Automatic Optimization Algorithm Research Articles

An Open-Source algorithm for automatic geometrical optimization of extruded liquid cold plates for enhanced thermal management in railway electronics

This paper presents the development and application of an optimization algorithm for determining the geometric parameters of an extruded Liquid Cold Plate (LCP) with internally finned channels. The entire workflow operates within a fully open-source environment, offering a comprehensive and accessible solution for optimizing LCP geometric parameters for efficient thermal management in railway power electronics as well as other industrial applications. In particular, the aim is to minimize the maximum temperature and the temperature gradient at the interface between the LCP and an electronic device for electric trains that dissipates heat. The algorithm explores a defined range of geometric parameters and automatically generates combinations and performs Computational Fluid Dynamics (CFD) simulations, using the open-source C++ toolbox OpenFOAM. Implemented in a bash script, the algorithm not only automates the simulation process but also provides a geometry of the LCP that is easy to manufacture and cost-effective. The correct value of parameters, such as the distance between the fins bottom surface and the channel base (gap), along with others, has shown a significant impact, leading to a reduction in both the maximum interface temperature (8K) and the temperature gradient (25K/m) within the system.

Automated Layout Design of Hydraulic Components With Constraints on Flow Channels

Abstract The lightweight design of hydraulic equipment has always been of vital interest. Additive manufacturing (AM) technology can meet the manufacturing requirements of heteroideus and lightweight hydraulic equipment. However, traditional layout optimization often cannot satisfy the functional constraints of hydraulic components. This article proposes a design method of function-based automatic layout optimization for hydraulic components to solve this problem. The proposed method combines multi-component layout optimization with flow-up channel path planning and uses the triangular mesh model of hydraulic components directly as layout units. The spatial pose of the layout unit is used as the gene sequence for a genetic algorithm (GA). To meet the functional constraints, this study also proposes a fast, accurate collision detection algorithm for irregular 3D models and the generating strategy for follow-up flow channels. Here, the volume of the layout units, the total centroid radius of the layout plan, the length of flow channels, and the pressure loss are taken as the objective functions, and an automatic layout optimization algorithm for hydraulic components is developed. By optimizing the initial layout plan of an aviation electro-hydrostatic actuator (EHA), the characteristic volume of the optimized layout is reduced by 30.68% and the total length of the flow channels is decreased by 39.53%, demonstrating the efficiency of this method for lightweight hydraulic equipment design.

A factor analysis based automatic optimization method of vibration sensor points for ship acoustic reconstruction

To address the challenge associated with optimizing the number and positioning of accelerometer points in ship acoustic reconstruction, factor analysis is applied to the automatic optimization of accelerometer position in this work according to the source independence of ship’s underwater radiated noise. Based on the numerical simulation method that calculates the vibration noise response under specific operation conditions, the minimum number and the optimal position of the accelerometers can be obtained through the factor analysis and multi-parameter comprehensive evaluation method. The results of acoustic reconfiguration and testing are compared using one test cabin as a validation object to verify the effectiveness of the proposed method. The results show that the automatic optimization algorithm can improve the reconstruction accuracy concerning the uniform distribution method. The error is reduced from 15.46% (1.5 dB) to 8.35% (0.8 dB), and the correlation coefficient is improved from 0.86 to 0.89. The semi-automatic optimization method is slightly lower in terms of errors and correlation coefficients compared to the automatic optimization algorithm. After increasing the number of monitoring positions for automatic optimization, the acoustic reconstruction accuracy is further improved, while the average error is reduced to 7.37% (0.7 dB) and the correlation coefficient is improved from 0.89 to 0.92. The experimental results are proximate to the conclusions of the simulation calculation. Even under the circumstance of multi-factor noise interference, the correlation coefficient of multi-position automatic optimization can still reach 0.8, in comparison with the uniform distribution method, the error is reduced from 32.49% (3.4 dB) to 25.94% (2.6 dB). The proposed method effectively captures the dynamic characteristics of complex structures and their impact on underwater radiated noise, and the integration of factor analysis can prominently enhance the computational efficiency, thereby providing technical support for engineering applications to the ship acoustic reconstruction.

