Search

Summary: Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach. [ABSTRACT FROM AUTHOR]

A wide range of applications of unsaturated hydraulic conductivity is well known in geotechnical, hydrological, and agricultural engineering fields. The standard prediction models for hydraulic conductivity function overlook the complexity of soil pore structure and employ a simplistic approach based on the bundle of capillary tubes. This study proposes an alternative approach employing pore network models calibrated to match soil water retention data to predict the hysteretic hydraulic conductivity function of granular soils. A novel approach to constructing a multidirectional pore network built on an irregular lattice with variable coordination numbers is presented for the realistic representation of soil voids. The geometric and topological parameters of the pore network model are optimized using the genetic algorithm, and adequate pore‐scale processes (piston‐like advance and corner flow during drainage and piston‐like advance, pore body filling, and snap‐off during imbibition) are modeled to get reasonable predictions of hysteretic hydraulic conductivity functions over the entire suction range of granular soils. Comparisons between the pore network model results, standard physically based models, and measured data for a variety of granular soils show that the proposed pore network has a superior performance over other models and compares favorably to the experimental data. [ABSTRACT FROM AUTHOR]

ABSTRACT The time domain is the most widely chosen alternative in mechanical fatigue tests to completely represent random events. However, the main disadvantages of this approach are the long time required to complete the tests and the high cost of the complex equipment. On the other hand, fatigue test in the frequency domain is a potential alternative to overcome these disadvantages. That is, it requires less sophisticated equipment, because it uses only simple actuators instead of servo actuators. Moreover, the duration of the fatigue test is reduced, because the total length of the event is replaced by constant repetitions and constant damage. The objective of this work is to propose and develop a methodology that uses the results of structural fatigue as a starting point, and through an optimization problem solved by the genetic algorithm, it determines which loads/displacements caused them in the frequency domain. The purpose is to assist future developments in fatigue tests, supplanting tests in the time domain without losing the correlation in results. [ABSTRACT FROM AUTHOR]

Testing is one of the most time‐consuming and unpredictable processes within the software development life cycle. As a result, many test case optimization (TCO) techniques have been proposed to make this process more scalable. Object Constraint Language (OCL) was initially introduced as a constraint language to provide additional details to Unified Modeling Language models. However, as OCL continues to evolve, an increasing number of systems are being expressed by this language. Despite this growth, a noticeable research gap exists for the testing of systems whose specifications are expressed in OCL. In our previous work, we verified the effectiveness and efficiency of performing the test case prioritization (TCP) process for these systems. In this study, we extend our previous work by integrating the test case minimization (TCM) process to determine whether TCM can also benefit the testing process under the context of OCL. The evaluation of TCO approaches often relies on well‐established metrics such as the average percentage of fault detection (APFD). However, the suitability of APFD for model‐based testing (MBT) is not ideal. This paper addresses this limitation by proposing a modification to the APFD metric to enhance its viability for MBT scenarios. We conducted four case studies to evaluate the feasibility of integrating the TCM and TCP processes into our proposed approach. In these studies, we applied the multi‐objective optimization algorithm NSGA‐II and the genetic algorithm independently to the TCM and TCP processes. The objective was to assess the effectiveness and efficiency of combining TCM and TCP in enhancing the testing phase. Through experimental analysis, the results highlight the benefits of integrating TCM and TCP in the context of OCL‐based testing, providing valuable insights for practitioners and researchers aiming to optimize their testing efforts. Specifically, the main contributions of this work include the following: (1) we introduce the integration of the TCM process into the TCO process for systems expressed by OCL. This integration benefits the testing process further by reducing redundant test cases while ensuring sufficient coverage. (2) We comprehensively analyze the limitations associated with the commonly used metric, APFD, and then, a modified version of the APFD metric has been proposed to overcome these weaknesses. (3). We systematically evaluate the effectiveness and efficiency of OCL‐based TCO processes on four real‐world case studies with different complexities. [ABSTRACT FROM AUTHOR]

This work presents the development and validation of a static thermochemical model for predicting process parameters in the MIDREX shaft furnace, a method used for producing direct reduced iron from lump ore and pellets. Industrial plant data is used to validate the model. Furthermore, the model is utilized to analyze the process based on different parameters. Genetic algorithm (GA) is used to estimate the critical parameters of the process (like reaction factors and extent of reactions) and validate the model with industrial data. Further investigations are conducted to assess the possibility of replacing the reformer gas (bustle gas) with hydrogen and coke oven gas (COG) to make the process greener and almost free from carbon emissions, using a systematic approach of overall heat balance, using already developed coupled thermodynamics and kinetics‐based model, and further using those data to estimate the reaction factors and extent of reactions using GA to be used in the static model. The results demonstrate the feasibility of replacing hydrogen and COG without much adverse effect on the process outcomes; however, this results in better metallization and reduced carbon footprint of the process effectively. [ABSTRACT FROM AUTHOR]

