Sequential Optimization Algorithm Research Articles

Economic effect of an efficient and environmentally friendly extractive distillation/pervaporation process on the separation of ternary azeotropes with different compositions

Exploring the process of cost-effective and efficient technology to separate ternary aqueous azeotropic systems has become essential research in chemistry and materials science. Traditional distillation technology and promising membrane technology occupy an irreplaceable position in wastewater treatment. However, single separation technology for the separation of azeotropic systems has disadvantages. To better meet the industrial practical needs of different azeotropic compositions in each process of rapeseed oil production, the separation performance and economic cost of single extractive distillation and an extractive distillation coupled pervaporation process for ternary azeotropic systems with different feed compositions were studied. Environmentally friendly and effective solvents were selected as extractants in the extractive distillation process from two aspects: selectivity and biological toxicity. Sequential iterative optimization algorithms were used to optimize the process with the ratio of water to the total amount of cyclohexane and isopropanol ranging from 1:9 to 9:1, considering cost as the evaluation index. The results showed that the cost of the processes increases significantly with increasing water content in the feed composition. Because the flux and selectivity of the membrane module were considerably affected by temperature when the water content was less than 0.4, the integrated process was more economical; otherwise, the extractive distillation process was more economical when the water content was greater than 0.4.

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

Permanent magnet synchronous motors (PMSMs) with rectangular coils in hairpin windings exhibit improved fill factor and reduced end turn of the coils, which in turn improve the efficiency and power density of PMSMs, making them ideal for e-mobility applications. Herein, the shape of a PMSM was optimized for torque ripple reduction using metamodels to improve the noise and vibrational performance of the motor. The objective function of the optimal design aimed to minimize the torque ripple, and the average torque and efficiency were set as constraints. The notch width and depth and barrier length were selected as the design variables to satisfy the objective function and constraints. Using the optimal Latin hypercube design technique, 27 experimental points were selected, and a finite element analysis (FEA) was performed for each point. Furthermore, a function approximation was performed using six metamodels, and the best metamodel was selected using the root mean square error test. Moreover, the optimization was performed by combining the best metamodels for each variable with a sequential two-point diagonal quadratic approximation optimization algorithm. The torque ripple was improved by approximately 1.63% compared with the initial model, whereas the constraint values remained constant. Finally, an FEA was performed on the optimal point, and the FEA results matched with those of the optimal method.

Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

One of the most important topics in geotechnical engineering is seismic analysis of the earth slope. In this study, a pseudo-static limit equilibrium approach is applied for the slope stability evaluation under earthquake loading based on the Morgenstern–Price method for the general shape of the slip surface. In this approach, the minimum factor of safety corresponding to the critical failure surface should be investigated and it is a complex optimization problem. This paper proposed an effective sequential hybrid optimization algorithm based on the tunicate swarm algorithm (TSA) and pattern search (PS) for seismic slope stability analysis. The proposed method employs the global search ability of TSA and the local search ability of PS. The performance of the new CTSA-PS algorithm is investigated using a set of benchmark test functions and the results are compared with the standard TSA and some other methods from the literature. In addition, two case studies from the literature are considered to evaluate the efficiency of the proposed CTSA-PS for seismic slope stability analysis. The numerical investigations show that the new approach may provide better optimal solutions and outperform previous methods.

Nonconvex multicommodity near equilibrium models: Energy markets perspective

In this paper we explore the application of the minimum total opportunity cost (MTOC) model of Fuller and Celebi (2017) to multicommodity market planning models containing binary variables and price sensitive demands, with or without substitution among commodities. We present a greatly simplified derivation of the MTOC approximation of Fuller and Celebi (2017), here called the near equilibrium (NE) model, a mixed integer program with nonlinearities only in the objective function. For some models, the NE solution achieves the MTOC solution exactly, as in an example. We provide a simple example of capacity expansion in gas and electricity markets that are linked through substitution in demand and in the possibility of using gas to produce electricity. In several cases, we compare the NE solution to the social welfare (SW) maximization solution calculated by a sequential optimization algorithm. In one case, the sequential optimization algorithm fails to converge, due to the binary variables. For the other cases, the NE model has smaller producer opportunity costs – in particular, in most cases, smaller make whole payments that bring negative producer profits up to zero – at some sacrifice of social welfare. We suggest that the NE model could be useful to government regulators as a supplementary tool along with SW models, as the NE solution usually reduces subsidies needed for make whole payments, and sometimes benefits consumers compared to the SW solution.

Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval

Deep learning-based multi-task learning (MTL) methods have recently attracted attention for content-based image retrieval (CBIR) applications in remote sensing (RS). For a given set of tasks (e.g., scene classification, semantic segmentation, and image reconstruction), existing MTL methods employ a joint optimization algorithm on the direct aggregation of task-specific loss functions. Such an approach may provide limited CBIR performance when: 1) tasks compete or even distract each other; 2) one of the tasks dominates the whole learning procedure; or 3) characterization of each task is underperformed compared to single-task learning. This is mainly due to the lack of: 1) plasticity condition (which is associated with sensitivity to new information) or 2) stability condition (which is associated with protection from radical disruptions by new information) of the whole learning procedure. To avoid this issue, as a first time, we propose a novel plasticity-stability preserving MTL (PLASTA-MTL) approach to ensure the plasticity and the stability conditions of the whole learning procedure independently of the number and type of tasks. This is achieved by defining two novel loss functions. The first loss function is the plasticity preserving loss (PPL) function that aims to enforce the global image representation space to be sensitive to new information learned with each task. This is achieved by minimizing the difference of gradient magnitudes for the global representation and task-specific embedding spaces. The second loss function is the stability preserving loss (SPL) function that aims to protect the global representation space radically disrupted by a new task. This is achieved by minimizing the angular distances between the task gradients over global representation space. To effectively employ the proposed loss functions, we also introduce a novel sequential optimization algorithm. Experimental results show the effectiveness of the proposed approach compared to the state-of-the-art MTL methods in the context of CBIR.

A Grey Wolf Optimized 15-Level Inverter Design with Confined Switching Components

Multilevel inverters are a new class of dc-ac converters designed for high-power medium voltage and power applications as they work at high switching frequencies and in renewable applications by avoiding stresses like dv/dt and has low harmonic distortion in their output voltage. In variable speed drives and power generation systems, the use of multilevel inverters is obligatory. To estimate the switching positions in inverter configuration with low harmonic distortion value, a fast sequential optimization algorithm has been established. For harmonic reduction in multilevel inverter design, a hybrid optimization technique combining Firefly and the Genetic algorithm was used. In several real-time systems and for solving complex engineering problems, optimization approaches are gaining popularity. Based on Grey Wolf Optimization (GWO), this research paper proposes a novel multilevel inverter architecture. The GWO algorithm is based on the natural leadership hierarchy and hunting mechanism of grey wolves (Canis lupus). The Grey Wolf Optimizer (GWO) algorithm is used to find the best switching angles for a cascaded multilevel inverter in order to eliminate some high order harmonics while sustaining the desired fundamental voltage. The proposed inverter has 15 stages, and the circuit’s unique feature includes a limited switching device. This proposed method comprises, MOSFET-based switches well as three DC sources in the main circuit. The switching parameters of the inverter topology are tuned using GWO in this methodology. The THD value of the proposed system is reduced to 6.629% compared to that of Multilevel Inverter using Genetic Algorithm, standalone power supply, Firefly assisted Genetic Algorithm, hybrid APSO algorithm, DC voltage regulation and FPGA. A few simulation studies have been included in this paper to affirm the capability of the hybrid topologies with various condition parameters, dynamic changes, and modulation indexes.

