency band determines the quality of demodulation. Although transient detection based methods have achieved satisfactory performance in some scenarios, they may be severely disturbed by strong random impulses or harmonics. Additionally, frequency division strategy is always considered as rough. Aiming at these problems, a new method is proposed to select the optimal frequency band by combining the frequency division strategy, genetic algorithm (GA) optimization, and an automatic fault characteristic order (FCO) search algorithm. Aimed at a signal, its sub-signals are extracted according to the frequency division strategy and the order tracing technology is implemented to obtain the order spectrum of each sub-signal at first. Then, the FCO and its multiples (FCOs) are automatically searched from each order spectrum, and their amplitude summation is calculated. Afterwards, the ratio of FCOs summation to noise amplitude (RFN) is measured as the basis to construct an RFNgram for selecting the primary optimal frequency band Finally, GA is implemented to select the optimized optimal frequency band where the signal is demodulated for estimating the bearing condition. RFN measures both the impulsiveness and periodicity of signals, and GA optimizes the result from a RFNgram, essentially making up for the shortcomings of previous methods under the condition of multiple interferences. RFN is measured through the order spectrum, which also allows it to be applicable to varying speeds, thus achieving a more comprehensive range of industrial applications. Three case studies are implemented to present the superiority of the proposed method in condition estimation for rolling bearings by comparison with four classical or advanced methods. • An automatic FCO search algorithm is proposed, which avoids any prior knowledge. • RFN simultaneously measures both the impulsiveness and periodicity of signals. • GA is applied for more finely selecting the OFB without heavy computational burden. • The proposed method maintains its effectiveness under varying speeds. [ABSTRACT FROM AUTHOR]

In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers of a pre-trained architecture and connecting them to fully-connected layers to adapt them to a new problem. Consequently, the configuration of the these layers becomes crucial for the performance of the model. Unfortunately, the optimization of these models is usually a computationally demanding task. One strategy to optimize Deep Learning models is the pruning scheme. Pruning methods are focused on reducing the complexity of the network, assuming an expected performance penalty of the model once pruned. However, the pruning could potentially be used to improve the performance, using an optimization algorithm to identify and eventually remove unnecessary connections among neurons. This work proposes EvoPruneDeepTL, an evolutionary pruning model for Transfer Learning based Deep Neural Networks which replaces the last fully-connected layers with sparse layers optimized by a genetic algorithm. Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show the contribution of EvoPruneDeepTL and feature selection to the overall computational efficiency of the network as a result of the optimization process. In particular, the accuracy is improved, reducing at the same time the number of active neurons in the final layers. [ABSTRACT FROM AUTHOR]

A multi-parameter sensitivity analysis method was proposed based on Latin hypercube sampling and the rank correlation method. The general genetic algorithm was improved by incorporating a niche parallel algorithm, suspicious peak point judgment, and a local search strategy, and both the improved genetic algorithm and multi-parameter sensitivity analysis method were implemented through C++ programming. The hydrogen pressure-composition isotherm (PCT curve) was found to play an important role in the study of hydrogen storage properties of metallic materials, and its reaction enthalpy change term was widely replaced by a power series form of smoothing. Based on the improved multi-parameter sensitivity analysis method, the effects of the range of parameter values and the number of samples on sensitivity were investigated, where the sensitivity ranking of the 10 parameters in the classical model was obtained. In addition, the equilibrium hydrogen pressure expression models of the highest order i of the power series from 3 to 9 were developed. The influence of the highest order on the optimization of multi-parameter identification was compared using an improved genetic algorithm, and we concluded that the eighth order polynomial model had the optimal convergence and fitting effect. Based on the inspiration of the function trend shape, two innovative forms of the PCT model power series were proposed in this work. Combined with typical examples, three high-order models were fully evolved, and a better power series form was obtained compared to the traditional model. • Two improved approaches were used to smooth-fit the hydrogen pressure-composition isotherm (PCT curve). • Based on the inspiration of function trend shape, two innovative forms of the PCT model power series were proposed. • The influencing factors of parameter sensitivity of PCT curve were determined and the applicable conditions were discussed. • The PCT curve optimized by intelligent algorithm was in good agreement with the experimental value. • An evolution process of one new power series form was more excellent than the classic one. [ABSTRACT FROM AUTHOR]