The reliability modeling and optimization of performance sharing systems (PSSs) are of vital importance due to their wide applications. Existing research mainly focuses on evaluating and maximizing the reliability of PSSs. However, in many practical systems, decision‐makers tend to prioritize the average cost of the system over its reliability. This paper studies the resource allocation optimization in common bus PSSs. In such systems, each unit has binary‐state random performance to satisfy the multi‐state random demand. The surplus performance can be shared via a common bus with transmission loss between the common bus and the unit. The performance allocation, performance transmission, and unsupplied demand incur costs. Resource allocation strategies are determined by optimization models considering different objective functions and constraints. Additionally, transmission loss between the common bus and the unit is considered. A genetic algorithm is employed to efficiently find the optimal allocation strategies. Numerical examples prove the effectiveness of the proposed models in improving system reliability and reducing system costs. [ABSTRACT FROM AUTHOR]

At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm. [ABSTRACT FROM AUTHOR]

The intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well‐planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability‐based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality. [ABSTRACT FROM AUTHOR]

Customized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach. [ABSTRACT FROM AUTHOR]

In this study, a combination of Plackett–Burman and Box–Behnken designs is applied to discover the relationships between the components of rubber compounds and technical specifications. Optimization of rubber compound formulation is realized by support vector regression integrated genetic algorithm to minimize compound cost. Twelve components potentially affecting the technical specifications of rubber compound, which are natural rubber, carbon black, white filler, stearic acid, zinc oxide, antiozonant, antioxidant, process oil, curing retarder, curing agent, and accelerator, are screened through Plackett–Burman design to decide the significant variables. Afterwards, four significant parameters, including carbon black, process oil, curing agent, and accelerator are analyzed using Box–Behnken design to minimize the number of experiments while obtaining the correlation between formulation and specifications. Lastly, a support vector regression integrated genetic algorithm is implemented to predict optimum compound formulation at minimum cost. Highlights: Optimization of rubber compound to reduce the mixture and curing cost.Combination of Plackett–Burman and Box–Behnken designs.Integration of support vector regression to genetic algorithm.Correlations between the amounts of components and technical specifications. [ABSTRACT FROM AUTHOR]

Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches.We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we 'evolve' a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives.We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users.Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes. [ABSTRACT FROM AUTHOR]

Summary: Software‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions. [ABSTRACT FROM AUTHOR]

ABSTRACT The sliding mode control (SMC) strategy based on genetic algorithm is proposed for discrete two‐dimensional (2‐D) Markov switching systems with the Roesser model. By means of common 2‐D sliding surface, the 2‐D SMC law is designed for the underlying system. According to Lyapunov stability criteria, sufficient conditions are established for the asymptotic mean‐square stability of the underlying 2‐D Markov switching systems and the reachability of the sliding region is guaranteed. Utilizing an iteration optimizing algorithm, an effective SMC strategy under genetic algorithm is constructed to minimize the sliding region by searching an ideal sliding gain. Finally, the applicability of the proposed 2‐D SMC strategy is verified through an example. [ABSTRACT FROM AUTHOR]

The neural network technology combining genetic algorithm is utilized to predict the sulfur content and optimize the desulfurization operation at the end of the refining process. Three types of prediction models are developed to achieve the optimal model. The prediction accuracy can be improved by the application of the deep neural network while the root means square error (RMSE) value of the optimal prediction model and the mean absolute error (MAE) value are less than 5 ppm. Moreover, the proportion of heats with prediction errors less than 5 ppm reaches 82%. Effects of dissolved oxygen contents, initial sulfur contents, carbon contents, and the amount of desulfurizer addition on the desulfurization process are considered. The optimal amount of slag addition with various initial sulfur contents is calculated. With the increase of initial sulfur content in the molten steel, the optimal amount of slag‐modified agent addition increases from about 500–750 kg. [ABSTRACT FROM AUTHOR]

Highlights Carbon fiber reinforced polyetherketoneketone (CF/PEKK) possess excellent mechanical properties and also meet low‐carbon environmental standards, leading to their increasingly widespread application in recent years. To ensure the strength of mechanical connections, it is crucial to minimize drilling damage. This paper utilizes Ultrasonic Vibration‐Assisted Drilling (UVAD) to enhance hole‐making quality, comparing multiple drilling performance indicators with Conventional Drilling (CD), including thrust force, drilling temperature, delamination factor, and burr factor. The results demonstrate that UVAD significantly reduces thrust force and drilling temperature, decreases delamination and burring damage, and effectively improves hole quality. To further optimize processing parameters, the Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) was used, targeting thrust force, Material removal rate (MRR), and delamination factor as optimization goals. The reliability of the model was verified through experimentations. This paper provides an effective technical approach for high‐quality drilling in CF/PEKK materials, offering significant practical application value for material processing in aerospace and other fields. The quality of CF/PEKK processed by CD and UVAD was compared. UVAD can effectively improve the quality of drilling. The drilling parameters were optimized by NSGA‐II. The reliability of the prediction model was verified by experiments. [ABSTRACT FROM AUTHOR]