Human-in-the-loop optimization for vehicle body lightweight design

Automatic optimization algorithms are crucial for vehicle body lightweight design; however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive optimization strategies partially mitigate this issue but lack a broad range of manipulation points and auxiliary information models. As such, we introduce a novel approach, “Human-in-the-Loop based method for Vehicle Body Lightweight Design” (HIL-VBLD). This method integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) an innovative interaction mode that provides multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI); (2) A comprehensive auxiliary information model that supports decision-making for designers. Our analysis demonstrates HIL-VBLD’s efficacy, showing a 54.5 % reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5 % mass reduction, mitigating local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model achieved a further 1.25 % mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9 % fewer iterations.

Optimization of acquisition patterns for establishing inter CubeSat optical communications

As commercially available CubeSats with up to six standardized units cannot achieve the precision required for an instantaneous establishment of a low-divergence optical inter-satellite link, search patterns are used to scan the remaining field of uncertainty. This analysis optimizes the simultaneously executed search pattern combinations of the two laser communication terminals involved. Based on a Monte Carlo simulation, the perturbations on these links are investigated, and the corresponding key performance parameters such as mean acquisition time and success rate are calculated. The results are penalized by the hardware specifications, including actuator and sensor bandwidths, given by their design. Residual attitude error components imply a significant influence on the acquisition process and are therefore presented within this work. The pattern pairs are fed through an automated optimization algorithm to tune and analyze them. In this particular scenario of two CubeISL models, the mean duration for a first detected acquisition hit is within a pattern period of 3.2 s for the best performing pairs spiral-rose and lissajous-rose. Assuming an uncertainty field of ±0.2deg due to limited attitude knowledge, success rates between 82.3% and 99.9% are achieved.

A New Automated Algorithm for Optimization of Measurements for Achieving the Required Accuracy of a Geodetic Network

The optimization of measurements in a geodetic network (second-order design) has been investigated in the past; however, the practical usability of the outcomes of most of such studies is doubtful. Hence, we have proposed a new automated optimization algorithm, taking into account the practical aspects of total station measurements. The algorithm consists of four parallel partial algorithms, of which one is subsequently automatically selected—the one meeting the geodetic network accuracy requirements with the lowest number of necessary measurements. We tested the algorithm (and individual partial algorithms) on four geodetic networks designed to resemble real-world networks with 50–500 modifications to each of those networks in individual tests. The results indicate that (i) the results achieved by the combined algorithm are close to the optimal results and (ii) none of the four partial algorithms universally performs the best, implying that the combination of the four partial algorithms is necessary for achieving the best possible results of geodetic network optimization.

Design of 2.5 Bit Programmable Metasurface Unit Cell for Electromagnetic Manipulation

Programmable metasurfaces are two-dimensional electromagnetic structures characterized by a low profile, conformability, and the ability to flexibly manipulate the amplitude and phase of electromagnetic waves. For high-quality beam scanning with the metasurface, it is essential that the metasurface possesses high-precision phase response quantization characteristics. This paper constructs a reflection-type metasurface unit cell featuring four P-I-N diodes and six operating states. To address the unit cell’s complexity and optimization challenges, we developed an automatic optimization algorithm, derived from the genetic optimization algorithm, for the metasurface unit cell. This algorithm was used to optimize a six-phase reflective 2.5 bit programmable metasurface cell operating at 5 GHz. The unit cell’s prototype was fabricated and measured to verify the design. Additionally, a metasurface comprising 16 × 16 unit cells was designed and simulated. The results highlight the metasurface unit cell’s excellent phase response quantization characteristics, and investigate the impact of quantization accuracy on beam scanning.

