Global Search Algorithm Research Articles

Two-stage sampling scheme for a CNN-based well placement optimization of 3D benchmark reservoirs

Well placement is crucial for optimal oilfield development. Since global search algorithms require lots of computational costs, proxy models have been developed to replace full reservoir simulation. However, many proxy models have difficulties for predicting dynamic behaviors of complex reservoirs. Some may utilize extensive amount of training data, apply different machine learning techniques, incorporate parameters for dynamic behaviors, or keep retraining proxy models as needed. For the retraining of a proxy model, its time and frequency are arbitrary.In this study, we propose an improved sampling scheme for initial training data to achieve good performances of the proxy model without the retraining process. The proposed method performs sampling over two stages. Quality information on the well placement scenarios, which indicates the potential productivity of each option, is evaluated from the samples in the first stage. After that, the second samples are obtained by reflecting this quality information as probability distribution. To validate the effectiveness of the proposed method, we perform well placement optimization for 3D benchmark reservoirs of PUNQ-S3 model and Egg model. The proposed proxy model yields optimization results with good predictive errors less than 2.1% without the retraining.

An optimization framework for the design of LMUs used in offshore platform float-over installation

Leg mating units (LMU), consisting of steels and hyperelastic materials, are utilized to protect the structures during offshore platform float-over installation. Initial LMU design usually does not satisfy required performance since it is geometrically adjusted to fit given requirements based on previous design. To meet design criteria, design optimization can be applied to the initial design. However, a direct implementation of an accurate local search algorithm may cause heavy computations with the risk of falling into a local optimum. It can be also challenging to find an accurate optimum with efficient global search algorithms due to the characteristics of LMU problems. A small perturbation in nonlinear material can have a significant impact on LMU performance. Therefore, in this research, to find an optimum LMU design efficiently and accurately, an optimization framework that combines global and local search algorithms is implemented. In the framework, an efficient meta-model of optimal prognosis based evolutionary algorithm is first applied. The optimal design obtained from the first step, premature but near the optimum, is set as a new starting point for the sequential optimization where accurate polynomial based local adaptive response surface method is utilized. The robustness of the proposed framework is shown by the development of an LMU suitable for float-over installation of a 20,000-ton topside over an eight-legged fixed offshore jacket platform.

Frequency prediction after disturbance of grid-connected wind power systems based on WOA and Attention-LSTM

Under the guidance of the goal of the “carbon peaking and carbon neutrality”, due to the high proportion of renewable energy and the high proportion of power electronic equipment, the power system will bring strong randomness, low inertia and other characteristics, causing a large number of frequency stability problems. In order to solve the problems of traditional power system frequency prediction methods, such as difficulty in modeling and poor prediction accuracy, and to determine whether the frequency stability problem will occur after the wind power grid-connected system is disturbed, the Lasso algorithm is first used to reduce the dimension of the input data, and then the attention mechanism based long and short memory (attention LSTM) neural network is used to predict the output frequency curve, The network parameters are optimized by the global search algorithm Whale Optimization Algorithm (WOA). Finally, the accuracy of the algorithm is verified by taking the improved wind power grid-connected 39 bus as an example. The results show that this method has a good guiding significance for evaluating the frequency stability of the wind power grid-connected system after interference, and can effectively predict the frequency curve of the system after interference, and evaluate the frequency stability of the system after topology change.

River hydraulic modeling with ICESat-2 land and water surface elevation

Abstract. Advances in geodetic altimetry instruments are providing more accurate measurements, thus enabling satellite missions to produce useful data for narrow rivers and streams. Altimetry missions produce spatially dense land and water surface elevation (WSE) measurements in remote areas where in situ data are scarce that can be combined with hydraulic and/or hydrodynamic models to simulate WSE and estimate discharge. In this study, we combine ICESat-2 (Ice, Cloud and land Elevation Satellite) land and water surface elevation measurements with a low-parameterized hydraulic calibration to simulate WSE and discharge without the need for surveyed cross-sectional geometry and a rainfall–runoff model. ICESat-2 provides an opportunity to map river cross-sectional geometry very accurately, with an along-track resolution of 0.7 m, using the ATL03 product. These measurements are combined with the inland water product ATL13 to calibrate a steady-state hydraulic model to retrieve unobserved hydraulic parameters such as river depth or the roughness coefficient. The low-parameterized model, together with the assumption of steady-state hydraulics, enables the application of a global search algorithm for a spatially uniform parameter calibration at a manageable computational cost. The model performance is similar to that reported for highly parameterized models, with a root mean square error (RMSE) of around 0.41 m. With the calibrated model, we can calculate the WSE time series at any chainage point at any time for an available satellite pass within the river reach and estimate discharge from WSE. The discharge estimates are validated with in situ measurements at two available gauging stations. In addition, we use the calibrated parameters in a full hydrodynamic model simulation, resulting in a RMSE of 0.59 m for the entire observation period.