In the field of simulation, it is difficult to find the relevant values for the properties of materials and in this context this approach has been proposed on optimizing the performance of organic solar cells, a promising technology in the field of renewable energy, to increase their efficiency. It adopts a hybrid approach combining the response surface methodology (RSM) with a Box–Behnken design (BBD) and the nondominated sorting genetic algorithm II (NSGA‐II). The RSM BBD method is used to identify objective functions to be optimized, considering interactions between selected parameters such as the thickness of the active layer, electron‐transport layer (ETL), hole‐transport layer (HTL), and the doping of these layers. Concurrently, the NSGA‐II genetic algorithm aims to maximize the performance of the solar cell based on these parameters. The specific importance of NSGA‐II lies in its ability to solve complex multiobjective optimization problems. Indeed, NSGA‐II is designed to simultaneously manage several performance objectives, which is crucial for organic solar cells. Its ability to generate a diverse set of optimal solutions enables efficient configurations to be found that may not be obvious with simpler optimization approaches. The results of this study show that optimum solar cell performance is achieved with active layer, ETL layer, and HTL layer thicknesses of 100.86, 79.9, and 20.24 nm, respectively, and active layer doping of 8.71E + 21 cm−3, HTL layer doping of 9.90E + 21 cm−3, and ETL layer doping of 9.49E + 21 cm−3. Analysis using Solar Cell Capacitance Simulator‐1D (SCAPS‐1D) software shows that optimum performance is achieved with these specific parameter values. After optimization with NSGA‐II, the power conversion efficiency increases by 39% compared to previous work. This study provides evidence of the effectiveness of the proposed hybrid approach for optimizing the performance of organic solar cells. By showing remarkable agreement between the results obtained by NSGA‐II and SCAPS‐1D, this approach opens up promising prospects for the future of renewable energy. [ABSTRACT FROM AUTHOR]

Summary: Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well‐studied major crops.Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions.The genetic algorithm identified pre‐ and postanthesis maximum temperature, mid‐season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity.The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well‐studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates. [ABSTRACT FROM AUTHOR]

ABSTRACT This work presents a novel wide tuning range ring voltage controlled oscillator (RVCO) designed to generate six‐phase signals. The proposed circuit is realized by coupling two identical three‐stage single‐ended inverter‐based RVCOs. The coupling network injects the common mode signals from one RVCO to the other one and vice versa in a cross‐connected manner. Three‐stage RVCOs used for coupling have been configured by modifying the conventional current‐starved RVCO. The proposed six‐phase RVCO is optimized using an improved version of non‐dominated Sorting Genetic Algorithm III (NSGAIII). In this algorithm, the sigma scaling method is used to control selection pressure and enhance the algorithm's performance. The proposed RVCO oscillates at 3.46 GHz. It can operate from 970 MHz to 4 GHz with a supply voltage of 1.5 V, demonstrating a wide frequency tuning range of 120%. Additionally, another six‐phase oscillator is proposed, achieved by capacitively coupling two identical three‐stage RVCOs. Both proposed oscillators are designed in TSMC 0.18 μm CMOS technology. [ABSTRACT FROM AUTHOR]

This study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)‐dedicated lane implementation at large‐scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi‐level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV‐dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human‐driven vehicles to a fully AV environment. [ABSTRACT FROM AUTHOR]

Site investigation provides essential geotechnical parameter information for analysis and design. However, three conflicting objectives, namely exploration effort, robustness and parameter uncertainty, pose a challenge to the development of an optimal site investigation program. In this study, a three objective optimization framework for the site investigation program is proposed based on the Bayesian approach and the non‐dominated sorting genetic algorithm (NSGA‐II). The only inputs required by the proposed framework are prior distribution of geotechnical parameters and error information. The prior distribution of geotechnical parameters is derived from integrating engineering experience and measurements from basic exploration boreholes. The error information is obtained based on literature and expert judgment related to the specific project. Firstly, a design pool of candidate investigation programs is generated using Bayesian approach to determine the locations and number of exploration boreholes. The NSGA‐II is then applied to identify the optimal program that balances lower cost, higher robustness, and lower uncertainty. The proposed multiobjective optimization framework is illustrated and validated through a real site investigation case in Chongqing, China, aimed at determining the ultimate bearing capacity of the rock foundation. The spatial correlation of parameters within the study area is also considered. The optimal program is represented by the location and number of exploration boreholes. By comparing measurements with predictions from different site investigation programs, the efficiency of the proposed multiobjective framework is demonstrated. Additionally, the influence of engineering experience and random field modeling on the investigation program is discussed. [ABSTRACT FROM AUTHOR]

