We propose a novel antenna optimization framework leveraging an NP-to-P problem approach. Initially, the complex antenna optimization mathematical model is segmented into several simpler submodels, clarifying the rela...
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We propose a novel antenna optimization framework leveraging an NP-to-P problem approach. Initially, the complex antenna optimization mathematical model is segmented into several simpler submodels, clarifying the relationship between design variables and performance indicators. Upon training these submodels, a substantial dataset is rapidly generated. Subsequently, this dataset is used to train an inverse model, which contrasts with the original mathematical model in terms of input and output relations. This inverse approach enables the direct acquisition of high-quality antenna design solutions tailored to specific requirements, thereby bypassing the need for simulations. Consequently, this method transforms the intricate "NP" problem in antenna optimization into a more tractable "P" problem. These high-quality design solutions are then incorporated as foundational knowledge into the improved differential evolution (IDE) algorithm for further refinement. This integration allows IDE to bypass initial exploratory searches, effectively reducing the journey toward the optimal solution. The experimental results indicate that this method can save approximately 55% of the simulation runs compared to traditional methods, leading to more efficient antenna optimization.
Three-dimensional curved photovoltaic (PV) modules offer flexible integration on irregular surfaces such as building facades, vehicles, and wearable devices, but it faces severe power losses from non-uniform irradianc...
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Three-dimensional curved photovoltaic (PV) modules offer flexible integration on irregular surfaces such as building facades, vehicles, and wearable devices, but it faces severe power losses from non-uniform irradiance and current mismatch. This study develops an Improved Simulated Annealing algorithm (ISAA) for real-time electrical topology reconfiguration of a 6 x 6 curved PV module. The ISAA reconfiguration method embeds facet-level irradiance feedback into the annealing search, integrates a global-best current-mismatch index into its acceptance criterion to overcome local optima, and executes parallel, parameter-free cell-swap trials for rapid convergence. This paper compares the performance of ISAA, TCT, and SDS reconfiguration methods under various illumination distribution conditions and evaluates the adaptability under dynamic irradiance environments. Results show ISAA maintains a current mismatch index below one in all cases, smooths I-V/P-V characteristics. In addition, ISAA effectively reduces the cumulative effect of current mismatch and maintains relatively stable performance improvement under both sunny and cloudy weather conditions. ISAA reduces daily mismatch losses by up to 52 %, boosts daily energy yield by 42 % similar to 44 % compared to TCT and SDS, and enhances fill factor and operational stability under rapidly changing irradiance. Overall, ISAA handles complex irradiance, dynamic shading, and weather fluctuations, enabling optimized 3D PV performance.
Growth optimizer is a novel metaheuristic algorithm that has powerful numerical optimization capabilities. However, its parameters and search operators become crucial factors that significantly impact its optimization...
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Growth optimizer is a novel metaheuristic algorithm that has powerful numerical optimization capabilities. However, its parameters and search operators become crucial factors that significantly impact its optimization capability for engineering problems and benchmarks. Therefore, this paper proposes a quadruple parameter adaptation growth optimizer (QAGO) integrated with distribution, confrontation, and balance features. In QAGO, the quadruple parameter adaptation mechanism aims to reduce the algorithmic sensitivity for parameter setting and enhance the algorithmic adaptability. By employing parameter sampling that adheres to specific probability distributions, the parameter adaptation mechanism achieves dynamic tuning of the algorithm hyperparameters. Moreover, one-dimensional mapping and fitness difference methods are designed in the triple parameter self-adaptation mechanism based on the contradictory relationship to adjust the operator's parameters. After that, "spear" and "shield" are balanced based on the Jensen-Shannon divergence in information theory. Furthermore, the topological structure of the operators is redesigned, and by combining the parameter adaptation mechanism, operator refinement is achieved. Refined operators can effectively utilize different evolutionary information to improve the quality of the solution. The experiment evaluates the performance of QAGO on distinct optimization problems on the CEC 2017 and CEC 2022 test suites. To demonstrate the capability of QAGO in solving real-world applications, it was applied to tackle two specific problems: multilevel threshold image segmentation and wireless sensor network node deployment. The results demonstrated that QAGO delivers highly promising optimization results compared to seventy-one high-performance competing algorithms, including the five IEEE CEC competition winners. The source code of the QAGO algorithm is publicly available at https://***/tsingke/QAGO
The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, espec...
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The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.
Increased battery energy density is required to boost electric vehicle endurance;however, this also raises the possibility of thermal runaway and power battery explosion. Improving the cooling system performance requi...
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Increased battery energy density is required to boost electric vehicle endurance;however, this also raises the possibility of thermal runaway and power battery explosion. Improving the cooling system performance requires optimization and enhancement of classical systems. Traditional design approaches struggle to simultaneously enhance multiple aspects of performance, while an optimization based on Computational Fluid Dynamics (CFD) methods is often inefficient. Therefore, by integrating a flow resistance network model (FRNM) with a weighted average optimization algorithm (INFO), an efficient optimization for the comprehensive performance of the system can be achieved. Five optimized systems under different airflow rates were obtained through optimization. A comparison with two existing systems validated the effectiveness of the optimized system. The results demonstrate that, compared to the two reference systems, the optimized system decreases the maximum temperature difference by 65.51 % and 39.07 %, respectively. Furthermore, the improvement in temperature uniformity is more significant, increasing by 63.76 % and 34.40 %, respectively.