A grasp planning algorithm under uneven contact point distribution scenario for space non-cooperative target capture

The contact point configuration should be carefully chosen to ensure a stable capture, especially for the non-cooperative target capture mission using multi-armed spacecraft. In this work scenario, the contact points on the base and on the arms are distributed on the opposite side of the target. Otherwise, large forces will be needed. To cope with this problem, an uneven-oriented distribution union criterion is proposed. The union criterion contains a virtual symmetrical criterion and a geometry criterion. The virtual symmetrical contact point criterion is derived from the proof of the force closure principle using computational geometry to ensure a stable grasp, and the geometry criterion is calculated by the volume of the minimum polyhedron formed by the contact points to get a wide-range distribution. To further accelerate the optimization rate and enhance the global search ability, a line array modeling method and a continuous-discrete global search algorithm are proposed. The line array modeling method reduces the workload of calculating the descent direction and the gradient available, while the continuous-discrete global search algorithm reducing the optimization dimension. Then a highly efficient grasping is achieved and the corresponding contact point is calculated. Finally, an exhaustive verification is conducted to numerically analyze the disturbance resistance ability, and simulation results demonstrate the effectiveness of the proposed algorithms.

Eikonal equation-based earthquake location with irregular surfaces

SUMMARYEarthquake location is a basic seismological problem and has a key role in many quantitative seismic analyses. The significant anomaly of traveltimes observed in mountainous areas with irregular surfaces has made earthquake location a challenge. To cope with this problem, we develop an effective eikonal equation-based earthquake location method based on unstructured mesh for 2-D/3-D isotropic and anisotropic media with irregular surfaces. First, the location misfit function is established by the reciprocity principle. Then, we use a global search algorithm to find the optimal origin times and hypocentres. Afterward, we apply eikonal equation-based master-event relocation method to relocate earthquakes on lateral boundaries of the region where the location results may be biased. To accurately compute the traveltime in 3-D vertically transversely isotropic and tilted transversely isotropic models with irregular surfaces or interfaces, we also propose a 3-D iterative fast sweeping method for eikonal equation on the unstructured tetrahedral mesh. Finally, we verify the proposed method by performing numerical experiments in 2-D/3-D irregular isotropic and anisotropic models. The numerical tests indicate that the proposed eikonal equation-based earthquake location method provides an effective way to find accurate hypocentre and origin time in 2-D/3-D isotropic and anisotropic media with irregular surfaces, even for inhomogeneous complex structures. In addition, the eikonal equation-based master-event location method has also yielded promising relocation results.

Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel

Facing the demand of efficient automatic docking for USV, this paper proposes a new kind of iterative varying-radius Dubins curve design. Due to the special mobility of dockyards, one novel trajectory optimization problem considering time optimum is modeled first. Then, we use the ant colony optimization (ACO) algorithm to solve such a complex problem. In addition to the global exhaustive search algorithm and ACO-based solution, a novel distributed local path planning algorithm named ADTA-TRD is proposed to obtain the solution of the above optimization problem restricted by the local perception of the USV. In essence, this paper draws lessons from the idea of the circular arc segment and straight line segment in the traditional Dubins curve, and consequently, the smoothness of the derived automatic docking trajectory can be guaranteed. Finally, the performance of proposed automatic docking path planning method is verified through simulation results and an actual sea trial.

Motion Error Estimation and Compensation of Airborne Array Flexible SAR Based on Multi-Channel Interferometric Phase

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR.

