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A robust trajectory planning method for non-cooperative target rendezvous is investigated based on closed-loop uncertainty analysis. The proposed method addresses the issue that uncertainties affect the optimality of objective functions and the reliability of constraints. Considering multiple uncertainties, a stochastic optimal rendezvous model with a robustness performance index and chance constraints is presented with line-of-sight dynamics. The guidance model, angles-only navigation model and control model are derived, and uncertainty propagation equations for closed-loop GN & C system is proposed by using linear covariance analysis. The stochastic optimal rendezvous model is transformed into a robust optimal rendezvous model by using 3- σ of the random variables. The genetic algorithm is used to solve the robust trajectory planning problem, and a series of examples demonstrate that the robust maneuver solution can be significantly better than the solution corresponding to deterministic trajectory optimization problem. [ABSTRACT FROM AUTHOR]

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of satellite formation reconfiguration from the best passive safety parameters while ensuring the minimum fuel cost and desired formation configuration. Based on the relative eccentricity and inclination vectors, a hybrid method is proposed that combines the analytical solution and the genetic algorithm optimization method. It transforms the fuel-optimal conditions into the constraints of optimization model, which theoretically ensures the optimal fuel cost. The proposed hybrid approach is applicable to satellite formation reconfiguration problems with variable relative eccentricity and inclination vectors and passive safety concerns. Compared with the optimal three-impulse strategy and the analytical method in two different reconfiguration scenarios, the proposed method is proven to be more general and has advantages in passive safety performance. [ABSTRACT FROM AUTHOR]

• This algorithm attains attitude inversion with less data, confirming accuracy and noise resilience. The attitude of space debris holds significant implications for active debris removal and collision warnings. This study constructs a comprehensive photometry dataset for the target, utilizing angle transformation relationships and drawing upon the CZ-4C photometric data tested in the laboratory. The photometry in the dataset with noise and translation simulates the telescope observed one. We use the genetic algorithm to search for attitude parameters of the photometry that matches the simulated photometry in the dataset under the constraint of attitude angle rules. The derived and preset values of attitude parameters exhibit a strong alignment, applicable to light curves with an amplitude of about 2.5 magnitudes and noise levels up to 1.0 magnitudes. This suggests that the algorithm's accuracy is validated against the preset values, demonstrating its robust resistance to noise. Furthermore, the algorithm proposed in this study for deriving attitude from photometry requires a small amount of photometry data. This study provides a promising algorithm for future attitude analyses from telescope-observed photometry. [ABSTRACT FROM AUTHOR]

• Interstellar exploration is defined as bi-objective multi-vehicle problem with profits. • Mission return increases approximately linearly with mission duration. • Lower probe numbers enable more efficient routing through shorter transfers. In the past decade, the discovery of exoplanets has sparked new interests in the idea of interstellar travel and exploration. Despite various proposals for probe concepts and relevant technologies, there is a lack of extensive literature on viable exploration strategies for journeys beyond a single star system. Such exploration strategies might not only have implications on technology development strategies for achieving interstellar exploration but could also enrich existing models for galactic exploration feeding into solutions of the Fermi Paradox. This article presents optimal strategies for the exploration of a large number of near-by stars, using a dedicated, novel methodology, which sets it apart from existing literature: For the first time, the mission design problem of interstellar exploration is redefined as a bi-objective multi-vehicle open routing problem with profits. It is tackled by an adapted hybrid multi-objective genetic algorithm, which is further improved and modified according to the problem characteristics (e. g. large search space). The overall mission model assumes probes traveling on straight trajectories, utilizing flybys, and maintaining an average velocity of 10% of the speed of light. Surpassing prior research that typically relies on statistical models or restricted star data, the star models are founded on the second Gaia data release (Gaia DR2), which represents the most extensive star catalogue to the date of this study and is employed for the first time in the context of interstellar exploration. The resulting star model contains a maximum of 10,000 stars within a spherical region around Sol, covering a distance of 110 light years. It is found, that the number of explored stars J 1 scales with mission duration J 2 and probe number m according to J 1 ∼ J 2 m 0.66 , which provides an initial guidance for future interstellar mission design. Furthermore, the routes and selection of stars vary depending on the number of probes used: When conducting missions with a large number of probes, stars in close proximity to the Solar System are given more focus. On the other hand, missions with a small number of probes include more distant stars to facilitate shorter transfers along the route. Based on these findings, the following recommendations for interstellar exploration strategies can be drawn: When energy resources such as fuel reserves are scarce and the exploration mission is not limited to nearby stars, low probe numbers are more efficient. In contrast, high probe numbers enable faster exploration of nearby stars but involve less resource-efficient transfers, making them a suitable option for small, remotely propelled probe concepts. To address crowding effects in high probe number missions, swarm-based probe concepts are recommended based on the scaling law characteristics derived. [ABSTRACT FROM AUTHOR]