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others. [ABSTRACT FROM AUTHOR]

This paper constructs four integrated power generation systems using liquefied natural gas (LNG) cold energy and geothermal energy. These systems contain a geothermal single flash cycle, LNG expansion process and organic Rankine cycle (ORC). Different mathematical models are established to simulate the systems under steady-state conditions respectively. The influences of key working parameters on energy efficiency, exergy efficiency and levelized cost of energy (LCOE) are analyzed, and the comprehensive performances are estimated. The results show that increasing separator pressure, LNG turbine and Rankine turbine inlet pressures could increase the energy and exergy efficiencies. Also, a higher LNG turbine outlet pressure leads to a higher LCOE. To achieve the best system performance, single- and multi-objective genetic algorithm (GA) based optimizations are performed respectively. By single-objective optimization, GSF-TSORC and GSF-TPORC can reach the highest energy and exergy efficiencies of 34.80% and 52.61% respectively, and GSFC has the lowest total cost rate of 0.326 M$·year−1. After using multi-objective optimization, the energy and exergy efficiencies of GSF-TPORC are 29.56% and 51.29% respectively, which are superior to the other three systems, from the thermodynamic point of view. From an economic perspective, GSF-ORC has the lowest LCOE of 0.028 $·kWh−1, implying that it is the best system. [ABSTRACT FROM AUTHOR]

In this paper, a site-specific optimization procedure aimed at finding the optimal substructures of a 10 MW Floating Offshore Wind Turbine (FOWT) is presented. An in-house developed Frequency Domain (FD) model is adopted for the simulation of the coupled system. Two Mediterranean sites have been chosen for the characterization of the metocean environments. 20-year databases have been used to obtain the joint distributions of wind speed, significant wave height and peak spectral period. The optimizations, performed adopting a Genetic Algorithm (GA), are aimed at reducing the costs of the floating substructure, controlling the maximum system response under both extreme and fatigue wind-wave loads. Results show that the optimized solution significantly reduces the system cost with an acceptable increase of the loads, opening interesting perspectives for the reduction of the Levelized Cost of Energy (LCOE) in sites characterized by mild sea states and low wind resource. • A site-specific optimization procedure for a 10 MW semisubmersible wind turbine is proposed. • Simulations are performed using a 7-DoF coupled frequency-domain model. • Optimizations are performed considering two installation sites in the Mediterranean Sea. • Objective functions are: maximum tower load, manufacturing cost and fatigue damage. • Results show the existence of improved solutions compared to an upscaled configuration. [ABSTRACT FROM AUTHOR]

A formation mission scenario is considered, consisting of small satellites (Chasers) that must keep a reference trajectory with respect to a larger and non-cooperating Target satellite. The Chaser must perform its formation guidance and control tasks within the framework of its thruster constraints: the case of a low thrust electrical engine is here considered. The constraints of such a thruster are relevant to number and orientation of thrusters, the admissible thrust levels and minimum and maximum ON time. The control algorithm developed to satisfy all these constraints is a model predictive control based on a mixed-integer genetic algorithm optimization, which provides a fast estimate of the necessary scheduled actions. The algorithm takes J 2 and drag perturbations into account; at the scope a differential orbital parameters linear relative dynamics model has been used, while the real-world dynamics is represented by a high precision orbital propagator. The developed orbital controller is particularly effective when trajectory keeping is required; different relative configurations, like Helix or Train formations, are taken as benchmark trajectories. The results show promising and robust performance in counteracting differential perturbations, even in the case in which the Target satellite performs scheduled orbital maneuvers which are not timely communicated to the Chaser satellite. • Micro satellites are characterized by hardware with limited capabilities. • Formation control can be much more demanding the classic standalone control. • An optimal control schedule with thruster and mission constraints is developed. • Mixed-integer GA optimization employed to compute control schedule. • Realistic performance has been tested with high precision orbital propagators. [ABSTRACT FROM AUTHOR]