Data classification mining of university mental health education resources based on global search algorithm

Data classification mining of university mental health education resources based on global search algorithm

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms by using the CEC-2013 test function. The results show that PACS algorithm outperforms other algorithms in 20 of 28 test functions. Due to the superior performance of PACS algorithm, this paper uses it to solve the problem of the rectangular layout. Experimental results show that this scheme has a significant effect, and the material utilization rate is improved from 89.5% to 97.8% after optimization.

Improved parameter optimization method for measurement device independent protocol

The optimal selection of parameters in practical quantum key distribution can greatly improve the key generation rate and maximum transmission distance of the system. Owing to the high cost of global search algorithm, local search algorithm is widely used. However, there are two shortcomings in local search algorithm. One is that the solution obtained is not always the global optimal solution, and the other is that the effectiveness of the algorithm is greatly dependent on the choice of initial value. This paper uses the Monte Carlo method to prove whether the key generation rate function is convex, and also simulates and analyzes the projection of the key generation rate function on each dimension of the parameter, in contrast to the approach in previous article. In order to eliminate the effect of the initial value, this paper proposes the particle swarm local search optimization algorithm which integrates particle swarm optimization algorithm and local search algorithm. The first step is to use the particle swarm optimization to find a valid parameter which leads to nonzero key generation rate, and the second step is to adopt the parameter as the initial value of local search algorithm to derive the global optimal solution. Then, the two algorithms are used to conduct simulation and their results are compared. The results show that the key generation rate function is non-convex because it does not satisfy the definition of a convex function, however, since the key generation rate function has only one non-zero stagnation point, the LSA algorithm can still obtain the global optimal solution with an appropriate initial value. When the transmission distance is relatively long, the local search algorithm is invalid because it is difficult to obtain an effective initial value by random value method. The particle swarm optimization algorithm can overcome this shortcoming and improve the maximum transmission distance of the system at the cost of slightly increasing the complexity of the algorithm.

Flower pollination global peak search algorithm for partially shaded solar photovoltaic system

Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.

Optimizations of Multi Optima Problems Using the Genetic Algorithm, Punch-Out Method, and Least Exponent Method

The Genetic Algorithm (GA) is a very effective global search algorithm. When the dimension of the optimization problem becomes great, it is not a simple task for methods, such as gradient methods. For the accuracy of a given solution, the search frequency of the variables necessary for a variable axis is far greater with the dimension increase of the solution vector. When the smoothness of the problem is good, the small frequency of the variable will do, with the progress of the generations. However, when the problem has many sharp peaks, such as in data regression, several frequencies of the variable in each variable axis are needed to result in a tremendous total number of individuals. In order to resolve this kind of ineffectiveness for many variables with a moderate number of individuals, a punch-out method is proposed for effectively finding the optimum. The least square method is revisited here to suggest least exponent method to take care of the outlier problem in data regression.

Optimized Fuzzy Logic Control System for Diver’s Automatic Buoyancy Control Device

In this article, the design of a fuzzy logic control system (FLCS) in combination with multi-objective optimization for diver’s buoyancy control device (BCD) is presented. To either change or maintain the depth, the diver manually controls two pneumatic valves that are mounted on the inflatable diving jacket. This task can be very difficult, especially in specific diving circumstances such as poor visibility, safety stop procedures or critical life functions of the diver. The implemented BCD hardware automatically controls the diver’s depth by inflating or deflating the diver’s jacket with two electro-pneumatic valves. The FLCS in combination with the multi-objective optimization was used to minimize control error and simultaneously ensure minimal air supply consumption of the BCD. The diver’s vertical velocity is also critical, especially while the diver is ascending during the decompression procedure; therefore, a combination of depth and vertical velocity control was configured as a cascaded controller setup with outer proportional depth and inner FLCS vertical velocity control loops. The optimization of the FLCS parameters was achieved with differential evolution global optimum search algorithm. The results obtained were compared with the optimized cascaded position and velocity PID controller in simulations.

Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification.

Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding. This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction. The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method. The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time. These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.

HDL-ODPRs: A Hybrid Deep Learning Technique Based Optimal Duplication Detection for Pull-Requests in Open-Source Repositories

Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models.