In genomic selection (GS), the prediction accuracy is heavily influenced by the composition of the training set (TS). Currently, two primary strategies for building TS are used: one involves accumulating historical phenotypic records from multiple years, while the other is the “test‐and‐shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self‐pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long‐term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, test and shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations where the exchange of germplasm will occur at a predefined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term. [ABSTRACT FROM AUTHOR]

In terms of the interplay between manufacturing cost and quality during the molding of fiber reinforced thermosetting composites, this study proposes a multi‐scale collaborative optimization strategy aimed at shortening molding time and reducing the diminishing defects generated during the process. Macro‐scale thermochemical and micro‐scale thermomechanical calculations are conducted on the composite molding to analyze the origins of temperature gradients and residual stresses. Then, the curing cycle, temperature gradient in the macro‐scale model, and residual stresses in the micro‐scale model are optimized by combining finite element analysis with the Non‐dominated Sorting Genetic Algorithm II. Several curing processes within the Pareto front were selected, and their impact on compressive performance was assessed through experimental analysis. Compared to the standard curing process (SCP), the optimal curing process exhibited a 41.4% reduction in maximum temperature gradient (∆Tmax), a 25.2% decrease in effective residual stress σ¯, and a 20.0% increase in compressive strength. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Highlights: Investigated of interactions among components in composites molding.Optimization of composite molding process with Non‐dominated Sorting Genetic Algorithm II and finite element.Examined the impact of composite molding process on compressive performance. [ABSTRACT FROM AUTHOR]

This paper aims to study the effect of eliminating some elements from the original planar array (OPA) to generate a useful flat‐top coverage pattern (FTCP) using a genetic algorithm. The study involves dividing the fully planar array (i.e., OPA) into two regions with an unequal number of elements. The first region includes the elements located in the core (center) of the array, and the remaining elements are the second region, and their location is within the boundary (side) of the array. Only the elements of the first region are included in the optimization process, while the elements of the second region are eliminated in the form of a specific geometric composition without affecting the FTCP generation. Comparing the OPA that N×M elements with the first region (active subarray) elements, it is noted that the number of elements has been reduced from N×M to (N − 2x) × (M − 2y) to reduce the complexity of the system as much as possible without affecting the performance in general. Different boundary subarray shapes are presented in this paper with different numbers of elements. The obtained results showed the effectiveness of the proposed method in generating FTCP close to its counterpart in the OPA. [ABSTRACT FROM AUTHOR]

In the production of hybrid composites made of thermoplast and metal sheets, certain limitations in the linear forming process can be overcome through a free kinematic forming process. This process involves distributing the thermoplast press mass using a tumbling or cyclic rolling movement of the forming tool. Due to this special kinematics, only a small part of the tool is ever in engagement with the workpiece, so the component is formed incrementally. Therefore, the final geometry does not have to be created in one stroke but can be carried out step by step in several partial strokes. In order to determine the optimal trajectory of the forming tool, the kinematic interplay between the workpiece, forming tool, and toolpath should be numerically captured in a mathematical model. The specific toolpath depends on the component being formed, as well as the actual geometry of the forming tool. In this research, the free kinematic forming process is modeled and the optimal toolpath is determined using a genetic algorithm. The optimal toolpath enables a uniform distribution of the thermoplast to a specified thickness and collisions between the upper and lower tool are avoided. This enables key insights into the free kinematic forming process and opens up further avenues of research for this innovative and future‐oriented manufacturing concept. [ABSTRACT FROM AUTHOR]

This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence‐dependent setup times (DRCFJS‐SDSTs). We introduce a pioneering tri‐objective mixed‐integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi‐skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence‐dependent setup time‐related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP‐hard nature of the proposed DRCFJS‐SDST, we employ the epsilon constraint method to derive exact optimal solutions for small‐scale problems. For larger instances, we develop a modified variant of the multi‐objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi‐objective particle swarm optimization (MOPSO) and non‐dominated sorted genetic algorithm (NSGA‐II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA‐II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies. [ABSTRACT FROM AUTHOR]

One of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy‐based algorithms in last studies. Greedy‐based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta‐heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA‐based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy‐based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets. [ABSTRACT FROM AUTHOR]