Transmit–Receive Design for Airborne Radar With Nonuniform Pulse Repetition Intervals

This article aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with nonuniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signal-dependent clutter is first established. Then the 2-D range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant nonconvex problem, the sequential greedy optimization algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent framework and a Lagrangian-based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

Deployment the Spoke of a Large-Sized Transformable Refl ector Using a Sequential Optimization Algorithm

The use of large-sized opening reflectors close-packed in spacecraft is associated with spreading the spokes at a given angle, extending the fragments of the spokes and adjusting the shape of the radio-reflecting mesh. The problem of optimizing these processes with automatic output of the reflector to the deployed working state is urgent. The optimal control problem of spreading spokes of a large-sized space-based reflector with respect to bending vibrations is investigated in the article. The optimization process is complicated by ensuring convergence of iterative procedure for control finding. The bending vibrations of the spokes complicate the task of spreading. That makes it difficult to fix spokes when reaching the stops. In this paper the mathematical model of spoke dynamics is improved with respect to spoke’s bend change in length and in time, the model takes into account the presence of stop and retainer devices and an actuator. It is proposed to consider a hierarchy of two target composed functions and develop an algorithm for sequential optimization for a smooth exit to the stops. It is suggested to include the terminal condition for the angular spreading rate in the first criterion. A study was carried out using mathematical simulation for the process of turning the spoke by a given angle at small values of the angular velocity at the final moment of time taking into account bending vibrations. The exact values of the weight coefficients included in the target composed functions are found. Weight coefficients influence on transient processes is investigated. The performance of the algorithm was checked when the value of the optimization interval was changed. The comparison of the results of simulation modeling with control options using the PID controller, application of an algorithm with a predictive model and an algorithm with optimal correction of the control structure, revealed by means of the maximum principle, was carried out. The results of simulation modeling foe spokes spreading process using the sequential optimization algorithm demonstrate the achievement of the required accuracy with permissible tolerance residual vibrations. The developed algorithm of sequential optimization forms control in a real time and it is recommended to use it in more complex solutions under random disturbances using measurement process and optimization of observation intervals.

Optimization of Corrugated Bulkhead Forms By Genetic Algorithm

In this research, the optimal design of corrugated bulkheads being used in ship structures is explored by means of the state-of-theart genetic algorithms. The influences of genetic parameters, such as population sizes, crossover probability and mutation probability, on the optimal design of corrugated bulkheads are carefully examined. An objective function defined for the cost of materials and labors is to be minimized to attain the least cost of panel production. The obtained optimal values of each design variable must satisfy the predescribed constraints and some restrictions on fabrication. The optimal values of minimum weight and minimum cost of a panel production are obtained. Finally, a comparison between the method of genetic algorithm and the sequential linear programming optimization algorithm is made to demonstrate the superiority of the proposed genetic algorithms. The numerical simulation results reveal that the genetic algorithms can efficiently minimize panels’ weight and costs in production.

Photonic crystal topological design for polarized and polarization-independent band gaps by gradient-free topology optimization.

Photonic crystals can be adopted to control light propagation due to their superior band gap feature. It is well known the band gap feature of photonic crystals depends significantly on the topological design of the lattices, which is rather challenging due to the highly nonlinear objective function and multiple local minima feature of such design problems. To this end, this paper proposed a new band-gap topology optimization framework for photonic crystals considering different electromagnetic wave polarization modes. Based on the material-field series-expansion (MFSE) model and the dielectric permittivity interpolation scheme, the lattice topologies are represented by using a small number of design variables. Then, a sequential Kriging-based optimization algorithm, which shows strong global search capability and requires no sensitivity information, is employed to solve the band gap design problem as a series of sub-optimization problems with adaptive-adjusting design spaces. Numerical examples demonstrated the effectiveness of the proposed gradient-free method to maximize the band gap for transverse magnetic field (TM), transverse electric field (TE), and complete modes. Compared with previously reported designs, the present results exhibit less dependency on the guess of the initial design, larger band gaps and some interesting topology configurations.