The UT Austin team presents their methodology and result for GTOC11, constructing a hypothetical Dyson ring using asteroids encountered by 10 motherships departing from Earth. A pathfinding algorithm for the mothership is designed using a fast lookup table and a robust Lambert solver. Sequencing of unique mothership itineraries is performed by approximating the mass delivered to the ring via a linear data-based estimator trained on simulated asteroid-to-ring trajectories. A detailed derivation for the necessary boundary conditions of the constant-thrust-acceleration indirect optimization problem is presented, with key insights about the magnitudes of the co-states. An indirect boundary value solver finds feasible asteroid-to-ring trajectories for each of the 12 phasing locations in a high-performance parallel computing environment. A genetic algorithm informs the sequencing for the ring station arrival order that gives the highest performance index value. UT Austin's final submitted solution placed 4th in the competition, with a performance index value of 5885.5, 235 asteroids collected, and a minimum build-station mass of 1.1328 × 1 0 15 kg. • The UT Austin team presents their 4th place solution for GTOC11. • Multiple-leg impulsive trajectories are generated using spatial/temporal grid lookups. • Incoming and outgoing impulsive maneuvers are efficiently combined between legs. • A linear estimator aids selection of multiple-leg impulsive trajectory itineraries. • A genetic-algorithm-based scheduler delivers equal asteroid mass to 12 build stations. [ABSTRACT FROM AUTHOR]

The method used and results found by the team "ASTL-NUAA" from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition. • The method used and results found by NUAA for the GTOC 11 is presented. • The flyby sequence of asteroids is obtained by the two-level optimization algorithm. • For less computation burden, a filter strategy is proposed to filter the input of time-optimal problem. • To assign asteroids, the branch-and-bound algorithm is applied. [ABSTRACT FROM AUTHOR]

In this paper, the methods and results from Tsinghua University and Shanghai Institute of Satellite Engineering for the 11th Global Trajectory Optimization Competition (GTOC11) are presented. To deal with the complicated "Dyson Sphere" building problem, a three-stage procedure is conducted. First, the pre-analysis is performed to reduce search space. It is found that two-impulse maneuvers between asteroid flybys are near-optimal, the semi-major axis of the "Dyson Ring" should be better at 1.0–1.5 AU, and the larger arrival mass asteroids tend to be selected. Second, the globally optimal trajectory design problem is further divided into two sub-problems, the mothership trajectory design and the asteroid assignment to the "Dyson Ring" power stations. For the first problem, beam search is used to obtain numerous single mothership trajectories based on a pre-constructed flyby trajectory database of 3–8 asteroids. The overall trajectories and asteroids visited are obtained by selecting 10 mothership trajectories with a genetic algorithm. For the second problem, we build a database of optimal rendezvous times for all the 83,453 asteroids at different phase angles to reach power stations of different radii and phase angles, then a greedy algorithm is proposed to obtain the asteroid arrival schedule based on all the asteroids visited by motherships. Finally, local optimization of asteroid sequence and flyby epochs is conducted. The activation time adjustment in combination with indirect continuous-thrust trajectory optimization is used based on the global optimization result. In the final submission, motherships fly by 388 asteroids, and the minimum mass of twelve power stations reaches 94% of the theoretical upper bound, which is defined using the minimum-time orbital transfers with free initial and target phases. • The winning result submitted for GTOC 11 is discussed. • Solving the flyby by the rendezvous in combination with geometric corrections. • Optimizing the flyby times in asteroid chains to solve the time-dependent problems. • An exponential form of the heuristic function is used in beam search. • A low-thrust solution database is constructed to obtain accurate flight time. [ABSTRACT FROM AUTHOR]

