Global Optimization Model Research Articles

Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine

In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear dynamic element in Wiener model are designed to tune engine operating conditions. Reduced-dimension matrix inversion incorporated with the fast leave one out cross validation strategy is utilized to decrease computational time for the selection of engine model feature parameters. An optimization algorithm is no longer needed compared to the former method. The contribution of this study is that a more convenient and appropriate methodology is developed to describe aircraft engine thermodynamic behavior during its static and dynamic operations. The methodology is evaluated in terms of computational efforts, dynamic and static estimation accuracy through a case study involving data that are generated by general aircraft engine simulation. The results confirm our viewpoints in this paper.

Teaching–learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling

Teaching–learning-based optimization (TLBO) algorithm is one of the recently proposed optimization algorithms. It has been successfully used for solving optimization problems in continuous spaces. To improve the optimization performance of the TLBO algorithm, a modified TLBO algorithm with differential and repulsion learning (DRLTLBO) is presented in this paper. In the proposed algorithm, the differential evolution (DE) operators are introduced into the teacher phase of DRLTLBO to increase the diversity of the new population. In the learner phase of DRLLBO, local learning method or repulsion learning method are adopted according to a certain probability to make learners search knowledge from different directions. In the local learning method, learners learn knowledge not only from the best learner but also from another random learner of their neighbors. In the repulsion learning method, learners learn knowledge from the best learner and keep away from the worst learner of their neighbors. Moreover, self-learning method is adopted to improve the exploitation ability of learners when they are not changed in some continuous generations. To decrease the blindness of random self-learning method, the history information of the corresponding learners in some continuous generations is used in self-learning phase. Furthermore, all learners are regrouped after a certain iterations to improve the local diversity of the learners. In the end, DRLTLBO is tested on 32 benchmark functions with different characteristics and two typical nonlinear modeling problems, and the comparison results show that the proposed DRLTLBO algorithm has shown interesting outcomes in some aspects.

Globally Consistent Wrinkle-Aware Shading of Line Drawings.

Shading is a tedious process for artists involved in 2D cartoon and manga production given the volume of contents that the artists have to prepare regularly over tight schedule. While we can automate shading production with the presence of geometry, it is impractical for artists to model the geometry for every single drawing. In this work, we aim to automate shading generation by analyzing the local shapes, connections, and spatial arrangement of wrinkle strokes in a clean line drawing. By this, artists can focus more on the design rather than the tedious manual editing work, and experiment with different shading effects under different conditions. To achieve this, we have made three key technical contributions. First, we model five perceptual cues by exploring relevant psychological principles to estimate the local depth profile around strokes. Second, we formulate stroke interpretation as a global optimization model that simultaneously balances different interpretations suggested by the perceptual cues and minimizes the interpretation discrepancy. Lastly, we develop a wrinkle-aware inflation method to generate a height field for the surface to support the shading region computation. In particular, we enable the generation of two commonly-used shading styles: 3D-like soft shading and manga-style flat shading.

A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing

We propose a neural probabilistic structured-prediction method for transition-based natural language processing, which integrates beam search and contrastive learning. The method uses a global optimization model, which can leverage arbitrary features over non-local context. Beam search is used for efficient heuristic decoding, and contrastive learning is performed for adjusting the model according to search errors. When evaluated on both chunking and dependency parsing tasks, the proposed method achieves significant accuracy improvements over the locally normalized greedy baseline on the two tasks, respectively.

Energy Security prospects in Cyprus and Israel: A focus on Natural Gas

The global production of natural gas has increased from 1226 bcm in 1973 to 3282 bcm in 2010 and is projected to continue rising by an annual growth rate of 1.6% between 2010 to 2035. Cyprus and Israel have recently made major offshore discoveries of natural gas, which can supply to a great extent the two countries’ current domestic energy needs for the next few decades and still export a substantial volume. MESSAGE, a global optimization model was used to explore the possible interactions between the two countries’ energy systems. Scenarios are presented that assess the export potential for electricity (generated by gas-fired power plants), liquefied natural gas (LNG) or gas-to-liquid products (GTL). The results are compared to a scenario without any available reserves to illustrate the financial benefits that will arise from the exploitation of the gas resources in the two countries.

A consensus-based model for global optimization and its mean-field limit

We introduce a novel first-order stochastic swarm intelligence (SI) model in the spirit of consensus formation models, namely a consensus-based optimization (CBO) algorithm, which may be used for the global optimization of a function in multiple dimensions. The CBO algorithm allows for passage to the mean-field limit, which results in a nonstandard, nonlocal, degenerate parabolic partial differential equation (PDE). Exploiting tools from PDE analysis we provide convergence results that help to understand the asymptotic behavior of the SI model. We further present numerical investigations underlining the feasibility of our approach.