Highlights The engineering structures with cutouts should consider both buckling and stiffness. Inspired by the multi‐level leaf veins, in this paper, the skeleton of the grid‐honeycomb sandwich structure was designed in the hierarchical form, and combined with the cutout characteristics of the side walls of the railway vehicle body, the multi‐objective stiffness and mass optimization of the skeleton layout under buckling constraints was achieved. The cutout honeycomb sandwich structure was designed with orthogonal grids in the stiffness design area and curved grids based on the quadratic polynomial in the buckling design area. The optimization model also included the linear angle hierarchical grid, the constant angle hierarchical grid, and the single‐level orthogonal grid. The multi‐objective optimization framework based on the Radial Basis Function (RBF) surrogate model and the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) was developed. The optimization results indicated that the hierarchical grid provided the more extensive design space. The optimal solution of the quadratic polynomial hierarchical grid had the highest stiffness and stiffness per unit mass compared to the rest of the grid layouts, which were 23.28% and 30.47% higher than the single‐level orthogonal grid, respectively. Finally, the multi‐order eigenvalue buckling was analyzed based on the optimal structure. Layout of hierarchical grids proposed inspired by multi‐level leaf veins. Orthogonal grids ensure stiffness and curved grids inhibit buckling at cutouts. Apply the hierarchical grids as skeletons to honeycomb sandwich structures. Stiffness and mass optimization subject to buckling constraints is performed. [ABSTRACT FROM AUTHOR]

In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geometric width and length of the circuit components to meet all performance specifications. This difficulty makes the task of designing analog IC challenging. To address the challenge of analog IC design, this paper introduces a machine learning methodology—specifically employing the genetic algorithm (GA)—to automate the selection of circuit components' geometrical parameters, aiding the work of analog circuit designers in determining the appropriate dimensions for transistors within the charge‐pump circuit. The GA, implemented in Python and integrated with a script program written in the OCEAN language, synergistically collaborates in the GA‐OCEAN framework. The result, which ensures compliance with all specifications with remarkably low error margins, ranging as low as 0.03% and as high as 1.24%, highlights the proposed GA‐OCEAN framework's remarkable capacity to determine optimal dimensions for components inside the charge‐pump circuit. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field. [ABSTRACT FROM AUTHOR]

Hardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery. [ABSTRACT FROM AUTHOR]

The Equilibrium Optimiser (EO) has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems. Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research. To alleviate these limitations, an EO variant named adaptive elite‐guided Equilibrium Optimiser (AEEO) is introduced. Specifically, the adaptive elite‐guided search mechanism enhances the balance between exploration and exploitation. The modified mutualism phase reinforces the information interaction among particles and local optima avoidance. The cooperation of these two mechanisms boosts the overall performance of the basic EO. The AEEO is subjected to competitive experiments with state‐of‐the‐art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite. Experimental results demonstrate that AEEO outperforms several well‐performing EO variants, DE variants, PSO variants, SSA variants, and GWO variants in terms of convergence speed and accuracy. In addition, the AEEO algorithm is used for the edge server (ES) placement problem in mobile edge computing (MEC) environments. The experimental results show that the author's approach outperforms the representative approaches compared in terms of access latency and deployment cost. [ABSTRACT FROM AUTHOR]

Sustainable development requires focusing on renewable energy, such as solar power, due to the limited availability of fossil fuels. Solar energy, available only during daylight, can be stored in thermal batteries using phase change materials (PCM). However, PCM has low thermal conductivity, slowing its melting process. Fins have been added to enhance heat diffusion, but their optimal design requires further study. This research examines an annular chamber with fixed inner and outer wall temperatures and explores fin configurations. Two series of evenly spaced fins (four and five) were analyzed, with melting times calculated using CFD simulations. Larger fins reduce melting time but limit PCM storage. Effectiveness, defined as the PCM space over melting time, was used to evaluate performance. Higher boundary temperatures decreased melting time but did not always increase effectiveness. The study also used artificial neural networks (ANN) combined with a genetic algorithm to predict optimal conditions. The configuration with five fins, a 90°C boundary temperature, a fin length of 45 mm, and a thickness of 5.4 mm achieved the highest effectiveness of 4.7, showing that smaller fins at lower temperatures are more efficient. [ABSTRACT FROM AUTHOR]

For citrus trees cultivated by dwarf dense planting, the fruits are randomly distributed in space, which poses difficulties for mechanized picking. To improve picking efficiency, a citrus picking system based on dual robot collaboration is designed and a global scanning picking scheme that scans the entire fruit tree to achieve orderly fruit picking is proposed in this research. The dual‐robot calibration with the iterative method and closed‐form method is completed in the research. The picking problem is attributed to the single traveling salesman problem, and the trajectory planning and the picking task are completed with genetic algorithm in the research. The picking experiments are designed in the research. As a result, in the picking experiments, the average time for planning the picking sequence of citrus was 0.1184 s, the longest time is 0.1560 s. In each group of citrus picking experiments, the total picking time averaged 158.9 s, the longest group took 170 s, the picking success rate is 82%. The picking results show that the built dual‐machine system can effectively complete the picking task and meet the real‐time requirement of picking. The proposed dual‐robot picking system can provide a reference for the establishment of other picking robot system. [ABSTRACT FROM AUTHOR]