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

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

For an operational satellite in orbit, potential risks may arise when an unknown target approaches. Therefore, it is essential for the satellite to infer the intentions of the target in space. To achieve this objective, this paper proposes a method of sequential intention inference based on meta-fuzzy decision tree. The proposed method initially defines three situational functions: flyaround, collision, and observation. These functions help organize the inherent characteristics found in the trace and relative attitude of the target. Subsequently, by combining fuzzy decision tree (FDT), spatio-temporal modeling based on Long Short-Term Memory (LSTM), and task migration using meta-learning techniques, we can realize sequential intention inference for the target while ensuring quick adaptation to new tasks. Additionally, our proposed method takes into account the impact of fuzzy elements within FDT. Through quantum genetic algorithm (QGA) optimization, these fuzzy elements can be fine-tuned to enhance intention inference accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a mission concept designed for satellite-to-cell connectivity through a fractionated CubeSat approach, aiming to fulfill the objectives set by the BlueWalker-3 specifications. The envisioned system consists of 41 CubeSats, each adopting a 16U form factor, to provide dedicated coverage across specified sub-regions, as a reasonable solution. The pre-Phase-A design capitalizes on commercially available components with proven flight heritage, ensuring reliability and cost-efficiency. A genetic algorithm (GA)-based method has been introduced to tackle the complex challenge of designing a large antenna's folding pattern, with an analysis on how this design influences the overall system cost. The study conducts an in-depth analysis of system architecture and operational scenarios. Employing Monte-Carlo simulations, it assesses the Life Cycle Cost (LCC) and the system's resilience to failures, comparing it with similar configurations differing in CubeSat size and number. The results demonstrate that a fractionated approach significantly enhances the mission's scalability and robustness, providing substantial operational flexibility. The comparison highlights a cost-effective configuration within fractionated systems, emphasizing the proposed system's potential advantages. Furthermore, the paper addresses the environmental implications of deploying a fractionated spacecraft constellation, especially concerning astronomical studies. It underscores the CubeSats' minimized brightness and their reduced potential to interfere with telescopic observations, positioning the system as a more compatible option with nighttime sky studies. This consideration is particularly pertinent amidst increasing concerns over the impact of satellite constellations on astronomical research and night sky quality. • Introduction of a novel mission concept employing fractionated spacecraft for satellite-to-cell connectivity, showcasing an innovative design utilizing 41 CubeSats. • Comparative analysis demonstrating the proposed system's performance and cost advantages over the BlueWalker-3 mission, with findings supported by Monte-Carlo simulations. • Detailed discussion on the use of commercially available off-the-shelf components, highlighting the system's feasibility and reliability. • Exploration of the fractionated system's scalability and robustness, revealing potential for enhanced mission flexibility and economic efficiency. • Addressing the impact on astronomical observations, presenting a significant reduction in brightness and interference, enhancing compatibility with observational studies. [ABSTRACT FROM AUTHOR]

With the increase of the complexity of space environment and the improvement of satellite maneuverability, the satellite maneuver game has become the focus of space research. In view of the need for pre-observation during debris removal or on-orbit maintenance missions, a scenario of observation and counter-observation similar to the pursuit-evasion game is proposed. In this paper, the impulsive observation and counter-observation game (OCOG) of two satellites is comprehensively investigated using a multi-objective optimization method. A sequential decision impulsive maneuver model is constructed and the impulsive OCOG is transformed into a bilateral optimization problem in terms of multiple constraints such as maneuverability, illumination angle, and fuel consumption, etc. To determine the optimal impulsive maneuvers for both satellites, the ring topology multi-objective particle swarm optimization (RTMOPSO) is designed. This novel algorithm uses adjacent particles to form multiple small populations to make the population converge in multiple directions during the optimization process, avoiding the single convergence of the population. Compared with non-dominated sorting genetic algorithm II (NSGA-II) and self-organized speciation based multi-objective particle swarm optimization (SSMOPSO) in the OCOG scenario, proposed method always has superiority in the game. Additional simulation is conducted and shows that the proposed algorithm is also applied for the case of non-maneuvering satellites. This research provides an effective maneuver strategy for future space game. [ABSTRACT FROM AUTHOR]