Enhanced RGB-D Mapping Method for Detailed 3D Indoor and Outdoor Modeling

RGB-D sensors (sensors with RGB camera and Depth camera) are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks including limited measurement ranges (e.g., within 3 m) and errors in depth measurement increase with distance from the sensor with respect to 3D dense mapping. In this paper, we present a novel approach to geometrically integrate the depth scene and RGB scene to enlarge the measurement distance of RGB-D sensors and enrich the details of model generated from depth images. First, precise calibration for RGB-D Sensors is introduced. In addition to the calibration of internal and external parameters for both, IR camera and RGB camera, the relative pose between RGB camera and IR camera is also calibrated. Second, to ensure poses accuracy of RGB images, a refined false features matches rejection method is introduced by combining the depth information and initial camera poses between frames of the RGB-D sensor. Then, a global optimization model is used to improve the accuracy of the camera pose, decreasing the inconsistencies between the depth frames in advance. In order to eliminate the geometric inconsistencies between RGB scene and depth scene, the scale ambiguity problem encountered during the pose estimation with RGB image sequences can be resolved by integrating the depth and visual information and a robust rigid-transformation recovery method is developed to register RGB scene to depth scene. The benefit of the proposed joint optimization method is firstly evaluated with the publicly available benchmark datasets collected with Kinect. Then, the proposed method is examined by tests with two sets of datasets collected in both outside and inside environments. The experimental results demonstrate the feasibility and robustness of the proposed method.

Root system growth biomimicry for global optimization models and emergent behaviors

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with new methods for global optimization. Accordingly a novel bioinspired optimizer, namely the root system growth algorithm (RSGA), is proposed, which adopts the root foraging, memory and communication and auxin-regulated mechanism of the root system. Then RSGA is benchmarked against several state-of-the-art reference algorithms on a suit of CEC2014 functions. Experimental results show that RSGA can obtain satisfactory performances on several benchmarks in terms of accuracy, robustness and convergence speed. Moreover, a comprehensive simulation is conducted to investigate the explicit adaptability of root system in RSGA. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system are social and cooperative that is analogous to animal foraging behaviors.

QoS-aware service composition with user preferences and multiple constraints

With the growing application of services on the basis of physical networks and Internet of Things devices, service composition based on quality of service (QoS) has attracted wide attention. However, QoS-aware service composition with multiple QoS constraints is time consuming work. Given the evident user preference for QoS, we propose a service composition framework and a QoS-aware method incorporating local and global optimization models. Candidate services are preprocessed and assigned different priority levels based on local optimization. The priority weight of quality attributes is obtained by a fuzzy analytic hierarchy process and the approximate optimal composite service is derived by global optimization for multiple QoS constraints. Simulations verify the proposed method outperforms traditional integer programming (IP) and genetic algorithm, particularly for large scale datasets, providing near optimal solutions with significantly less time cost.

Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system

Cloud manufacturing offers the potential to make mass manufacturing resources and capabilities more widely integrated and accessible to users through network. Most related research assumes that there exists only one management center for all manufacturing resources and capabilities in a manufacturing cloud. However, this could cause the efficiency problem (e.g. scheduling time) and harm the quality of service (e.g. response time). Actually, a large-scale manufacturing cloud should have multiple management centers to deal with massive, widely distributed manufacturing resources and capabilities and users; meanwhile, the constraint of finite manufacturing resources and capabilities and the cost of remote collaboration should be taken into consideration. Thus, this article first presents the architecture for the multi-centric management with two-level scheduling strategy combining the advantages of the centralized and decentralized decision-making. Then, after quantifying the availability and the collaborative cost of the manufacturing resources and capabilities, we propose a global optimization model for the manufacturing resources and capability allocation under the multi-centric architecture. Finally, a case study adopting our new method shows that the utilization of the manufacturing resources and capabilities would be more balanced, while the cost of the total collaboration would be reduced, compared with the typical decentralized solution. The research results can support cloud manufacturing to effectively deal with the challenge of management and allocation for increasingly large-scale and distributed manufacturing resources and capabilities.

