Combination Of Optimization Methods Research Articles

OPTIMIZATION OF THE PARAMETERS OF SYNTHESIZED SIGNALS USING LINEAR APPROXIMATIONS BY THE NELDER-MEAD METHOD

Context. The article presents the results of a study of the effectiveness of using the Nelder-Mead method to optimize the parameters of linear approximations of synthesized signals. Algorithms have been developed and tested that integrate spectral, temporal, and statistical analyzes and provide reasonable optimization. The effectiveness of the application of the Nelder-Mead method was proven by experiment. The obtained results substantiate the improvement of the properties of the mutual correlation of signals and the reduction of the maximum deviations of the side lobes, which opens up prospects for the further application of the method in complex scenarios of signal synthesis. Objective. The purpose of the work is to evaluate the effectiveness of the application of the Nelder-Mead method when adjusting the parameters of linear approximations to optimize the mutual correlation and minimize side deviations of complex synthesized signals. Method. The main research method is the comparison of various optimization algorithms for the selection of the most effective approaches in linear approximations of synthesized signals, taking into account such criteria as accuracy, speed and minimization of deviations. Scientific works [1, 2, 4–6, 8, 9] present algorithms, including the Nelder-Mead method and differential evolution. The effectiveness of these methods is achieved due to adaptive optimization procedures that improve the characteristics of signals. It is worth noting that the methods have disadvantages associated with high requirements for computing resources, especially when processing large data. This can be minimized using combined optimization methods that take into account the interaction of signal parameters. Another important direction of improvement is the optimization of methods for adaptation to dynamic changes in the characteristics of complex signals, which allows to achieve high adaptability and reliability of real-time systems. Results. As a result of the experiment using the Nelder-Mead method, an increase in the similarity of spectral densities was achieved from 0.52 in the first iteration to 0.90 in the fourth, with a significant decrease in the distance between the peaks of the spectrum from 1.2 to 0.4, which indicates high adaptability and the accuracy of the method in adjusting the parameters of the synthesizedsignals. Conclusions. The effectiveness of the Nelder-Mead method for adjusting the specified parameters of the synthesized signals was experimentally proven, which is confirmed by a significant improvement in the similarity of the spectra with each iteration. This opens the way for additional optimizations and application of the method in various technological areas.

Bionic-response surface combination optimization method for latent heat storage performance improvement

Improving the melting and solidification rate of phase change material is crucial for enhancing the charging or discharging power of a latent heat storage system. This research proposed a novel two-tube heat exchanger latent heat storage unit with longitudinal fins of horsetail stem based on the bionic structure of horsetail stem cross-section. Numerical modeling of transient melting of phase change material in horsetail stem fins by natural convection was established. The model’s accuracy was verified and compared with horsetail stem fins and conventional straight fins. Moreover, we optimized and designed the new fin structure based on the response surface method with minimum melting time as the objective function. The optimization results show that the optimized ponytail fins have a more uniform temperature distribution of the phase change material, significantly eliminating the influence of the heat transfer hysteresis zone, and reaching the steady state more quickly under the heat storage condition. Compared to straight fins, optimized horsetail stem fins reduced the heat storage area by only 3.80% and shortened the phase change material melting time by 44.30%. The solidification time was also reduced by 54.59%. This study provides a new idea for the structural design of phase change heat storage fins.

A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration

Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization.

Multi-objective optimization of assembly free-form cable-stiffened reticulated shell considering the influence of initial states

In this study, we propose a geometric model of free-form grid structures based on the sliding translation method. Furthermore, an end-flattened inserted-plate joint could be applied to a quadrilateral free-form grid structure. The height of the control point was considered as the optimization variable, while the strain energy, geometric integrated index, and ultimate bearing capacity were considered as the optimization objectives. The results demonstrated the influence of the initial prestress and joint stiffness using multi-objective optimization. Based on the effects of the initial prestress, joint stiffness, and initial geometric imperfections, we propose a multi-objective optimization method for the assembly of a free-form cable-stiffened reticulated shell. The feasibility of the optimization method was verified using examples. Compared with the results of the shape optimization, structures having superior mechanical properties and mesh quality were obtained via the combined method for optimizing the shape and initial prestress, which verified the necessity and effectiveness of the combined optimization method. The mechanical properties of the optimized structures were improved by increasing the joint stiffness; in particular, the ultimate bearing capacity was improved. On using the multi-objective optimization method while considering the initial geometric imperfections, the properties of the structure were improved effectively, and the mechanical properties can be guaranteed even after geometric imperfections are applied.