• Fusion of advanced deep-learning models CNN, LSTM, DNN and RNN with GA optimizes GWPZ mapping. • Overlaying GWPZ with drought maps unveils links between drought levels and groundwater potential. • CMIP6 climate models project changing GWPZ under different socioeconomic scenarios. • Workshops and local collaboration empower communities for sustainable water management. This study presents a novel approach to groundwater resource assessment and drought vulnerability, achieved through integrating cutting-edge deep-learning algorithms into a comprehensive framework. In regions susceptible to drought, ensuring groundwater availability is of paramount importance. Addressing this critical need, our research employs an ensemble of advanced algorithms, including long short-term memory, convolutional neural network, deep neural network, and recurrent neural network. These algorithms are further enhanced through optimization using a genetic algorithm to map groundwater potential zones (GWPZ). Leveraging model validation based on the area under the receiver operating characteristic curve (AUCROC), the long short-term memory-genetic algorithm model emerges as the superior algorithm, boasting the highest values of AUCROC: 0.995 for training and 0.996 for testing). Utilizing this optimized model, this study developed the GWPZ map, subsequently overlaying it with established drought maps developed by the Bangladesh Agricultural Research Council across distinct periods—Pre-Kharif, Kharif, and Rabi. The derived baseline GWPZ spatial distribution revealed five categories—very low (34.99%), low (27.67%), moderate (13.26%), high (11.71%), and very high (12.37%)—and their intersection with drought-prone regions, indicative of probabilities of drought occurrence ranging from very severe to low (15.14% to 24.69%). Moreover, employing the best-predicted model, this study projected future GWPZ for 2050 and 2100 using the coupled model intercomparison project 6 general circulation model data under the ambit of three distinct shared socioeconomic pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our findings suggested a contraction in the groundwater potential area by the end of the twenty-first century (2100). This pioneering integration sheds light on the intricate relationship between groundwater availability and drought susceptibility, furnishing invaluable insights for formulating targeted water resource management strategies. By amalgamating advanced computational techniques with geospatial analyses, this research contributes to a more comprehensive grasp of water resource dynamics within the context of escalating climate challenges. Consequently, it offers a foundation for informed decision-making and implementing sustainable water management practices in regions grappling with the dual challenges of groundwater scarcity and recurrent droughts. [ABSTRACT FROM AUTHOR]

The amount of space debris is rising rapidly, and the number of satellites launched by humans is also growing. This poses a serious threat to the security of many important space assets including the International Space Station and the China Space Station. Due to the frequent occurrence of hazardous space conjunction (potential collisions) events, we aim to use a constellation composed of a few optical satellites to observe these space dangerous conjunction events, thereby improving the observation accuracy and timeliness of key conjunction events. We consider a variety of factors to establish an optical nanosatellite constellation design model, develop constellation observation efficiency evaluation criteria, construct a constellation configuration evaluation function, and design the performance parameters of space-based optical telescopes. The optimal orbit parameters and constellation configuration are obtained by using the genetic algorithm for optimization calculation. It is determined that a constellation of 6 satellites can track and observe more than 96% of potentially dangerous conjunction objects 24–48 h before a dangerous conjunction occurs. According to the observation data, high-precision orbit information of space objects can be obtained, which can reduce the collision false alarm rate by 99%. As a result, operators are notified to take collision avoidance measures only when the space object is truly in danger. This approach greatly reduces the number of avoidance operations caused by false early warnings, lowers the operating cost of space assets, and ensures the safety of space assets. [ABSTRACT FROM AUTHOR]

• A new constellation lifecycle cost optimization problem was introduced. • Useful insights to the satellite burn-in problem can guide satellite manufacturers. • Long testing durations are undesirable; testing can be traded off with number of satellites produced. • Combining burn-in, warranty and availability analysis opens new paths for constellation research. Constellation lifecycle costs are an important metric for constellation designers. Recurring costs of mega constellations in particular are large relative to the non-recurring costs, yet little attention has been given to the reduction of recurring costs. Moreover, few studies exist on the impacts of pre-launch activities such as satellite testing on subsequent operational reliability. We thus study the lifecycle cost impacts of environmental stress screening during acceptance testing to minimize constellation lifecycle costs. Parallels are drawn with the field of electronics testing, and costs arising from burn-in, replacement and availability are considered in our formulation. A single-objective cost minimization problem and a multi-objective cost and unavailability minimization problem are presented to capture different mission applications that require different objectives. The effects of system homogeneity and the use of sparing strategies are investigated. We propose genetic algorithms to solve the optimization problems and give some insight into how burn-in should be carried out for minimizing total costs. The optimization is also applied to a case study of a proposed mission scenario and the constellation lifecycle costs of an optimized burn-in plan are compared to those of a typical "reliability target" burn-in plan. The optimization resulted in lifecycle cost savings of around 19% while significantly reducing the burn-in duration, showing much promise in the burn-in cost optimization approach. [ABSTRACT FROM AUTHOR]