Global optimization of open pit mining complexes with uncertainty

Global optimization for mining complexes aims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole. Aside from the large scale of the optimization models, one of the major challenges associated with optimizing mining complexes is related to the blending and non-linear geo-metallurgical interactions in the processing streams as materials are transformed from bulk material to refined products. This work proposes a new two-stage stochastic global optimization model for the production scheduling of open pit mining complexes with uncertainty. Three combinations of metaheuristics, including simulated annealing, particle swarm optimization and differential evolution, are tested to assess the performance of the solver. Experimental results for a copper-gold mining complex demonstrate that the optimizer is capable of generating designs that reduce the risk of not meeting production targets, have 6.6% higher expected net present value than the deterministic-equivalent design and 22.6% higher net present value than an industry-standard deterministic mine planning software.

Testing adaptive regime shifts for range size evolution of endemic birds of China

In this brief report, adaptive regime shifts for the range size evolution of the endemic birds of China were identified. Four models with different biological meanings were tested and compared through maximum likelihood models, including the Brownian motion model, one global optimal range size model for all lineages in the phylogeny, two optimal regime model of range sizes for lineages with large and small range sizes (OU2), and three optimal regime model in which an additional regime is added to the ancestral lineages. The results of model evaluation and comparison using the maximum likelihood technique show that over 48 endemic taxa, two optimal regimes (the OU2 model) were observed for bird lineages with large and small range sizes in the country. The possible reasons for such an observation were outlined accordingly, including the different evolutionary times, which were subjected to different historical and geological conditions, heterogeneous environmental conditions, and complex climatic fluctuations. Overall, the range size evolution of the endemic taxa was subjected to multiple selective stresses. For future implications, more studies are desired to provide a holistic view of the evolution and divergence of endemic taxa.

Application of Harmony Search Algorithm to Reservoir Operation Optimization

In this study, a meta-heuristic technique called harmony search (HS) algorithm is developed for reservoir operation optimization with respect to flood control. The HS algorithm is used to minimize the water supply deficit and flood damages downstream of a reservoir. The GIS database is used to determine the flood damage functions. The efficacy of HS algorithm is evaluated in comparison with other techniques by using a benchmark problem for a single reservoir operation optimization problem. HS showed promising results in terms of speed of convergence to an optimal objective function value compared with other techniques such as honey-bee mating optimization (HBMO) and a global optimization model (LINGO 8.0 NLP solver). The HS algorithm is then applied to the Narmab reservoir, north of Iran, as a case study. Narmab reservoir serves multiple purposes including irrigation, flood control, and drinking water requirements. The developed model is applied for monthly operation. The results show that the HS algorithm can be effectively used for operation of reservoir for flood management.

The dynamic soil organic carbon mitigation potential of European cropland

Changes in soil organic carbon stocks depend on the management regime and a variety of environmental factors including climatic conditions and soil properties. So far, the dynamics of soil organic carbon have not been explicitly represented in global economic land use optimization models. Here, we apply an approach to represent soil organic carbon dynamics explicitly in a global bottom-up recursive dynamic partial equilibrium model using carbon response functions simulated with a biophysical process-based model. We project soil organic carbon emissions from European cropland to decrease by 40% from 64MtCO2 in 2010 to about 39MtCO2 in 2050 mainly due to saturation effect when soils converge toward their equilibrium after management, crop rotation, or land use change. Moreover, we estimate a soil organic carbon mitigation potential for European cropland between 9 and 38MtCO2 per year until 2050 for carbon prices between 10 and 100 USD/tCO2. The total European mitigation potential including co-benefits from the crop and livestock sector due to the carbon price is even higher with 60MtCO2 equivalents (eq) per year. Thus carbon sequestration in soils could compensate 7% of total emissions from agriculture within the EU, 10% when including co-benefits from the crop and livestock sector. However, as production is reallocated outside Europe with increasing carbon prices, emissions decrease in Europe but increase in the rest of the world (20MtCO2 eq). Preventing GHG emission leakage to the rest of the world would decrease the European soil organic carbon mitigation potential by around 9% and the total European mitigation potential including co-benefits by 16%. Nevertheless, the net global mitigation potential would still increase. We conclude that no significant contributions to emission reduction targets should be expected from the European cropland carbon sequestration options considered in this study.

Combining sparse representation and local rank constraint for single image super resolution

Sparse representation based reconstruction methods are efficient for single image super resolution. They generally consist of the code stage and the linear combination stage. However, the simple linear combination has not considered the image edge constraint information of image, and hence the classical sparse representation based methods reconstruct the image with the unwanted edge artifacts and the unsharp edges. In this paper, considering that the local rank is able to extract better edge information than other edge operator, we propose a new single image super resolution method by combining the sparse representation and the local rank constraint information. In our method, we first learn the local rank of the HR image via the traditional sparse representation model, and then use it as the edge constraint to restrict the image edges during the linear combination stage to reconstruct the HR image. Furthermore, we propose a nonlocal and global optimization model to further improve the HR image quality. Compared with many state-of-art methods, extensive experimental results validate that the proposed method can obtain the less edge artifacts and sharper edges.