Purpose: We propose and evaluate multiphoton parallel transmission (MP‐pTx) to mitigate flip angle inhomogeneities in high‐field MRI. MP‐pTx is an excitation method that utilizes a single, conventional birdcage coil supplemented with low‐frequency (kHz) irradiation from a multichannel shim array and/or gradient channels. SAR analysis is simplified to that of a conventional birdcage coil, because only the radiofrequency (RF) field from the birdcage coil produces significant SAR. Methods: MP‐pTx employs an off‐resonance RF pulse from a conventional birdcage coil supplemented with oscillating z$$ z $$‐directed fields from a multichannel shim array and/or the gradient coils. We simulate the ability of MP‐pTx to create uniform nonselective brain excitations at 7 T using realistic B1+$$ {\mathrm{B}}_1^{+} $$ and ΔB0$$ \Delta {\mathrm{B}}_0 $$ field maps. The RF, shim array, and gradient waveform's amplitudes and phases are optimized using a genetic algorithm followed by sequential quadratic programming. Results: A 1 ms MP‐pTx excitation using a 32‐channel shim array with current constrained to less than 50 Amp‐turns reduced the transverse magnetization's normalized root‐mean‐squared error from 29% for a conventional birdcage excitation to 6.6% and was nearly 40% better than a 1 ms birdcage coil 5 kT‐point excitation with optimized kT‐point locations and comparable pulse power. Conclusion: The MP‐pTx method resembles conventional pTx in its goals and approach but replaces the parallel RF channels with cheaper, low‐frequency shim channels. The method mitigates high‐field flip angle inhomogeneities to a level better than 3 T CP‐mode and comparable to 7 T pTx while retaining the straightforward SAR characteristics of conventional birdcage excitations, as low‐frequency shim array fields produce negligible SAR. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ensemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet‐based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling‐Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics‐informed, seq‐to‐seq streamflow forecasting, which further reduces biases in the pre‐trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology. Plain Language Summary: Many common tasks in hydrology, such as data assimilation, parameter estimation, uncertainty quantification, require running a process‐based model over many realizations. However, the computational burden of running models, especially when distributed models are involved, can be prohibitive. Leveraging existing ensembles for similar tasks presents a desirable solution to mitigate this challenge. In this study, we propose and demonstrate an integrated deep learning approach to seamlessly connect common forward and inverse modeling tasks in ensemble‐based hydrological learning. Our methods utilize deep neural operator learning to effectively approximate functional mappings between model input and output spaces. The proposed neural operator based workflow comprises three key stages that are model‐agnostic: learning a surrogate model from an existing ensemble, using the learned surrogate model for parameter estimation, and incorporating additional meteorological forcing data to further reduce model biases. The efficacy of our deep learning framework is demonstrated through a real case study. Key Points: A neural operator approach based on DeepONet is proposed to leverage existing large hydrological ensembles for deep learningDeepONet models are used to learn mappings between parameter space and model outputs, and between forcing and streamflow observationsDemonstration over a real case study suggests that our DeepONet workflow is a promising tool for bridging common hydrological modeling tasks [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the stress evolution of high‐density polyethylene (HDPE) at loading, relaxation, and recovery stages in a multi‐relaxation‐recovery (RR) test. The analysis is based on a three‐branch spring‐dashpot model that uses the Eyring's law to govern the viscous behavior. The spring‐dashpot model comprises two viscous branches to represent the short‐ and long‐term time‐dependent stress responses to deformation, and a quasi‐static branch to represent the time‐independent stress response. A fast numerical analysis framework based on genetic algorithms was developed to determine values for the model parameters so that the difference between the simulation and the experimental data could be less than 0.08 MPa. Using this approach, values of the model parameters were determined as functions of deformation and time so that the model can simulate the stress response at loading, relaxation, and recovery stages of the RR test. The simulation also generated 10 sets of model parameter values to examine their consistency. The study concludes that the three‐branch model can serve as a suitable tool for analyzing the mechanical properties of HDPE, and values for the model parameters can potentially be used to characterize the difference among PEs for their mechanical performance. Highlights: Developed computer programs to determine parameter values automatically.Explained the unusual stress drop during stress recovery after unloading.Evaluated the statistical range of the parameter values for the good fitting. [ABSTRACT FROM AUTHOR]