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

To improve the cell productivity of Corynebacterium glutamicum , its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtain training data for the DNN, experimental design with an orthogonal array was set up using a chemically defined basal medium (GC XII). Based on the cultivation results for the training data, specific growth rates were observed between 0.04 and 0.3/h. The resulting DNN model estimated the test data with high accuracy (R2 test ≥ 0.98). According to the validation cultivation, specific growth rates in the optimal media components estimated by DNN-BO and DNN-GA increased from 0.242 to 0.355/h. Using the optimal media (UCB_3), the specific growth rate, along with other parameters, was evaluated in batch culture. The specific growth rate reached 0.371/h from 3 to 12 h, and the dry cell weight was 28.0 g/L at 22.5 h. From the cultivation, the cell yields against glucose, ammonium ion, phosphate ion, sulfate ion, potassium ion, and magnesium ion were calculated. The cell yield calculation was used to estimate the required amounts of each component, and magnesium was found to limit the cell growth. However, in the follow-up fed-batch cultivation, glucose and magnesium addition was required to achieve the high initial specific growth rate, while appropriate feeding of glucose and magnesium during cultivation resulted in maintaining the high specific growth rate, and obtaining a cell yield of 80 g/L ini. • A novel growth medium for Corynebacterium glutamicum was developed using deep neural networks and optimization algorithms. • During the validation cultivation, high specific growth rates were observed in the proposed media. • The limiting factors for high biomass yield were identified in the proposed medium. • To optimize productivity and yield, a fed-batch culture was designed using the proposed medium. [ABSTRACT FROM AUTHOR]

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

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

In this article, we investigate the interaction of multiple steps in hybrid rocket fuel grains. Previous studies have shown the potential of single steps to increase the regression rate. In this study, we evaluate if the single step results can be translated to a multi-step approach. Of special interest are the interactions between the different step configurations. Four grain profiles are assembled by using fuel grain segments with different inner diameters, therefore forming a multi-step approximation of the profiles that can easily be manufactured and scaled up. The multi-step grains enhance the regression rate because of increased mixing and convective heat transfer induced by the recirculation zones of the steps. The experimentally obtained regression rate profiles are similar to the single step experimental data. This signifies that they are reproducible and therefore predictable. Most importantly, multiple steps do not interfere negatively with each other, which proves that steps can be used to approximate different fuel grain profiles. We show experimentally that the regression rate can be increased up to 81% by accumulating the regression rate enhancing potential of single steps. Moreover, we developed a genetic algorithm to estimate the spatially resolved Marxman parameters with only one single burn. • Results from single steps translate to multiple steps. • Regression rate profiles reproducible for multi-steps. • Multi-step grains increase regression rate by up to 81%. • Spatial resolution of Marxman law possible with genetic algorithm. • Complex fuel profiles can be approximated with steps. [ABSTRACT FROM AUTHOR]

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

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

In order to solve the deficiencies of traditional board-level assembly CubeSat, such as low modularity, complex assembly process and poor scalability, a sandwich module assembly structure and method with no drive components is proposed, which can achieve quick connection and flexible assembly between modules. First, the proposed CubeSat standard module can be compatible with the standard interface of traditional CubeSat. The 4-in-1 interface of Mechanical-Electrical-Thermal-Data does not occupy the internal space of the standard module, and the electrical and thermal interfaces meet the requirements of CubeSat in-orbit work. Secondly, in order to improve the reliability of the connection between the modules, a plug-in connection scheme of the mechanical interface was designed. Finally, a genetic algorithm was used to optimize the interface size, so that it has better plug-in performance. The simulation results show that the performance of the male interface of titanium alloy material is the best, and the error between the finite element analysis and genetic algorithm results is less than 4.8 %, which is consistent with the actual tensile test results of the optimized prototype, and meets the mechanical requirements of the launch process and space orbit operation for CubeSat. The test results can guide production and provide a reference for subsequent engineering applications of CubeSat modular assembly. • In this paper, a standardized modular assembly method of CubeSat sandwich structure is designed. The assembly structure does not include driving parts, is simple in form, can be quickly connected, is flexible in assembly mode, and does not occupy the internal space of the standard module. • A standard 4-in-1 connection device was designed in this paper, including four interfaces: mechanical, electrical, thermal and data, which can achieve "plug and play". • This paper uses genetic algorithm to optimize the structure of the mechanical connection male joint, and obtains the optimal configuration of the male joint. This ensures the reliability, firmness and stability of mechanical connections, and meets the mechanical requirements of CubeSats during launch and in orbit. [ABSTRACT FROM AUTHOR]