Forward fitting STIX visibilities

Aims. We seek to determine to what extent the problem of forward fitting visibilities measured by the Spectrometer/Telescope Imaging X-rays (STIX) on board Solar Orbiter becomes more challenging with respect to the same problem in the case of previous hard X-ray solar imaging missions. In addition, we aim to identify an effective optimization scheme for parametric imaging for STIX. Methods. This paper introduces a global search optimization for forward-fitting STIX visibilities and compares its effectiveness with respect to the standard simplex-based optimization used so far for the analysis of visibilities measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We made this comparison by considering experimental visibilities measured by both RHESSI and STIX, as weel as synthetic visibilities generated by accounting for the STIX signal formation model. Results. We found that among the three global search algorithms for parametric imaging, particle swarm optimization (PSO) exhibits the best performances in terms of both stability and computational effectiveness. This method is as reliable as the simplex method in the case of RHESSI visibilities. However, PSO is significantly more robust when applied to STIX simulated and experimental visibilities. Conclusions. A standard optimization based on local search of minima is not effective enough for forward-fitting the few visibilities sampled by STIX in the spatial frequency plane. Therefore, more sophisticated optimization schemes based on global search must be introduced for parametric imaging in the case of the Solar Orbiter X-ray telescope. The forward-fitting routine based on PSO proved to be significantly robust and reliable, and it could be considered as an effective candidate tool for parametric imaging in the STIX context.

Novel concept design of low energy hybrid battery thermal management system using PCM and multistage Tesla valve liquid cooling

To challenge the continuous operation performance of high energy density lithium-ion batteries at different ambient temperatures. A battery thermal management system combining Multi-stage tesla valve liquid cooling and Phase change material (MP) is proposed. After verification of the accuracy of the thermal model based on experimental measurements, numerical studies are performed at the end of 2C rate discharge. In addition, based on general full factorial design method, Kriging approximation model is used to construct complex nonlinear hybrid model between the spacing between batteries, the number of MSTV channels, coolant velocity and the performance of MP (pressure drop, standard deviation of surface temperature, and maximum temperature). At the same time, to avoid falling into local solutions, the adaptive simulated annealing (ASA) global search algorithm is integrated to optimize high-dimensionality multi-objective structural settings. The simulation results show that the designed MP-BTMS is connected with water/ethanol coolant and the inlet velocity is 0.193 m/s, the maximum temperature, standard deviation of surface temperature and pressure drop of the battery pack at 33.12 °C, 1.50 ℃ and 647.8 Pa, respectively. Finally, the optimised MP is used in continuous depth discharge conditions, as well as a range of simulation tests. The superiority of the optimum battery thermal management system in different application scenarios (liquid cooling/heating) is also verified. The cooling performance indicates that the use of MP reduces the total energy consumption required for coolant circulation by 79.9% compared to conventional BTMS. Moreover, it is found that at ambient temperature (Tamb = -10 °C), the use of MP can keep the battery module warm twice as long compared to conventional BTMS. This means that MP can significantly delay the temperature drop during cold stops of EVs, thus reducing the energy required for active battery heating after short-term parking.

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

In this study, the shape of the autonomous underwater helicopter (AUH) was optimized. AUH is a new type of disc autonomous underwater vehicle with good maneuverability and flexibility. In this paper, an optimization mathematical model is established, which takes the resistance coefficient as the optimization objective and the internal volume and the actual engineering demand as the constraints. The optimization mathematical model of AUH is solved by using two combinatorial optimization strategies. Two combinatorial optimization strategies use experimental design algorithm and multi Island genetic algorithm (MIGA) as global search algorithm, and sequential quadratic programming algorithm as local search algorithm. And based on the back propagation (BP) neural network, the sample points are trained and fitted, and the cloud map of the resistance coefficient distribution in the design space is obtained. The optimization results show that compared with the initial resistance coefficient, the first optimal solution reduces by 9.14%, and the second optimal solution reduces by 9.53%. So the resistance performance of AUH is significantly improved, which proves the feasibility of the method proposed in this paper. This provides theoretical support for the shape optimization design of AUH, which will improve the motion performance of AUH in practical engineering applications.

Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.