Spherical particles stand out as high‐value products with superior macroscopic properties and enhanced downstream processing efficiency. In this study, an integrated digital design strategy, combining artificial neural networks (ANN) and genetic algorithms (GA) has been employed to optimize the spherical agglomeration (SA) process. Initially, a dataset of benzoic acid SA processes was created, which was subsequently employed for training and testing the ANN model. An environmental impact sustainability index (STI) was constructed to assess the environmental effects associated with each operational variable in the SA process. To attain multi‐objective optimization, a GA was employed in combination with the ANN model. In addition, a Score function was formulated to generate Pareto fronts, tailored to meet the specific needs of real scenarios, considering variations in the assigned weights. Furthermore, the model was adapted for aspirin SA process, enhancing predictive abilities with only 20% of original data on operating conditions. [ABSTRACT FROM AUTHOR]

In the last decade, with the development of the electric vehicle industry and their acceptance in human societies, the participation plan of electric vehicles in supplying the load of the network has been taken into consideration. One of the requirements of this plan is the optimal location of the stations for these vehicles in the network so that they play an effective role in the operation of the network. In this regard, along with the construction of charging and discharging stations for electric vehicles, the construction of renewable sources in the network can play a complementary role for these stations. In this paper, the effect of using renewable resources as a supplement for smart charging stations and the placement of these stations to achieve technical and economic goals have been investigated. In order to manage the demand on the side of consumers and even out the load curve, the time of use mechanism as one of the demand response programs has been considered in this study. In this research, the improved nondominant sorting genetic algorithm is proposed to solve the problem, and the results of the proposed method are also compared with the conventional genetic and particle swarm optimization algorithms. All the simulations have been done in the MATLAB software and on the IEEE 33‐bus network. Based on the obtained results, after the implementation of the proposed plan in the distribution network, the objective functions of the loss, voltage drop, and the total cost have been reduced by 13.6%, 58.7%, and 54.4%, respectively, compared to the base conditions of the network. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed‐species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non‐additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom‐up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top‐down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment. [ABSTRACT FROM AUTHOR]

In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators. [ABSTRACT FROM AUTHOR]

Background: In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. Purpose: Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. Methods: We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB‐BNCT. Results: The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). Conclusion: The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges. [ABSTRACT FROM AUTHOR]

The two models of microwave assisted aqueous two‐phase extraction (MATPE) of polysaccharides from Malus hupehensis are established by response surface methodology (RSM) and back propagation neural network (BP‐NN), compared their prediction ability, and comparatively optimized extraction process by using RSM and BP‐NN coupled genetic algorithm (GA), and the antioxidant activity of M. hupehensis polysaccharides (MHPs) is evaluated by the scavenging ability of free radicals. The results show that the predictive ability of BP‐NN is significantly better than that of RSM. The optimal extraction parameters to achieve the highest MHPs yield (4.71 ± 0.04)% by MATPE are obtained through BP‐NN coupled GA method under the microwave power of 337 W, mass fraction of ammonium sulfate of 23%, ethanol concentration of 29%, and liquid‐to‐solid ratio of 22 mL g−1. The IC50 values of 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) and hydroxyl radicals (OH) scavenging capacities for MHPs achieve 5.13 and 3.29 mg mL−1, respectively. The findings provide important references for the development and utilization of M. hupehensis. [ABSTRACT FROM AUTHOR]

This work presents a unique technique for optimising composite laminates used as structural components, which is critical for situations where failure might result in disastrous effects. Unlike traditional surrogate‐based optimisation approaches, this methodology combines the accurate modelling capabilities of finite element (FE) analysis with the iterative refining capacity of metaheuristic algorithms. By combining these two methodologies, our method intends to improve the design process of laminated shell structures, assuring robustness and dependability is crucial. Compared to existing benchmark solutions, the current FE shows a <1% error for cylindrical and spherical shells. The prime objective of this study is to identify the optimum ply angles for attaining a high fundamental frequency. The problem is NP‐hard because the possible ply angles span a wide range (±90°), making it difficult for optimisation algorithms to find a solution. Seven popular metaheuristic algorithms, namely the genetic algorithm (GA), the ant lion optimisation (ALO), the arithmetic optimisation algorithm (AOA), the dragonfly algorithm (DA), the grey wolf optimisation (GWO), the salp swarm optimisation (SSO), and the whale optimisation algorithm (WOA), are applied to and compared on a wide range of shell design problems. It assesses parameter sensitivity, discovering significant design elements that influence dynamic behaviour. Convergence studies demonstrate the superior performance of AOA, GWO, and WOA optimisers. Rigorous statistical comparisons assist practitioners in picking the best optimisation technique. FE‐GWO, FE‐DA, and FE‐SSA methods surpass the other techniques as well as the layerwise optimisation strategy. The findings obtained, employing the GWO, DA, and SSA optimisers, demonstrate ~3% improvement over the existing literature. With respect to conventional layup designs (cross‐ply and angle‐ply), the current optimised designs are better by at least 0.43% and as much as 48.91%. [ABSTRACT FROM AUTHOR]