In the context of Mars exploration, the Mars multi-rotor aircraft plays a significant role, with its blades being the key components for achieving flight. Due to the thin Martian atmosphere, conventional Earth blades struggle to generate the required thrust for comprehensive aerial missions on the Red Planet. To solve these challenging conditions, there arises a compelling need to refine the rotor's design, optimizing it to provide sufficient thrust while minimizing power consumption. In this research, a novel method is introduced, combining genetic algorithms and computational fluid dynamics simulations. This approach focuses on enhancing the three-dimensional structure of the rotor blade. The results of this research are verified in experiments conducted within the Martain Atmosphere Simulator, where the blade after optimization demonstrated the ability to achieve the same level of thrust while significantly reducing power consumption. Consequently, this study represents a promising avenue for improving the efficiency of Mars multi-rotor aircraft. • The three-dimensional structure of the blade employed in the Martian atmosphere is optimized. • The three-dimensional structure of the blade is optimized by genetic algorithm. • Optimization results of the blade are verified in the Martain Atmosphere Simulator. • The blade is optimized with figure of merit as the optimization objective. [ABSTRACT FROM AUTHOR]

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

The research on particle size classification in lunar microgravity has gradually attracted the attention of scholars. Based on the discrete element method (DEM), this work proposes a double-layer auxiliary screening vibrating screen in the form of intermediate feeding, and takes the screening efficiency and screening time as the evaluation indicators. Firstly, the Helmholtz-Maxwell coil is used to simulate the force of particles under lunar microgravity, which verifies the reliability of the numerical simulation of particles under lunar microgravity based on the discrete element method. Then, the influence of the parameters of each screen on the comprehensive performance of screening was analyzed by a single-factor experiment. Finally, based on the BP (Back Propagation) neural network optimized by genetic algorithm, the evaluation model of screen parameters and screen performance is established, and the multi-objective genetic algorithm is used to optimize the screen parameters combination scheme with excellent performance. An efficient comprehensive model of screening is provided to improve the screening performance and the design of the screen machine under lunar microgravity. [ABSTRACT FROM AUTHOR]

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

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

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

The mesh reflector suffers inevitably from complicated environmental interferences during long-term on-orbit operation and consequently the reflector may deviate significantly from its initial state. It is thus crucial to identify the deformed reflector on-orbit in order to evaluate timely the mesh reflector accuracy. However, the existing reflector identification methods are difficult to achieve this object as the most of them need to install additional equipment on antenna or satellite. Aiming at realizing on-orbit mesh reflector identification, a novel identification approach based on tie cable tensions sensing (TCTS) is proposed in the paper. The TCTS method leverages real-time measurements of tie cable tensions through embedded force sensors to calculate the remaining cable tensions required to address reflector deformations. For this purpose, an optimization problem is established based on tension balance equation and then solved using a proposed iteration solution algorithm incorporating the genetic algorithm. The actual configuration of mesh reflector is then determined by all cable tensions according to the force density method and the reflector can be identified ultimately. The simulated studies on reflector identification of a mesh reflector with 8 m aperture diameter are conducted under temperature loads and cable stiffness degradations respectively. The results indicate that the TCTS method possesses high reflector identification accuracy and good anti-noise capability under different environmental interferences. Furthermore, a reflector identification method based on partial tie cable tensions sensing (PTCTS) is developed in order to reducing system complexity and cost. The sensitivity analyses of tie cable tensions are performed in order to provide a reference for location placement of force sensors in the PTCTS method. The simulated results indicate that the optimized location placement scheme can enhance significantly the reflector identification accuracy of PTCTS method. Besides, the PTCTS method exhibits equal anti-noise capability with the TCTS method. Finally, the experimental studies are conducted on reflector identification of a mesh reflector test model with 1.5 m diameter and 0.5 m height under cable stiffness degradations. Through comparing with the reflector identification results obtained by the photogrammetry method under different cable stiffness degradation degrees, the accuracy and effectiveness of the TCTS/PTCTS methods are demonstrated experimentally. • The mesh reflector is identified based on tie cable tension sensing (TCTS). • Partial tie cable tensions sensing (PTCTS) can reduce system complexity. • The TCTS/PTCTS method provides a promising way to identify mesh reflector in orbit. • The TCTS/PTCTS approach is demonstrated numerically and experimentally. [ABSTRACT FROM AUTHOR]

In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in the presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where we often evidence that inflicted with a certain disease, survival time would be under different stress factors like environmental stress, co-morbidity, the severity of disease etc. This work is concerned with a one-shot device data analysis and applies it to SEER gallbladder cancer data. The two-parameter logistic-exponential distribution is applied as a lifetime distribution. For robust parameter estimation, weighted minimum density power divergence estimators (WMDPDE) is obtained along with the conventional maximum likelihood estimators (MLE). The asymptotic behaviour of the WMDPDE and the robust test statistic based on the density power divergence measure are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to SEER gallbladder cancer data. Citing the importance of knowing exactly when to inspect the one-shot devices put to the test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is accomplished through the population-based heuristic optimization method genetic algorithm. • A robust inference based on weighted density power divergence. • Elaborated study of the asymptotic property. • Testing of hypothesis based on a robust statistic. • Search for optimum inspection times using genetic algorithm. • Application to SEER gallbladder cancer data. [ABSTRACT FROM AUTHOR]