• A Linde–Hampson refrigerator using blends based on R744/R1234ze(Z) is constructed. • Exergy analyses are conducted to perceive the possibilities of improvement. • Optimal refrigerating COP is globally optimized via genetic algorithm. • A basis for screening mixed refrigerants based on temperature glide is revealed. • The optimized Linde–Hampson system performs better than traditional systems. The theoretical simulation and thermodynamic analyses of the Linde–Hampson refrigeration system using multiple eco-friendly zeotropic refrigerants are conducted to compare the optimal COP, volumetric refrigerating capacity and irreversibility distribution at the cold space temperature of -45 °C to -25 °C. The genetic algorithm is employed on the global optimization of Linde–Hampson refrigeration system to find out the optimal operating pressures and proportions of mixed refrigerants. By comparison, the most potential binary and ternary refrigerants are selected as R744/R1234ze(Z) and R744/R1234ze(E)/R1234ze(Z), respectively. The temperature glide of ternary refrigerant is mainly regulated by the proportions of the middle-boiling-point and high-boiling-point components. The temperature glide of zeotropic refrigerants should well match the temperature span between heat reservoirs, since the matching degree plays an essential role in system performance, and it can be regarded as an important basis for the selection of mixed refrigerants. The results of comparison with the R744 two-stage compression transcritical refrigeration system and the cascade refrigeration system using R744 and R1234yf show that the Linde–Hampson system using suitable mixed refrigerants can obtain the greatest COP and can be considered as a supplement and alternative. The research results will provide a reference to the applications of the Linde–Hampson refrigeration system using eco-friendly zeotropic refrigerants for freezing process in considerations of environmental protection, safety, and efficiency. [ABSTRACT FROM AUTHOR]

Nonuniform irradiance on the Photovoltaic (PV) array would cause mismatch problem and decrease the generated power. To relieve the negative effects of partial shading, reconfiguring the interconnection of the PV array is a favorable solution. However, the current reconfiguration techniques are usually limited by different corresponding factors, e.g., not suit for various instantaneous shade cases, the shortage of experts' knowledge or large amounts of data, easy coverage to local optima, and the strict assumptions for application. Therefore, in this paper a unsupervised adaptive PV array reconfiguration technique is proposed with a Divide and Conquer Q-Learning (DCQL) based array reconfiguration scheme designed to decrease the influence of partial shades. To testify the performance of the proposed PV array reconfiguration scheme, mathematic theoretical evaluation and electric characteristic analyses are made and compared with those obtained by the conventional Total-Cross-Tied (TCT) arrangement, SuDoKu connection, Genetic Algorithm (GA) based scheme, as well as some lately proposed Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimizer (HHO), and Lo Shu (LS) technique based reconfiguration methods. Besides, additional estimation indices, including mismatch loss, fill factor, percentage power loss and percentage power enhancement are calculated with substantial analyses and comparisons. Subsequently, the application of the proposed scheme in real-time conditions is evaluated, with energy saving and income analysis estimated in comparison with other eminent challenger reconfiguration schemes. At last, a comprehensive qualitative comparison assessment, with respect to dispersion ability under partial shading conditions (PSC), sensor requirement, wiring complexity and capital investment is carried out and proves the versatility of proposed methodology. • States, actions and rewards are designed in the Q-Learning based reconfiguration. • The combination of divide and conquer principle fastens the decision making process. • The proposed reconfiguration is estimated better performance through variety contrasts. [ABSTRACT FROM AUTHOR]