Modeling and optimization of control processes of information-telecommunication systems

Modern information and telecommunication systems are characterized by the fact that they are large and have many heterogeneous parameters. It is important that during the development in different directions such systems are managed in an efficient way. The noted problem requires a comprehensive approach, which involves the use of an expert approach and the method of ranking. It is also required to take into account the information on the simulation experiment. Carrying out the choice of the corresponding variant of the information-telecommunication system should be realized at coordination of interests of the control center and objects of the system. To model and optimize the characteristics of the information and telecommunication systems in order to increase their effectiveness. The mechanism of changing the rating position of information-telecommunication system is shown. The basic steps of the scheme related to modeling and optimization of control facilities are presented. The illustration of changes in the volume of processed information with the use of control approaches is given. The types of interactions of the control center and information-telecommunication systems are indicated. The carried out research provides increase of efficiency of information-telecommunication systems, using a combination of optimization methods, expert and rating approaches.

Enhanced PON and AMCC Joint Transmission with GMM-Based Probability Shaping Techniques

In ITU-T standards, auxiliary management and control channels (AMCCs), as defined, facilitate the rapid deployment and efficient management of wavelength division multiplexing passive optical network (WDM-PON) systems. The super-imposition of an AMCC introduces additional interference to a PON signal, resulting in the degradation of the performance of the overall transmission. In prior research, we proposed employing a Gaussian mixture model (GMM) to fit a baseband-modulated AMCC signal. Following the analysis of the interference model and the distribution characteristics of received signal errors, we propose a combined optimization method for a transmitter and receiver in this paper. This method, grounded in probabilistic shaping (PS) techniques, optimizes the probability distribution of the transmitted signal based on the AMCC interference model, with the objective of reducing the error rate in PON signal transmission. We have validated this approach within a 50G-PON experimental system by utilizing PAM4 modulation. The experimental results demonstrate the effectiveness of this method for mitigating the impact of baseband-modulated AMCC, thereby reducing the error rate in PON signal transmission. The approach presented in this paper can further minimize the performance degradation introduced by baseband-modulated AMCC in WDM-PON systems, enhancing the efficiency of WDM-PON deployment.

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. This study aims to enhance the practicality of bearing fault diagnosis to meet real-world engineering requirements. In real industrial environments, the continuously changing operating conditions such as equipment speed and load pose challenges in collecting data for bearing fault diagnosis, as it is challenging to gather data for all operational conditions. This paper proposes a transfer learning approach for bearing fault diagnosis based on adaptive batch normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the Case Western Reserve University dataset and Northeast Forestry University (NEFU) dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.

An investigation on structural parameters optimization and coupled heat transfer of LNG submerged combustion vaporizer

Submerged combustion vaporizer (SCV) owns an extremely efficient multiphase heat exchanger adopted in the coastal liquefied natural gas (LNG) receiving terminal. Overall vaporization efficiency of SCV mostly depends on the matching relationship of structural parameters and coupled heat transfer characteristics, which has been rarely addressed in previous studies. In the present work, an integrated SCV experimental platform with handling capacity of 50 kg/h was set up to validate the reliability of numerical model and method. Additionally, on account of multi-parameter orthogonal combination optimization method, the effects and sensitivity orders of structural parameters (transverse tube pitch, longitudinal tube pitch, orifice size and number of distributor branch) on dynamics performance of SCV were investigated. Finally, the coupled heat transfer between gas–liquid two-phase flow and supercritical LNG gasification was revealed in view of optimized parameters. Results indicated that the transient numerical tube-side outlet temperature could be consistent with experimental data. For the shell-side heat transfer coefficient, the sensitivity order was transverse tube pitch > orifice size > longitudinal tube pitch > number of distributor branch. Nevertheless, for the shell-side pressure drop, the sensitivity order was longitudinal tube pitch > transverse tube pitch > orifice size > number of distributor branch. Under the condition of optimization scheme, the heat provided by gas–liquid two-phase flow could meet the requirements of supercritical LNG gasification, the NG outlet temperature might achieve approximately 272 K. The present outcomes could lay a foundation for the optimization design and deep understanding for actual SCV facility.