This research introduces the Sparse Sensor Placement optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on t...
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This research introduces the Sparse Sensor Placement optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10 degrees and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond.
Aiming at the lack of functional versatility in current engineering attachments, an innovative clamp-shear-grab integrated attachment (CSGI attachment), which combines clamping, shearing, and grabbing functions, is pr...
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Aiming at the lack of functional versatility in current engineering attachments, an innovative clamp-shear-grab integrated attachment (CSGI attachment), which combines clamping, shearing, and grabbing functions, is proposed based on the author's US patent. The variable frame four-bar mechanism and separate frame design ideas are adopted in mechanism design, and the attachment's motion scheme and virtual prototype are presented. In optimization, the optimization objectives of the CSGI attachment are chosen as the gripper opening size, the transmission angle of the clamp-shear mechanism, and the weight, so a multi-objective optimization model with mechanism dimension and topological structure is established. Next, an improved nondominated sorting genetic algorithm (WINSGA-II) is proposed by improving the crossover and search strategies, while its effectiveness is tested using standard indicators and data sets. Testing results show that the WINSGA-II algorithm has better convergence, population diversity, and uniformity than the nondominated sorting genetic algorithm (NSGA-II). Subsequently, the WINSGA-II algorithm is used to obtain the optimal results for the CSGI attachment. Finally, the engineering prototype is made and tested with clamping, shearing, and grabbing. The results show that the CSGI attachment can achieve the intended function. Regarding optimization, the gripper opening size of the CSGI attachment is expanded by 24.2%, and the weight is reduced by 10.1%. The HV and Spacing indicators of the WINSGA-II algorithm are 2.7% higher and 29.6% lower than those of the NSGA-II algorithm for this problem.
The increasing popularity and number of electric vehicles (EVs) globally have resulted in a growing demand for efficient, reliable, and effective electric vehicle charging station (EVCS) infrastructure. However, the d...
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The increasing popularity and number of electric vehicles (EVs) globally have resulted in a growing demand for efficient, reliable, and effective electric vehicle charging station (EVCS) infrastructure. However, the development and implementation of this infrastructure involves various challenges, including the variety of EVs, charging and battery technologies, high installation costs, limited grid capacity, and uncertainties regarding future demand, and raises issues regarding the quality, security, and stability of the power system. This paper presents a holistic understanding of the challenges, mitigation approaches, and available technologies and protocols related to EVCS network deployment. Moreover, optimization strategies such as location planning, network integration, scheduling charging time, and planning price, which involve maximizing the utilization of charging stations and minimizing associated costs, and their modeling techniques are highlighted. This review aims to provide insights to develop sustainable, efficient EVCS infrastructure while overcoming the challenges and optimizing the benefits.
Aiming at the influence of poor cooling conditions and traditional cooling control strategy on motorized spindle temperature and machining performance. Firstly, the heat transfer model of the motorized spindle cooling...
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Aiming at the influence of poor cooling conditions and traditional cooling control strategy on motorized spindle temperature and machining performance. Firstly, the heat transfer model of the motorized spindle cooling system is studied. Secondly, the influence of coolant inlet flow rate and temperature on the thermal characteristics of the motorized spindle is studied. Then, a thermal network model is established to solve the temperature of each temperature measuring point. Finally, the thermal characteristic experiment of the motorized spindle is carried out, and the cooling fluid flow optimization model is established based on the particle swarm optimization algorithm and simulated annealing algorithm. The results show that the temperature difference of the motorized spindle does not exceed 45 degrees C, the thermal deformation does not exceed 40.2 mu m, and the thermal elongation is inhibited by 36 %. The maximum error of the Thermal Network Method and Finite Element Method(FEM)is 14.24 %. The utilization of the average logarithmic temperature difference for assessing the cooling effectiveness of optimal flow rates revealed that the particle swarm optimization algorithm demonstrates a comparatively lower average logarithmic temperature difference in comparison to the simulated annealing algorithm. The heat exchange efficiency of the motorized spindle is higher under the optimal flow rate obtained by the particle swarm algorithm.
The significance of automatic text summarization (ATS) lies in its task of distilling textual information into a condensed yet meaningful structure that preserves the core message of the original content. This summary...
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The significance of automatic text summarization (ATS) lies in its task of distilling textual information into a condensed yet meaningful structure that preserves the core message of the original content. This summary generated by ATS plays a crucial role in simplifying the processing of textual information, as it captures the primary ideas of the source text while eliminating lengthy and irrelevant textual components. At present, the landscape of ATS is enriched with a multitude of innovative approaches, with a notable focus on optimization-based methods. These optimization-driven ATS techniques have introduced new perspectives, illuminating the field with their heightened accuracy in terms of metrics like ROUGE scores. Notably, their performance closely rivals other cutting-edge approaches, including various methodologies within the realm of machine learning and deep learning. The review presented in this paper delves into recent advancements in extractive ATS, centering mainly on the optimization-based approach. Through this exploration, the paper underscores the gains and trade-offs associated with adopting optimization-based ATS compared to other strategies, specifically with the application of real-time ATS. This review serves as a compass, pointing towards potential future directions that the optimization-based ATS approaches should consider traversing to enhance the field further.
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