• The performance of batch picking (BP) and batch-synchronized zone picking (BSZP) are compared with learning effects. • The heuristic methods, i.e. genetic algorithms, are proposed to solve BP and BSZP strategies. • With learning effects, although BSZP needs to travel longer than BP , it needs a shorter search time than BP. • If pickers' working days are long enough, the search time of BP becomes closer to BSZP , leading BP to outperform BSZP. • Which strategy outperforms mainly depends on learning rates, storage assignments, and pickers' working days. The performance of two common picking strategies, batch picking (BP) and batch-synchronized zone picking (BSZP), are compared by considering pickers' learning effects in online-to-offline (O2O) groceries. As we know from previous research, one major advantage of BSZP is that pickers can be more familiar with the item locations because each picker works in a relatively smaller picking area. However, few papers have formulated a quantitative method to verify this advantage. To address this, we use pickers' learning effects to quantify their familiarity with the item locations, and then compare the picking efficiency in BP and BSZP with learning effects. The optimization models of BP and BSZP are formulated to minimize the total service time, and heuristic methods are proposed to solve these models. The case study is implemented under three order types and three storage assignment policies, i.e. Random, ABC-1, and ABC-2. The results of the study indicate that with learning effects, although BSZP needs a longer travel time than BP , it requires a shorter search time. Which strategy, BP or BSZP , is more efficient mainly depends on the learning rates, storage assignment policies, and pickers' working days. When the storage assignments are Random and ABC-1 , a smaller learning rate can lead BSZP to perform better than BP. When the storage assignment is ABC-2, BSZP performs better than BP. When pickers' working days are long enough, the learning effect is weakened, and BP outperforms. Awareness of the learning effects is required to choose a better picking strategy, evaluate more accurate picking efficiency and hire an appropriate number of pickers. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new application of WDE for estimation of nonlinear Hammerstein-Wiener systems. • Designed WDE is implemented for measured input, internal and output data viably. • The sundry scenarios of HWM establish robustness, stability and convergence of WDE. • Statistical observations prove efficacy for controlled autoregressive scenarios. Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability. The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters. The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials. Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme. Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and Marine Predators Algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators. [ABSTRACT FROM AUTHOR]

A large number of prefabricated components are composed of different types of houses. The on-site assembly sequence of components is also the key link of the construction organization of the project. The assembly sequence obtained by manual experience is easy to omit components and interfere with each other. According to the weight and assembly process of construction components, the evaluation index of mechanical assembly process is introduced to clarify the relationship between the number of components and the assembly process. In the aspect of intelligent optimization algorithm, the simulated annealing program module is introduced into the existing genetic algorithm to form a new simulated annealing genetic algorithm. The comparative study of the calculation results shows that the simulated annealing genetic algorithm can search the global optimal assembly sequence scheme more accurately and efficiently, and meet the evaluation indexes at the same time. The mechanical simulation technology is innovatively used to simulate the assembly of building components, and the simulation process is used to guide the on-site construction process. [ABSTRACT FROM AUTHOR]

Wastewater Treatment Plants (WWTPs) are considered as an Ultra Low Head (ULH) hydraulic resource. This has provoked an inadequate extraction of its potential energy by Micro Hydroelectric Plants (MHP) due to the improper relationship between the size and performance of the turbine. Thus, this work proposes the optimized design of a turbine composed of an intake-runner-diffuser, which looks for the maximum energy extraction from this kind of hydraulic resource. The annual Flow Duration Curve (FDC) of a WWTP effluent was examined to determine the flow rate with which the maximal available energy could be extracted through an adequate runner design. A 3D computational model of the intake-runner-diffuser was optimized by coupling a Computational Fluid Dynamics (CFD) software with a Multi Island Genetic Algorithm, while the pressure and flow field of the selected cases were qualitative and quantitatively analyzed. It was found that with the lowest Reaction Degree (RD), a propeller turbine reached its highest efficiency and pressure ratio coefficient, extracting 76% of the annual available energy of the hydraulic resource. In this way, the maximal exploitation of a ULH resource through the turbine design of the MHP system instead of adapting turbines from Large Hydropower Plants (LHP) could help to establish an actual and attractive project cost-benefit ratio. [ABSTRACT FROM AUTHOR]

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). At 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 the detriment of others, concluding that the technical approach is preferable to the economic one. [ABSTRACT FROM AUTHOR]