Design and experiment of multi-mode steering system of agricultural machinery for hilly and mountainous area

In order to improve the steering flexibility of agricultural machinery in hilly and mountainous areas, a multi-mode steering system with front wheel steering, rear wheel steering, and four-wheel steering has been developed. The hydraulic steering system based on load sensitivity principle and proportion-integration-differentiation (PID) controlling algorithm was designed, which overcomes the negative impact of external load changes on flow control accuracy. The mechanical-hydraulic-controlling coupling model established in the AMESim and the sequential quadratic combinatorial optimization algorithm (SQCOA) was adopted to obtain the optimal combination of PID parameters. The simulation results demonstrate that the parameters such as pressure, speed, displacement of hydraulic cylinders, etc. in different steering modes meet the design requirements. To examine and verify the system performance, the test platform was researched and developed for conducting steering radius and displacement measurement. The experimental data illustrated that the front and rear hydraulic cylinders have good synchronization accuracy in four-wheel steering mode, and the fast switch of steering mode can be realized. The maximum error rate of is steering radius 4.21% and 3.77%, respectively, in two-wheel steering and four-wheel steering modes. The research methods and conclusions can provide a theoretical basis and reference for the other steering system development.

Multi-objective optimization of a clean, high-efficiency synthesis process of methyl-ethyl-ketone oxime from ammoximation

The ammoximation of ketones is an important chemical reaction, as oximes have a wide range of industrial applications. In this study, the process of preparing methyl ethyl ketone oxime by ammoximation of methyl ethyl ketone was studied. The modeling of the reaction unit was based on the kinetic data, and the reaction conditions were analyzed. Based on the total annual cost, an economic evaluation of the process was carried out, and a sequential iterative optimization algorithm integrating the reaction conditions was used to complete the process optimization. The minimum annual total cost was 2.41 × 107 USD/yr. The exergy of methyl ethyl ketone ammoximation to methyl ethyl ketone oxime process was analyzed, and the unit with the largest exergy loss was determined. The exergy loss rate was 65% for the minimum annual total cost. Considering exergy and annual total cost as the two optimization objectives, the process was comprehensively optimized by a multi-objective optimization algorithm. The annual total cost after multi-objective optimization was 2.55 × 107 USD/yr, which is 5.8% higher than minimum total annual cost, and the exergy loss rate was 64%, which is 1.54% lower than maximum exergy-loss rate. Compared with single-objective optimization, multi-objective optimization has a better comprehensive performance.

DEFT-FUNNEL: an open-source global optimization solver for constrained grey-box and black-box problems

The fast-growing need for grey-box and black-box optimization methods for constrained global optimization problems in fields such as medicine, chemistry, engineering and artificial intelligence, has led to the development of new efficient algorithms for finding the best possible solution. In this work, we present DEFT-FUNNEL, an open-source global optimization algorithm for general constrained grey-box and black-box problems that belongs to the class of trust-region sequential quadratic optimization algorithms. Polynomial interpolation models are used as surrogates for the black-box functions and a clustering-based multistart strategy is applied for searching for the global minima. Numerical experiments show that DEFT-FUNNEL compares favorably with state-of-the-art methods on two sets of benchmark problems: one set containing problems where every function is a black box and another set with problems where some of the functions and their derivatives are known to the solver. The code as well as the test sets used for experiments are available at the Github repository http://github.com/phrsampaio/deft-funnel.

Adaptive Maxwell's equations derived optimization and its application in antenna array synthesis

In this paper, a self-adaptive method for the Maxwell's Equations Derived Optimization (MEDO) is proposed. It is implemented by applying the Sequential Model-Based Optimization (SMBO) algorithm to the iterations of the MEDO, and achieves the automatic adjustment of the parameters. The proposed method is named as adaptive Maxwell's equations derived optimization (AMEDO). In order to evaluate the performance of AMEDO, eight benchmarks are used and the results are compared with the original MEDO method. The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters, and at the same time can keep the accuracy and stability. Moreover, the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters. In the end, the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array, and shows great potential in antenna array synthesis.

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design

The Efficient Global Optimization (EGO) algorithm has been widely used in the numerical design optimization of engineering systems. However, the need for an uncertainty estimator limits the selection of a surrogate model. In this paper, a Sequential Ensemble Optimization (SEO) algorithm based on the ensemble model is proposed. In the proposed algorithm, there is no limitation on the selection of an individual surrogate model. Specifically, the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model. Also, a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator (GUE) is proposed. The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions. The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate. Further, the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.