Optimized Intrusion Detection System in Fog Computing Environment Using Automatic Termination-based Whale Optimization with ELM

In fog computing, computing resources are deployed at the network edge, which can include routers, switches, gateways, and even end-user devices. Fog computing focuses on running computations and storing data directly on or near the fog devices themselves. The data processing occurs locally on the device, reducing the reliance on network connectivity and allowing for faster response times. However, the conventional intrusion detection system (IDS) failed to provide security during the data transfer between fog nodes to cloud, fog data centres. So, this work implemented the optimized IDS in fog computing environment (OIDS-FCE) using advanced naturally inspired optimization algorithms with extreme learning. Initially, the data preprocessing operation maintains the uniform characteristics in the dataset by normalizing the columns. Then, comprehensive learning particle swarm based effective seeker optimization (CLPS-ESO) algorithm extracts the intrusion specific features by analyzing the internal patterns of all rows, columns. In addition, automatic termination-based whale optimization algorithm (ATWOA) selects the best intrusion features from CLPS-ESO resultant features using correlation analysis. Finally, the hybrid extreme learning machine (HELM) classifies the varies instruction types from ATWOA optimal features. The simulation results show that the proposed OIDS-FCE achieved 98.52% accuracy, 96.38% precision, 95.50% of recall, and 95.90% of F1-score using UNSW-NB dataset, which are higher than other artificial intelligence IDS models.

An Improved Particle Swarm Optimization Algorithm for Automated Test Resource Allocation

With the rapid increase of software complexity, automated testing has become the mainstream of software testing. However, the current automated test tools adopt a large number of common test equipment resource. At the beginning of the test, for specific test scenarios, the testers need to plan the test equipment resources according to the requirements of the software under test. An automatic test equipment resource planning and optimization algorithm based on group optimization is proposed IPSO (Improved Particle Swarm Optimization) to solve this problem. This method maps the requirement description to the equipment description to obtain the matching matrix, and then establishes a multi-objective constrained optimization model for test equipment resource planning. The solution is obtained through improved particle swarm optimization algorithm to achieve the optimal planning of test equipment resources. Compared with traditional manual planning methods, this algorithm reduces the workload of testers, and improves the automation and efficiency of testing. Experimental results demonstrate that the optimal solutions found by the algorithm proposed in this paper is superior to those of the PSO (Particle Swarm Optimization)) and Linearly Decreasing Inertia Weight Particle Swarm Optimization (LWPSO) algorithms.

Influence of quality factor on operating mode of TM<sub>02</sub> mode relativistic backwave oscillator

<sec>The mode competition in an overmoded relativistic backward wave oscillator is studied through theoretical analysis and three-dimensional particle-in-cell simulation in this work. Based on the quality factor and coupling impedance, the mode selection for a TM<sub>02</sub> mode backward wave oscillator is achieved, and its output power and magnetic field strength are optimized in the simulation.</sec><sec>The quality factor is related to the group velocity and end reflection of each mode. The dispersion curves of some non-axisymmetric modes are very close, and the group velocities are basically equal. Therefore, the end reflection needs considering to distinguish between the quality factors of different modes. In frequency domain simulation, analyzing the quality factor of each mode by using the <i>S</i><sub>11</sub> parameter curve can avoid calculating the end reflection.</sec><sec>The three-dimensional simulation results show that the coupling impedance and quality factor jointly affect the operating mode. When the coupling impedance advantage of the working mode is not obvious, changing the resonant frequency of the high-frequency structure can affect the beam-wave interaction process, thereby changing the excitation mode. When the advantage is obvious, the beam-wave interaction of the excitation mode will not be destroyed by the resonant mode, and other modes of microwave output mainly come from the conversion of the same frequency modes. Due to the constant dispersion curve, the effect of resonance on the mode is essentially the effect of the quality factor on the mode dominated by the end reflection.</sec><sec>The insensitive parameters and the electron beam radius obtained from the simulation are used as the optimal parameters, and the automatic optimization algorithm is used in combination with the two-dimensional simulation to perform multi-objective optimization design in the above device. The final output power of the backward wave oscillator reaches 534 MW, with an efficiency of 23.64%, an increase of 221.7% compared with the efficiency of the original device. The device operating mode remains stable, with a power ratio of TM<sub>02</sub> mode reaching 94.95%.</sec>

Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework

Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. Few examples of such parameters include the cut values limiting the search space of the algorithm, the approximations accounting for complex phenomena or the parameters controlling algorithm performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The opensource track reconstruction software framework known as “A Common Tracking Framework (ACTS)” offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS come equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS.

Automatic Optimization Algorithm of Jewelry Design based on Machine Vision

Automatic Optimization Algorithm of Jewelry Design based on Machine Vision

Performance and Durability of Next Generation Automotive Membrane Electrode Assemblies in Automotive Short Stacks

The space for polymer electrolyte membrane fuel cell (PEMFC) systems for the application especially in small cars is a limiting factor. Hence, high power density at high current density is required to fulfill the power demand and space requirements of a fuel cell system when installed into a car. Additionally, the fuel cell operation at elevated temperature supports this requirement facilitating smaller cooling systems. Hence, within the EU funded project GAIA (next Generation AutomotIve membrane electrode Assemblies), a consortium of leading OEMs, industrial partners and academic/research partners/organizations/institutions aimed to develop well beyond state of the art membrane electrode assemblies (MEAs) for high power application at elevated operating temperature.Therefore, electrocatalysts, membranes, ionomers, gas diffusion and microporous layers were developed with respect to minimizing the resistances by improving their interfaces. The potential of the developed next generation MEAs was determined using automotive short stacks aiming a beginning of life power density of 1.8 W/cm2 at 0.6 V. The durability target was defined with less than 10 % voltage loss of the beginning of life performance after 6000 operating hours (extrapolated from a testing time of 1000 h). For verification of the durability, an automotive drive cycle was adapted from real life driving data and applied for durability determination. This drive cycle contained very challenging operating conditions such as e.g., an applied current density up to 3 A/cm2 and an increased operating temperature of 105 °C at the stack outlet.In this study, the project achievements in performance and durability at high power and high temperature operation of the developed next generation automotive MEAs were demonstrated within automotive short stacks. During the projects progression, the application of a newly developed gas diffusion layer with increased degree of graphitization and a microporous layer with large pore structure significantly increased the performance at high current density. The stability of the catalyst coated membrane (CCM) was drastically increased by a membrane with a PBI-X reinforcement developed within the project, which replaced a standard PTFE reinforcement. Additionally, a new ionomer with increased high temperature stability was implemented that inhibited ionomer migration at high temperatures. The addition of a radical scavenger to the catalyst layer inhibited the destruction of the CCM by radicals as observed at high temperature operation with the state-of-the-art material. Finally, besides the improvement of the MEA material properties, the fuel cell operating parameters were optimized in-situ using an automated optimization algorithm to find the optimum operation parameters for the newly designed MEA. This optimization demonstrated a significant contribution to the achievement of the performance and durability target as it allowed the operation of the developed MEA at its “sweet spot”.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning

IntroductionA beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans. Materials and MethodsA two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans. ResultsFor part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027). ConclusionsThe BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Analysis of possible reasons of erronious identificationof the dynamic parameters of a controlled thermal plant

For optimal adjustment of the temperature regulator, it is necessary to know the dynamic properties of the controlled plant, which are mainly obtained from an experimen, in the procedure of identifying the dynamic parameters of the controlled plant. The purpose of the study is to analyze possible causes of erronious identification of dynamic parameters of the plant. A possible mistake may be an unsuccessful selection of parameters, being identified. Their necessary and sufficient list includes static coefficient of transmission, the main and additional time constants, time delay. Possible strong nonlinearity of the actuator should be taken into account to compensate it in calculations. During the experiment, it is desirable not to allow large temperature deviations from the normal operating mode, to avoid the influence of possible small nonlinearity of the controlled plant. To process the results of the experiment, verified universal automatic optimization algorithms should be used. As a minimization criterion, it is recommended to use the logarithm of the mean square deviation between the experimental temperature oscillogram and the corresponding calculated oscillogram on the model. If, as the result of identification, equal or close values of the main time constants are obtained, this may be the result of a fault of the automatic search procedure, which is "stuck" in a local minimax. The values should be divercified, to help the automatic optimizer.