We propose a new method, called a deep-genetic algorithm (deep-GA), to accelerate the performance of the so-called deep-BSDE method, which is a deep learning algorithm to solve high dimensional partial differential equations through their corresponding backward stochastic differential equations (BSDEs). Recognizing the sensitivity of the solver to the initial guess selection, we embed a genetic algorithm (GA) into the solver to optimize the selection. We aim to achieve faster convergence for the nonlinear PDEs on a broader interval than deep-BSDE. Our proposed method is applied to two nonlinear parabolic PDEs, i.e., the Black-Scholes (BS) equation with default risk and the Hamilton-Jacobi-Bellman (HJB) equation. We compare the results of our method with those of the deep-BSDE and show that our method provides comparable accuracy with significantly improved computational efficiency. • We introduce a deep-GA method. • The method provides a better accuracy and efficiency than the deep-BSDE solver. • Efficiency and accuracy is obtained by embedding a genetic algorithm (GA). • We solve the Black-Scholes with default risk and Hamilton-Jacobi-Bellman equations. [ABSTRACT FROM AUTHOR]

This study introduces a data-driven surrogating modeling approach for determining thermal parameters in undetermined thermal mathematical models (TMM) used in spacecraft systems. Maintaining appropriate temperature levels for onboard components is crucial for the success and safety of space missions. However, accurately determining thermal parameters in the undetermined TMM presents a challenge. To address this, we propose an approach that combines TMM correlation with test data and data-driven surrogate modeling. Our approach minimizes an objective function that measures the deviation between predicted temperatures from a surrogate model, incorporating thermal parameters, and steady state temperatures obtained from thermal vacuum balance test results. This optimization is performed using a genetic algorithm while imposing constraints to ensure the physical validity of the thermal parameters and correlation criteria related to temperatures. We demonstrate the effectiveness and validity of our approach by applying the obtained thermal parameters to the TMM. The resulting calculated temperatures exhibit excellent agreement with the test results. This methodology offers a user-friendly and reliable way to determine thermal parameters in undetermined TMM within spacecraft systems, ultimately enhancing temperature control and mission success. • Efficient thermal parameter determination in spacecraft TMMs through data-driven surrogate models. • TMM correlations achieved by minimizing temperature deviation with surrogate models and test data. • Reliable TMM correlations achieved by considering realistic constraints, overcoming thermal parameter non-uniqueness. • Reasonable selection of thermal parameters, enhancing reliability and reducing user knowledge requirements. [ABSTRACT FROM AUTHOR]

The problem of optimizing variable-specific impulse electric propulsion maneuvers is presented, proposing a novel multi-objective methodology for the computation of near-optimal planetocentric solutions. The methodology relates a parameter expressing the instantaneous maneuvering efficiency, or thrust effectivity, with the specific impulse through an arbitrary parametric function. The parameters of this function are determined by a multi-objective genetic algorithm, which minimizes propellant consumption and maneuver duration. The study includes the analysis of several scenarios, with the results demonstrating that the methodology can be successfully employed in the context of space mobility of small satellites. The findings reveal that variable-specific impulse maneuvers can achieve improvements exceeding 17% in both propellant mass and maneuver duration compared to a constant-specific impulse case. Consequently, the resultant Pareto fronts showcase enhanced trade-offs and more favorable solutions for the optimization problem. The results highlight the significance of this optimization methodology for electric propulsion technologies with throttling capabilities, particularly in systems with near-constant propulsive efficiency. [ABSTRACT FROM AUTHOR]