A new sequence optimization algorithm based on particle swarm for machine learning

With the coming of 5Gera, the algorithm efficiency and accuracy of artificial intelligence and machine learning are facing more strict requirements and greater challenges. Although artificial neural network (ANN) and deep neural network (DNN) do well in classification and regression, a large amount of calculation leads to a slow response. And the speed of sequence maximum optimization (SMO) algorithm is very fast, but not accurate enough. In this paper, research attempts to put forward a sequential optimization algorithm based on the particle swarm optimization to balance the performance of accuracy and response speed. Compared with ANN, DNN and SMO, this approach not only has a high calculation efficiency and but also do well in the classification and regression.

Optimal Cooking Procedure Presentation System for Multiple Recipes and Investigating Its Effect

As the number of users who cook their own food increases, there is increasing demand for an optimal cooking procedure for multiple dishes, but the optimal cooking procedure varies from user to user due to the difference of each user’s cooking skill and environment. In this paper, we propose a system of presenting optimal cooking procedures that enables parallel cooking of multiple recipes. We formulate the problem of deciding optimal cooking procedures as a task scheduling problem by creating a task graph for each recipe. To reduce execution time, we propose two extensions to the preprocessing and bounding operation of PDF/IHS, a sequential optimization algorithm for the task scheduling problem, each taking into account the cooking characteristics. We confirmed that the proposed algorithm can reduce execution time by up to 44% compared to the base PDF/IHS, and increase execution time by about 900 times even when the number of required searches increases by 10,000 times. In addition, through the experiment with three recipes for 10 participants each, it was confirmed that by following the optimal cooking procedure for a certain menu, the actual cooking time was reduced by up to 13 min (14.8% of the time when users cooked freely) compared to the time when users cooked freely.

Optimization-Based Constrained Trajectory Generation for Robot-Assisted Stitching in Endonasal Surgery

The reduced workspace in endonasal endoscopic surgery (EES) hinders the execution of complex surgical tasks such as suturing. Typically, surgeons need to manipulate non-dexterous long surgical instruments with an endoscopic view that makes it difficult to estimate the distances and angles required for precise suturing motion. Recently, robot-assisted surgical systems have been used in laparoscopic surgery with promising results. Although robotic systems can provide enhanced dexterity, robot-assisted suturing is still highly challenging. In this paper, we propose a robot-assisted stitching method based on an online optimization-based trajectory generation for curved needle stitching and a constrained motion planning framework to ensure safe surgical instrument motion. The needle trajectory is generated online by using a sequential convex optimization algorithm subject to stitching kinematic constraints. The constrained motion planner is designed to reduce surrounding damages to the nasal cavity by setting a remote center of motion over the nostril. A dual concurrent inverse kinematics (IK) solver is proposed to achieve convergence of the solution and optimal time execution, in which two constrained IK methods are performed simultaneously; a task-priority based IK and a nonlinear optimization-based IK. We evaluate the performance of the proposed method in a stitching experiment with our surgical robotic system in a robot-assisted mode and an autonomous mode in comparison to the use of a conventional surgical tool. Our results demonstrate a noticeable improvement in the stitching success ratio in the robot-assisted mode and the shortest completion time for the autonomous mode. In addition, the force interaction with the tissue was highly reduced when using the robotic system.

Resource allocation method based on mobile edge computing in smart grid

Mobile edge computing(MEC) refers to offloading the collected data to the mobile edge server, which can calculate data efficiently, further improve the operating efficiency of the smart grid and reduce energy consumption. In the face of increasing data, the spectrum overhead and energy consumption of the network are becoming increasingly severe. Based on this, this paper proposes to use massive multiple input multiple output (MIMO) technology to improve the spectrum and energy efficiency of the system, and proposes a massive MIMO-MEC smart grid system framework. In addition, considering that it is easy to be eavesdropped in the process of offloading data, we propose to use physical layer security technology to ensure user information security. In order to minimize the energy consumption of applications such as smart meters, this paper proposes a sequential iterative optimization algorithm to jointly optimize the offloading rate and transmission power. The simulation results prove that as the number of antennas increases, the transmission rate of the system increases, which reduces the total energy consumption of the system.