Combination of Optimization Methods in a Multistage Approach for a Deep Neural Network Model

Combination of Optimization Methods in a Multistage Approach for a Deep Neural Network Model

Optimal Scheduling Framework of Integrated Energy System Based on Carbon Emission in Electricity Spot Market

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

Combination optimization method of grid sections based on deep reinforcement learning with accelerated convergence speed

A modern power system integrates more and more new energy and uses a large number of power electronic equipment, which makes it face more challenges in online optimization and real-time control. Deep reinforcement learning (DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. This paper explores a DRL-based online combination optimization method of grid sections for a large complex power system. In order to improve the convergence speed of the model, it proposes to discretize the output action of the unit and simplify the action space. It also designs a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain a stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, this paper proposes to use the annealing optimization algorithm to make the granularity of the unit output finer. The proposed method in this paper has been verified on an IEEE 118-bus system. The experimental results show that it has fast convergence speed and better performance and can obtain stable solutions.

Handwritten Digit Classification and Performance Analysis of Different Machine Learning Algorithms and CNN

This work deals with the method of hand written image classification by using machine learning and deep learning. Python along with Tensor Flow framework is used here for developing the classification model. Image classification can be considered as a supervised learning approach which works on a labelled dataset for classification. Though many research works are going on this machine learning based Image classification, yet it is challenging for accurate classification. This paper focuses on handwritten image data classification task using machine learning algorithms namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest classifier (RF). In this paper a comparative analysis is performed on the dataset based on various parameters using different machine learning algorithms. Results are discussed in terms of classification accuracy of different ML based algorithms. From our experiment, it has been noticed that RF Classifier exhibits the highest accuracy of 100% which is followed by SVM having the accuracy of 98% and finally a KNN model having a correctness of 97%. The novelty of this paper is that along with various ML algorithms (RF, KNN, SVM and Naive Bayes) we applied deep learning algorithms (CNN), with three different activation functions and their combinations (RELU, TANH and SIGMOID) along with variation of optimizers (Adam and SGD). Finally performance of the combined optimization method is found to be better than the performance of the individual optimization method.

Multi-Objective Optimization of Dimensional Accuracy in Electric Hot Incremental Sheet Forming

The forming defects of parts mainly include fracture, springback, thermal expansion, and rough surface during electric hot incremental forming, in which the springback and thermal expansion directly affects the dimensional accuracy of parts. In this paper, the combined optimization of process parameters and unsupported distances was proposed to control the dimensional accuracy of forming parts in the electric hot incremental sheet forming process. The predictive model of major errors was established through the response surface methodology, and then the multi-objective optimal model was obtained using Non-dominated Sorting Genetic Algorithms-II (NSGA-II). Meanwhile, a multi-objective optimal result was determined according to the error compensation feature of the forming process. On this basis, the effect of unsupported distances on dimensional errors was analyzed in detail, and a valid unsupported distance was proposed to further improve the forming accuracy of the whole forming region. Finally, the experimental result demonstrated that the combined optimal method proposed was accurate and feasible.

Optimization model for bus priority control considering carbon emissions

ABSTRACT To study the impact of bus priority control (BPC) on traffic carbon emissions under the strategies of speed guidance, green extension (GE), and red truncation (RT), with consideration of the main influencing factors such as delay, stopping times, and speed, a combination optimization method was used to develop a bi-level optimization model for BPC. The optimal carbon-emission reductions of buses and social vehicles with different fuel types in the upstream section of the intersection and the intersection control area was the upper-level objective, and the optimal total passenger-delay reduction was the lower-level objective. The Gauss – Seidel iterative algorithm was used to solve the model. Finally, the model was applied to the analysis of calculation cases. The results indicated that after BPC was adopted under the guidance acceleration strategy, the reductions in the carbon emissions and total delay of passenger were optimal when the guidance speed was 38 km/h, i.e. 12.67% and 21.05%, respectively. Under the guidance acceleration and GE strategy, the reductions in the carbon emissions and total delay of passenger were optimal when the guidance speed was 39 km/h and the GE was 6 s, i.e. 27.49% and 38.62%, respectively. Under the guidance deceleration and RT strategy, the reductions in the carbon emissions and total delay of passenger were optimal when the guidance speed was 29 km/h and the RT was 6 s, i.e. 22.18% and 33.52%, respectively. The model reduced the carbon emissions and total delay of passenger in the upstream section of the intersection and the intersection control area to achieve the optimal overall traffic benefit for the intersection.