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

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

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

A two-step optimization algorithm is proposed to redesign a precision reference repeating sun-synchronous orbit for interferometric synthetic aperture radar satellites with injection error. This purpose is to design a new reference orbit for the satellite that deviates from the original preset orbit. Manoeuvring satellites to this new orbit consumes less propellant than manoeuvring to the original orbit. In addition, the repeatability of the orbit is a guarantee for obtaining good interference images, which puts higher requirements on the revisit accuracy of the redesign reference orbit. In the first step, the initial guess for reference orbital elements is designed by considering the global coverage of the Earth and the propellant consumption, taking into account the J 2 and J 4 perturbations. A search table of feasible solutions in the neighbourhood of the injection orbit is established. This approach, which is based on a search table, transforms the optimization of continuous variables in infinite space into the traversal of discrete variables in finite space, thus greatly reducing the optimization time. In the second step, the revisit accuracy of the satellite's full-dimensional state vector is optimized under the orbit propagation of a high-order gravity field. Unlike traditional methods that only optimize the revisit error of position, this paper also optimizes that of velocity. A new optimization method for full-dimensional revisit error of state vector is proposed, which combines nested differential correction with non-dominated sorting genetic algorithm-II. Compared with the traditional genetic algorithms, the proposed nested differential correction method provides the non-dominated sorting genetic algorithm-II with more accurate initial values, which reduces the iterations. Finally, the simulation results show that the total velocity change from the actual injection orbit to the redesigned reference orbit is halved compared to the original preset orbit. Moreover, the revisit errors of position and velocity are within 1 m and 1 m / s , respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • An end-to-end inverse framework achieves fast identification of 3D microstructures from tailored strength, stiffness, and fatigue property. • A deep learning and genetic algorithm framework that is capable of both forward and inverse predictions. • We demonstrated the performance of this framework in the context of Ti-6Al-4V in both forward/inverse directions and achieved tailored mechanical properties with large variance. • The framework is highly transferrable to other materials and scientific fields. Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V alloy to demonstrate the effectiveness and efficiency of this framework. We realize this by tailoring its 3D microstructures to achieve various yield strengths and elastic moduli across a large design space, while minimizing stress concentration factors. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to a wide range of materials and properties. Paired with emerging additive manufacturing techniques in controlling local microstructural features, our method offers far-reaching potentials for the accelerated development of application-specific, high-performing materials. [ABSTRACT FROM AUTHOR]

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

After the completion of the launch mission, the soft landing recovery and reuse of the carrier rocket's first-stage can effectively control the landing area to ensure safety and significantly reduce the cost of space launch transportation. The trajectory optimization of the whole process from the first section to the landing site is significant to fuel saving. In the entire process, the first-stage needs to go through three startup ignitions of course adjustment, power deceleration, and vertical landing. For this segmented fuel optimization problem, optimizing the thrust size and direction during three sections and the switching time between each section is necessary. Optimizing the vertical landing section is essential to ensure the accuracy of landing position and speed, which greatly complicates the whole process's optimization. Therefore, the optimization process of the vertical landing is substituted by a neural network predictor, which is used to obtain the mapping relationship between the initial state of the vertical landing section and the fuel consumption of the segment. Besides, the thrust profiles of the first two startup sections are reasonably handled based on physical cognition. The combination of convex optimization and neural network successfully converted the multi-stage optimal control problem into a parameter optimization problem and solved it by a genetic algorithm. Optimization results were compared with the conventional method, which indicated its superiority. [ABSTRACT FROM AUTHOR]

• An optimal path planning is proposed for spacecraft fleet to asteroids detumbling. • Optimal low-thrust trajectories for the spacecraft fleets in a 3D high perturbed asteroid environment is designed through a combined GA-Collocation method. • A deep neural network is used to rapidly approximate the gravitational acceleration of the asteroid. • The optimal problem utilized optimization method, as GA for guessing systematically the optimal initial costates, with high speed and accuracy be able to it yield consistent and reliable results. Following space discoveries, asteroids were found as rich sources of minerals and organic matter that can be exploited. In this paper, we present a precise, fast, and robust energy-optimal soft landing with a combined GA-Collocation method for a spacecraft fleet aimed at an irregular asteroid detumbling mission. It is assumed that the spacecraft fleet carried to the asteroid's equilibrium points introduced as the start locations of the mission. Here, the 433 Eros asteroid has been considered as the stony target known for its irregular elongated shape and nearly large dimension. We modeled the gravitational potential field by polyhedron as the most accurate one for such asymmetric objects. Hence, to decline the high burden computational of related equations in gravitational acceleration calculation, a Deep Neural Network with seven hidden layers and a relatively large dataset covering 6000 pairs has been developed. We utilized a Genetic Algorithm to guess systematically more optimal and reliable initial costates of some key parameters to raise the speed and accuracy of estimation. The acceptable efficiency and accuracy in optimal path prediction of the presented approach for a fleet of spacecraft approved throughout precise simulations. Eventually, the feasibility of the proposed approach is demonstrated through the corresponding results. [ABSTRACT FROM AUTHOR]