Performance of Automatic Frequency Planning and Optimization Algorithm for Cellular Networks

Abstract Frequency planning is one of the most expensive aspects of deploying a cellular network. If a set of base stations can be deployed with minimal service and planning, the cost of both deploying and maintaining the network will decrease. To ensure that the scarce frequency is utilized to its maximum, planning and optimization are done. This is also carried out to ensure that there is high efficiency in cellular radio systems and little or minimum interference due to co-channeling. This paper focuses on coming up with an automatic way of planning and optimizing the frequency in the cellular network. The approach replaces the inefficient, inaccurate and tedious manual approach. The automatic approach simplifies work for the radio frequency(RF) engineers and also reduces the cost of operation. The automatic approach ensures that the cellular network is extensively deployed in a way that criteria of maximum quality, quantity and good coverage are met. The paper focuses on coming up with an automatic planning and optimization algorithm that minimizes the intra-system interference levels to reasonable ranges within the key performance indicators (KPIs) defined for any acceptable cellular network.

A multi-objective cooperative particle swarm optimization based on hybrid dimensions for ship pipe route design

Pipe route design has an important influence on ship performance. The main objective is to generate pipe routes satisfying complicated constraints in 3D space. The design process includes two parts: mathematical modeling and automatic optimization algorithm, where facility simplifying is an essential part of the mathematical modeling. However, previous studies mainly focused on pipe layout algorithms based on single objective optimization, and facility simplifying need to rely on artificial assistance. Nowadays, the optimization algorithm which can give several feasible solutions for designers, and the implementation of automatic pipe route design receive much attention. Therefore, this paper presents a multi-objective cooperative particle swarm optimization based on hybrid dimensions (HDMCPSO) for ship pipe route design (SPRD), which adopts multi-particle dimensions strategy and cooperative update mechanism via representative of each subswarm based on multi-objective particle swarm optimization (MOPSO). The framework can solve the two separate NP-hard problems of facility simplifying and pipe layout compatibly, and obtain high-quality Pareto solutions. To fully evaluate the effectiveness of HDMCPSO, the pipe length, bend number and assembly are optimized as the objectives, and a real piping system of ship engine room is used to compare different methods reported in the literature. The results show that HDMCPSO has generality which can simplify different facilities models and obtain better engineering pipe layout solutions.

Application of Automatic Completion Algorithm of Power Professional Knowledge Graphs in View of Convolutional Neural Network

With the continuous development of electric power informatization, a large amount of electric power data information has been produced. The reasonable application of electric power database is of great significance. Building the automatic completion optimization algorithm of knowledge graphs (KGs) in power professional field provides a method to extract structured knowledge from a large number of power information and images, which has broad application value. The automatic completion algorithm of power professional KGs in view of convolutional neural network (CNN) is conducive to completing the analysis and management of power data, enabling the flexible use of data information generated by the power grid, and bringing ideas for the in-depth exploration and innovation of power grid data information application.

Оптимизация ступеней центробежных компрессоров

The article describes а hydraulic efficiency increase in the centrifugal compressor stage using automatic optimization algorithms. The optimization criterion is the polytropic efficiency of the intermediate compressor stage. The sequence of stages of the optimization process in the Fine/Design3D software module of the Numeca software package is presented. The creating process a solid model of the compression stage is shown. The performance curves of the developed stage after optimization of the shape of the rotor and stator elements are also presented.