Time domain optimisation for monocular visual navigation: moving horizon approach

Estimating ego motion of monocular visual system from the input image sequence is a critical problem in computer vision. This paper considers this problem from the view of time domain optimisation. At first, based on the frame to frame matches, the traditional monocular visual navigation algorithm is introduced, and this process is also seen as a two-view method. On the basis of the three-view matches, the trifocal tensor based monocular navigation algorithm is described. In addition, this paper proposes a novel moving horizon estimation (MHE) based algorithm to tackle the pose estimation problem. In this algorithm, the epipolar constraints and the closed loop constraints of position along with consecutive time steps are all involved in the global optimisation model. In accordance with this optimisation function, the MHE is introduced to achieve the tradeoff between computation costs and estimation accuracy. Because of the inherent robustness of the referred moving horizon estimator, it is becoming more popular in various applications. Based on the general epipolar constraints, trifocal tensor constraints and step crossed optimisation function during the whole moving window, the corresponding three referred pose estimation algorithms are all implemented comparatively at the end of this paper. The rotation and translation experiments are implemented to validate the improvements of the time domain optimisation methods.

Assessment of the Effectiveness of Global Climate Policies Using Coupled Bottom-Up and Top-Down Models

In order to assess climate mitigation agreements, we propose an iterative procedure linking TIAM-WORLD, a global technology-rich optimization model, and GEMINI-E3, a global general equilibrium model. The coupling methodology combines the precise representation of energy and technology choices with a coherent representation of the macro-economic impacts, especially in terms of trade effects of climate policies on energy-intensive products. In climate mitigation scenarios, drastic technology breakthroughs are required as soon as possible, especially in large emitting countries, and in all sectors of the economy. Energy-intensive industries tend to be delocalized in regions where low-carbon production is feasible and cheap, or in regions without emission cap. However, emission leakage remains small, mainly due to global lower oil demand, and energy exporting countries are extremely penalized given lower energy exports. Emission reduction at least in the power sector and in energy-intensive industries of developing countries must be considered to reach the 2°C target.

Current Motion Tracking from Satellite Image Sequence With Global Similarity Optimization Model

A new approach is proposed for motion tracking from a satellite image sequence to address the issue of radiometric variations between two-frame images. A global similarity criterion is defined based on the cross correlation between two images to convert a convex optimization model to a nonconvex one. The retrieval of the motion field with the criterion of maximum similarity becomes solving a nonlinear minimization problem. One of the generic iterative equations is formulated based on the global similarity optimization model (GSOM) and a unified adaptive framework. The simplified iterative equation is easy to implement and highly efficient with lower computational complexity for motion estimation. The new GSOM method can adapt to the violations of the tracer conservation constraint for motion field estimation when there are radiometric variations between two images. The approach is tested using an ocean simulation data set and realistic satellite infrared image sequences. Experimental results indicate that the new approach is not only robust for radiometric variations between two images but also efficient, fast, and accurate for motion estimation.

Optimal design for an ethanol plant combining first and second-generation technologies

The synthesis and optimal design of batch plants is addressed in this study. It was applied to the technology of conventional ethanol production in a Cuban distillery using the product of enzymatic hydrolysis of pretreated bagasse as another sugared substrate, starting from laboratory results. The optimal configuration of stages, the number of units in each stage, the unit sizes and minimum total production cost are obtained from the global optimization model and the proposed superstructure. This global model is a mixed integer nonlinear programming (MINLP) formulation, which is represented and resolved by the Professional Software, General Algebraic Modeling System (GAMS) version 23.5 applying DICOPT Solver. Different scenarios are analyzed: attaching pretreatment and enzymatic hydrolysis of bagasse to a conventional distillery plant, selling ethanol, or selling the furfural as by-product if there is a guaranteed market. With this, an actual net present value (VNA) of USD 44´893 358.7 and 1.51 years of Payback Period (PP) are obtained.

A Hybrid Clustering Algorithm Based on Fuzzy c-Means and Improved Particle Swarm Optimization

Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques. However, it is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization model, which is used in many optimization problems. In this paper, a hybrid clustering algorithm based on an improved PSO and FCM is proposed, which makes use of the merits of both algorithms. Experimental results show that the proposed method has ability to escape local optima and can find more excellent optima than other nine state-of-the-art clustering methods.