[Display omitted] • Review of the knowledge structure using CiteSpace, VOSviewer, and SciMAT. • Visualizing the evolution of traffic collision reconstruction: the field is developing. • Analyzing co-word, co-citation, and major themes in traffic collision reconstruction. • Authors and institutions need more collaboration. • Head injury and uncertainty are hotspots: interdisciplinary research is the future. Traffic collisions are incidents with high fatality rate which generate billions of US dollars of loss worldwide each year. Post-collision scene reconstruction, which involves knowledge of multiple disciplines, is an important approach to restore the traffic collision and infer the cause of it. This paper uses software CiteSpace, VOSviewer, and SciMAT to conduct a visualization study of knowledge mapping on the literature of traffic collision scene reconstruction from 2001 to 2021 based on the Web of Science database. Knowledge mapping is a cutting-edge research method in scientometric, which has been widely applied in medicine and informatics. Compared with traditional literature review, knowledge mapping with visual techniques identifies hot keywords and key literature in the field more scientifically, and displays them in schematic diagrams intuitively which allows to further predict potential hotspots. A total of 803 original papers are retrieved to analyze and discuss the evolution of the field in the past 20 years, from macro to micro, in term of background information, popular themes, and knowledge structure. Results indicate the number of publications in this field is limited, and collaborations among authors and among institutions are insufficient. In the meantime, mappings imply the top three hot themes being scene reconstruction, computer technology, and injuries. The introduction of AI related technologies, such as neural networks and genetic algorithms, into collision reconstruction would be a potential research direction. [ABSTRACT FROM AUTHOR]

This study aims at improving the understanding in order to optimise an aeration system for artificial destratification to control cyanobacteria growth in the reservoirs. Previous applications for artificial destratification in reservoirs were based on installations based on computational methods, where neither the effect of air bubble size and configuration nor the effect of air density in the bubble plume could be investigated. This study seeks for an optimized design with the help of experimental and numerical analyses. In order to perform experimental studies, a novel water tank enabling the heating/cooling of the water column as desired and a diffuser system were manufactured. During the experimental studies, effect of bubble size, bubble slip velocity, and other parameters of air diffuser on destratification efficiency were investigated. Based on the nondimensional parameters, a new destratification efficiency formula is obtained by the Genetic Algorithm (GA) approach. Additionaly, the hydrodynamics of the water tank during the mixing process by air diffuser was simulated via 3D numerical model and validated with experimental results. The Eulerian multiphase model with the 'degassing' boundary condition and k-ω turbulence model are found to be suitable for the purposes of the study. Based on the error analysis of comparisons of the model and observations, the best configuration of air diffuser is proposed, and the numerical model is found to be successful in simulating the destratification of thermally stratified water columns by air diffuser. [ABSTRACT FROM AUTHOR]

• An embedding technique obtained the optimal number and location of BMPs in an urban watershed. • The Pareto front in the Em-RSM model was better composed than the genetic algorithm alone. • In addition to reducing the optimization time in the proposed model, the linear equation error was reduced. • The alternative linear model contributes significantly to the performance of the Em-RSM model for generating optimized responses. Best Management Practices (BMPs) are measures implemented to reduce urban runoff volume and pollution load. Determination of a cost-effective selection of BMP combinations is a challenge. In this study, an optimization model was developed to determine the optimal number, location, and type of BMPs with minimum cost and pollution load in the Majidieh catchment in Tehran, Iran. A novel framework was proposed combining the embedding technique with Response Surface Method (RSM) called "Em-RSM" in the form of a simulation–optimization (S/O) model. First, the storm water management model)SWMM(as the simulation model was linearized, and the linear programming results were used as the initial population of the genetic algorithm (GA). Then, the linearized model along with the SWMM model were alternatively used as the fitness function in the GA evolution process to increase the model run speed and results' accuracy. The results showed that the permeable pavement and infiltration trench were more effective than other BMPs because of the physical and local characteristics of the study area. It was demonstrated that the proposed model makes a considerable reduction in the model run time with acceptable accuracy in obtaining the compromise solution of the Pareto front. The proposed framework proved its effectiveness in the solution of GA-based S/O problems. It can also be applied in other case studies or optimization problems by replacing and simplifying the behavior of the simulation model in the optimization procedure. [ABSTRACT FROM AUTHOR]