Design optimization of conformal cooling channels for injection molds: 3D transient heat transfer analysis

This research aims to enhance the design of an injection mold in order to minimize ejection time and increase temperature uniformity by repositioning cooling channels. For 3D transient thermal simulations, the ANSYS finite element method (FEM) program was employed, while the MATLAB program was used for optimization. It was discovered that the method could be used successfully to optimize the design of Conformal Cooling Channels (CCCs) in injection molds. The objective function value of the optimized model represents a near 40% improvement in relation to the initial model. The combined parameterization, simulation, and optimization method has proven beneficial for enhancing the arrangement of channels in an injection machine mold. The methodology is generalizable. As such, it can be employed in any industrial applications involving injection molding machines, for the improvement of the quality of the manufactured part.

Research on Combination Optimization Method of Equipment Maintenance Task Package based on Maintenance Cost Analysis

This paper proposes a maintenance task package determination method based on the analysis of generalized maintenance cost (including maintenance cost, loss cost and maintenance time), aiming at the preventive maintenance task of aircraft. In the constructed maintenance task packaging model, practical issues such as approach, workload, logical sequence of maintenance tasks and the will of operators are considered in the implementation process of preventive maintenance tasks, to clarify the priority of packaging strategy and avoid problems such as excessively large workload distribution of each task package and even the inability to be realized in the actual field due to the pursuit of low cost. Based on the actual requirements of maintenance engineering, make preventive maintenance task package to meet the requirements of operators.

An optimization method of rough surface adapted small rectangular LD laser source based on combination of Powell prism and cylindrical lenses

A combination optimization method was proposed to design a small-size rectangular laser spot and decrease the measurement error caused by the uneven intensity distribution of LD laser source. First, the intensity distribution of LD was analyzed and the combination lens structure of Powell prism and cylindrical lenses was proposed. The equations were established to calculate the profile parameters of lenses. And an illuminance loss model δΔED±R was built to determine the values of laser spot size, working reference and range. Secondly, the specific design process of our method was given, the 3D model was established and simulated in ZEMAX to further improve the imaging uniformity. Finally, the collimation, uniformity and location deviation of the spot were discussed. The average uniformity was 95.626%, and the difference of location deviation on shot peened surfaces was 0.004 μm in x direction and 0.121 μm in y direction when compared with the white ceramic surface.

DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.

A Combinational Optimization Method for Efficient Production of Indigo by the Recombinant Escherichia coli with Expression of Monooxygenase and Malate Dehydrogenase.

Indigo pigment is a widely used pigment, and the use of biosynthesis to ferment indigo has become a hot research topic. Based on previous research, the indigo could be biosynthesized via the styrene oxygenation pathway, which is regulated by intracellular redox-cofactor rebalancing. In this work, the malate dehydrogenase (mdh) gene was selected as an NADH regeneration element to improve the intracellular cofactor regeneration level, and it was co-expressed with the styrene monooxygenase (styAB) gene by pET-28a(+) vector in E. coli for enhancing indigo production. The PT7 and Pcat promoter was constructed to change the styAB gene and mdh gene from inducible expression to constitutive expression, since the expressing vector pET-28a(+) needs to be induced by IPTG. After different strategies of genetic manipulations, the styAB gene and mdh gene were successfully constitutively co-expressed by different promoters in E. coli, which obviously enhanced the monooxygenase activity and indigo production, as expected. The maximum yield of indigo in recombinant strains was up to 787.25 mg/L after 24 h of fermentation using 2.0 g/L tryptophan as substrate, which was nearly the highest indigo-producing ability using tryptophan as substrate in recent studies. In summary, this work provided a theoretical basis for the subsequent study of indigo biosynthesis and probably revealed a new insight into the construction of indigo biosynthesis cell factory for application.