Estimating the synthesizability is the prerequisite to discovering novel high‐entropy ceramics with exotic properties. Herein, combined with the high‐throughput experiments and machine learning (ML) methods, the synthesizability of high‐entropy hexaborides (HEB6) is investigated. To construct the database, 100 equimolar quinary HEB6 samples synthesized using a self‐developed high‐throughput solid‐state reaction technique and 20 potential synthesizability descriptors calculated from fundamental parameters of constituent precursors are simultaneously collected. By employing the ML and the genetic algorithms, an optimal model consisting of five synthesizability descriptors (l¯$\bar{l}$, δl${\delta }_l$, δχP${\delta }_{{\chi }^{\mathrm{P}}}$, δI1${\delta }_{{I}_1}$, and δEF${\delta }_{{E}_{\mathrm{F}}}$) is determined for predicting the synthesizability of equimolar HEB6 with a high validation accuracy (93.0%), and 7 005 new equimolar quinary HEB6 are then proposed. Moreover, the applicability of our established model on non‐equimolar HEB6 is explored by the prediction of 30 synthesizability diagrams of non‐equimolar quinary HEB6. A high accuracy of 90.9% is further validated by the synthesis experiments of 11 non‐equimolar HEB6 candidates. Our work establishes an effective ML model for assessing the synthesizability of both equimolar and non‐equimolar HEB6 and paves a promising way to accelerate the discovery of new synthetic accessible high‐entropy ceramics. [ABSTRACT FROM AUTHOR]

The quest for augmenting dropwise condensation heat transfer performance has been the driving force behind the exploration of innovative techniques and approaches to fulfill the desired objective. Mostly, earlier research on dropwise condensation was devoted to study condensation from horizontal tubes and plates but, rarely, addressed dropwise condensation from vertical tubes. An interesting topic of current research in dropwise condensation is to explore the application of Slippery Liquid Infused Porous Surfaces (SLIPSs) to improve the condensation performance. The aforesaid facts are the motivation behind the topics of interest in the present study. In this study, we explore the applicability of semisolid lubricant‐impregnated porous surfaces in enhancing dropwise condensation performance of a vertical condenser tube. The experiments conducted over a wide range of subcooling (5°C ≤ ΔT ≤ 65°C) in saturated steam environment showed significant improvement in condensation heat transfer coefficient of a vertical condenser tube impregnated with semisolid lubricants when compared to bare vertical condenser tube. The highest enhancement is found to be 280% at a subcooling of 40°C. Furthermore, these surfaces proficiently sustain dropwise condensation over a period of 72 hours without compromising heat transfer performance. The devised fabrication method is simpler, more cost‐effective, and less time‐consuming than earlier techniques used for creating SLIPSs. Additionally, an approach based on numerical optimization by a stochastic global optimization technique, namely, Genetic Algorithm, is proposed to retrieve the coefficients and the exponent in the mathematical expression of overall resistance, that is being used to compute the tube‐side dropwise convection heat transfer coefficient. [ABSTRACT FROM AUTHOR]

To optimize the flow field of a multistrand tundish and improve the steel quality, a prediction model integrating the techniques of computational fluid dynamics (CFD) and support vector regression (SVR) is proposed. A total of 125 cases with different structure parameters, i.e., the diameter and inclination angle of the orifices, are designed. The flow field of liquid steel in the tundish is calculated by CFD, and a sample dataset is established. Then, the SVR, combining the genetic algorithm with K‐fold, is employed to train and predict the sample dataset. In the results, it is shown that the SVR model demonstrates good learning performance on the training dataset. The average absolute errors of predictions for dead volume fraction and consistency are 0.50% and 0.71%. In addition, the SVR model takes much less computational cost than the CFD method to predict the flow performance of tundish in a given baffle structure. [ABSTRACT FROM AUTHOR]

Summary: The existing single‐phase grounding (SPG) fault section location methods typically suffer from difficulty in feature selection, limited feeder terminal units (FTUs) configuration, and excessive dependence on communication, which weaken their generalization and robustness. To overcome these challenges, an SPG fault section location approach based on feature subset optimization is proposed. First, the relation between the position of FTU and its three‐phase current variation is analyzed, and its fault features are extracted to construct the candidate feature sets as feature subset optimization objects. Then, genetic algorithm and support vector machine (SVM) are combined to select the optimal feature subset with small dimensions and recognition error, which avoids the empirical errors of artificial feature selection. To reduce the cumulative errors, the SVM hyperparameters are simultaneously optimized. Finally, the SVM model is trained based on the optimal feature subset and hyperparameters. In the absence of zero‐sequence current measurement, three‐phase currents measured by FTU are locally processed to locate the fault section by the trained SVM. The experimental results verified the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]