The development of deep space exploration has led to new exploration demands that cannot be met by traditional planetary rovers. This paper proposes a new wheeled rover that combines the advantages of passive all-wheel attachment and vibration reduction at high speeds. This rover can achieve vibration reduction in the vertical direction from ground excitation, preventing the instability of pitch and roll. Flexible wheels were used to reduce the high-frequency excitation of the ground on the rover. The design concept and mechanical principle of the proposed rover were comprehensively analyzed. A quasi-static kinematic model was developed to analyze the overstepping performance of the rover with this new design. Further, the response characteristics of the system to the ground excitation were investigated by vibration equations established using the Lagrangian method. The optimal parameters were obtained using a genetic algorithm to optimize the system with multiple objectives. Furthermore, the overstepping performance, all-wheel attachment, and vibration reduction of the planetary rover were verified through multi-body simulation. The proposed rover has considerable potential for missions such as the Mars sample return and planetary station construction. • A planetary rover with adaptive suspension for high-speed driving is proposed. • The theoretical analysis of the climbing ability of a planetary rover is provided. • A vibration modeling and evaluation method for proposed rover is established. • A variety of objective functions are proposed and optimized accordingly. [ABSTRACT FROM AUTHOR]

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

The "IADC space debris mitigation guidelines" requires that the post-mission orbit lifetime of low Earth orbiting (LEO) satellites should not be longer than 25 years, which is the key to debris mitigation. To analyze the degree of global compliance to this 25-year guideline, a fast and relatively accurate orbit life estimation algorithm is required. The traditional method uses numerical orbit propagation to accomplish the task, although its accuracy is high when a high-precision force model is used, the calculation is time-consuming. This paper proposes a new method for orbit lifetime estimation without relying on orbit propagation. A back propagation (BP) neural network is used to directly fit the relationship from the satellite parameters to the orbit lifetime. The space objects that have reentered the Earth's atmosphere are selected from the catalog database of the US Space Surveillance Network (SSN), and are used to generate samples for network training. The genetic algorithm is combined with the traditional back propagation algorithm for the training of the neural network. The results show that for the specific application background of identifying whether a satellite complies with the 25-year guideline, the orbit lifetime estimation accuracy of the BP network meets the demand to some extent. Moreover, because it does not involve orbit propagation, the BP network has a high computational efficiency in estimating orbit lifetime, showing a good application prospect. [ABSTRACT FROM AUTHOR]

• Two-level optimization solves the maneuver planning for multiple spacecraft close-range rendezvous. • Multi-impulse solved by trajectory planning is used in each rendezvous. • An impulsive correction method is proposed for precise rendezvous. • The surrogate model built by FNN accurately predicts the impulsive velocity increase for rendezvous. Rendezvous mission planning between service spacecraft and multiple close-range target spacecraft in the on-orbit service application is studied, using multiple impulses based on the Clohessy-Wiltshire equation as a maneuvering strategy. An impulsive correction method is proposed to avoid rendezvous errors. The optimization objectives of this work include the minimum total velocity increase and the minimum mission time. A two-level optimization approach is developed to find the Pareto-optimal set of the above multi-objective optimization problem. In the low-level optimization, the propellant-optimal impulsive trajectory for the rendezvous between the SSc and each target spacecraft at a fixed maneuver duration is solved by a proposed iterative optimization algorithm with a double loop. On the other hand, the rendezvous sequence and maneuver duration are optimized by a proposed neural-network-assisted evolutionary algorithm in up-level optimization, which combines the feedforward neural network with the elitist multi-objective genetic algorithm (NSGA-II). Finally, representative simulation examples are presented to verify the proposed optimization algorithm and demonstrate its superiority over the traditional algorithm. [ABSTRACT FROM AUTHOR]

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

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

• Solve a machine configuration problem of hybrid flow-shop manufacturing network reliability maximization. • Develop the absorbing Markov chain to evaluate the network reliability. • Apply a genetic algorithm to determine the optimal machine configuration. • Demonstrate the applicability and efficiency of the proposed approach by three manufacturing cases. In this study, we adopt a strategy of machine configuration to construct a reliable hybrid flow-shop manufacturing system (HFSMS) with reworking and scrapping actions, in which the reworking and scrapping actions are due to the yield rates of the configured machines. The machine configuration determines the machine suppliers and the number of machines for each production stage in the HFSMS, which may affect the stability of the HFSMS. The machines configured to a production stage in parallel are provided by the same supplier. Accordingly, each production stage has multiple states, following a probability distribution. The HFSMS is a typical stochastic-flow network under machine configuration, and network reliability indicates the probability that order demand d can be fulfilled by the HFSMS and used as a decision reference. Our study integrates genetic algorithm (GA) and absorbing Markov chain (AMC) to solve the reliability-oriented machine configuration problem of HFSMS subject to a configuration budget, where the GA is utilized to find the optimal machine configuration with maximum network reliability and the AMC model constructed based on the yield rates is used for network reliability evaluation. For validating the applicability and computational efficiency of the proposed AMC and GA-based approach, a simple manufacturing system and two practical manufacturing systems are used to compare the proposed approach with four popular meta-heuristic algorithms. The experimental results show that the proposed approach can find the exact optimal solution and has better computational efficiency than the other four meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

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

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