Optimal Execution Research Articles

Iterative search for optimized configurations of micro-tunneling as a compensation method for rectifying tilted structures based on numerical simulations

Tilted structures may threat the residents as well as the surrounding buildings. Extracting soil underneath the less leaned side of the structure is considered as the most efficient technique to rectify tilted structures. In a previous research, the authors investigated a new technique to rectify tilting structures via soil extracting using micro-tunnels with the application of additional surface loads. Micro tunnel usually has a diameter smaller than 1 m that constructed through pipe jacking, which is the technique of installing pipe by pushing segments from a shaft to another to form a continuous hole in the ground media. This research study is a deterministic search to find the optimal execution parameters that provide accurate, safe, and economic rectification of tilted structures using 50 cm diameter micro tunnels. The number and position of the micro-tunnels, as well as the surface loads, are the main parameters that should be accurately designed to ensure the efficiency and safety of this technique. The theoretical analysis showed that two or three micro-tunnels, located close to the end of the less leaned foundation side, as well as the application of appropriate value of surface loads on the less leaned side of the foundation can effectively rectify tilted structures.

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

In this paper, a mathematical model and solution for performing the inventory tasks of a multi-user, mixed warehouse in which neither satellite positioning nor other IT solutions can be used was presented. After reviewing the literature on road planning and the use of drones in warehouses, a method is presented that can be used to control drones that can be moved in all directions for imaging and transmission. The proposed method consists of three main steps. As a first step, we provide the mathematical model and solution method needed to determine the (optimal execution time) access routes required for processing the compartments of the warehouses. This is an initial step before starting the inventory. This considers the structure of the warehouse, its features, the number of drones, and the parameters of the drones. In the second step, based on the routes obtained in the first step, the real-time movement of the drones was controlled during processing, including camera movement and image recording. The third step is post-processing, i.e., processing the images for QR code identification, interpreting the QR code, and recognizing empty compartments for inventory control. A major advantage for users of the solution method is that the result can be achieved automatically without an external orientation device, relying solely on its own movement and the organization of a pre-planned route. The proposed model and solution method are suitable not only for inventory control, but also for solving other problems matching the model.

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

TimeStitch: Exploiting Slack to Mitigate Decoherence in Quantum Circuits

Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [ 13 , 20 , 56 ], calibrating new gates [ 50 ], or extending a circuit’s runtime [ 32 ], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [ 35 ], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.

Consortium Blockchain-Based Computation Offloading Using Mobile Edge Platoon Cloud in Internet of Vehicles

The rapid advancement of intelligent vehicles is deemed crucial to the emergence of diverse compute-intensive applications of assisted driving, which consist of automatic driving, speed recognition, hybrid sensing data fusion, etc. Nevertheless, resources-constraint vehicles with high mobility cannot always meet the computing and communication demands when the above applications occur. Additionally, considering the expensive and inflexible deployment of edge servers, offloading application tasks to “Edge” in the vehicular networks is not always working well. To effectively mitigate the above issues, the complicated application tasks are motivated to offload to the vehicle platoon, where the vehicles travel synchronously in a string with small headway. Benefiting from the stable connectivity, adjustable mobility, and reasonable charge, the task vehicle would like to process the task by leveraging the idle resources of each platoon member (PM). To make more effective use of the resources on the mobile edge platoon cloud (MEPC), we investigate the resource allocation strategy based on the task vehicle’s service pricing strategy in this work. We first formulate the interactions between MEPC and task vehicle as a Stackelberg game to study the joint utility maximization of the MEPC and task vehicle. Then the Stackelberg Equilibrium (SE) for the proposed game is characterized and proved. The proposed algorithm Hook-Jeeves-based Stackelberg game (HJSG) can reach the SE. Finally, we introduce the consortium blockchain to ensure the security and privacy of service transactions. The entire system helps enhance task processing efficiency, protect transaction data, and improve service experience. Experimental results over numerical simulation based on practical scenarios demonstrate that compared with Multi-round Stackelberg Game (MRSG), uniform pricing, and the local computation strategy, the proposed HJSG algorithm can attain less execution time and faster convergence performance.

Tifinagh handwritten character recognition using optimized convolutional neural network

<p><span>Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.</span></p>

Optimization of Adaptive Genetic Algorithm Parameters in Traveling Salesman Problem

The TSP problem is one where a seller visits multiple destinations at the same time and they are only allowed to visit once. The purpose of this TSP is to shorten the shortest distance, thereby minimizing time and cost. There are various methods to solve the TSP problem, including greedy algorithm, brute force algorithm, hill climbing method, ant algorithm, and genetic algorithm. Each process in a genetic algorithm is affected by several parameters, including population size, maximum generation, crossover rate, and mutation rate. The purpose of this study is to apply genetic algorithms to the traveling salesman problem optimization, calculate the maximum influence of generation, chromosome number, crossover rate and mutation rate on the optimal genetic algorithm, calculate the range of chromosome number, population size, crossover rate and mutation rate for genetic algorithm in the traveling salesman problem and calculate the effect of adaptive genetic algorithm parameters on genetic algorithm results. Based on the results obtained from research and testing, the four parameters of the genetic algorithm are positively correlated with fitness results while negatively correlated with execution time performance where each adaptive parameter applied provides more optimal fitness results than static parameters. The four adaptive parameters that are applied together give optimal results, both fitness which reaches 1.0% and time reaches 38.7%.

Optimal Execution with Identity Optionality

ABSTRACT This paper investigates the impact of anonymous trading on the agents' strategy in an optimal execution framework. It mainly explores the specificity of order attribution on the Toronto Stock Exchange, where brokers can choose to either trade with their own identity or under a generic anonymous code that is common to all the brokers. We formulate a stochastic differential game for the optimal execution problem of a population of N brokers and incorporate permanent and temporary price impacts for both the identity-revealed and anonymous trading processes. We then formulate the limiting mean-field game of controls with common noise and obtain a solution in closed-form via the probablistic approach for the Almgren-Chris price impact framework. Finally, we perform a sensitivity analysis to explore the impact of the model parameters on the optimal strategy.

Strategic Execution Trajectories

ABSTRACT We obtain the optimal execution strategy for two sequential trades in the presence of a transient price impact. We first present a novel and general solution method for the case of a single trade (a metaorder) that is executed as a sequence of sub-trades (child orders). We then analyze the case of two sequential metaorders, including the case where the size and direction of the second metaorder are uncertain at the time the first metaorder is initiated. We obtain the optimal execution strategy under two different cost functions. First, we minimize the total cost when each metaorder is benchmarked to the price at its initiation, the total separate costs approach widely used by practitioners. Although simple, we show that optimizing total separate costs can lead to a significant understatement of the real costs of trading whilst also adversely impacting order scheduling. We overcome these issues by introducing a new cost function that splits the second metaorder into two parts, one that is predictable when the first metaorder is initiated and a residual that is not. The predictable and residual parts of the second metaorder are benchmarked using the initiation prices of the first and second metaorders, respectively. We prove existence of an optimal execution trajectory for linear instantaneous price impact and positive definite decay, and derive the explicit form of the minimizer in the special case of exponentially decaying impact, however uniqueness in general remains unproven. Various numerical examples are included for illustration.

Military Target Detection in Remote Sensing Imagery Based on YOLOv4-Faster

The continuing development of remote sensing has resulted in a rapidly increasing number of remote sensing applications. High-resolution remote sensing images are used in various fields in the military. We propose methods for object detection based on remote sensing images. We develop a signal processing method for normalizing remote sensing images to eliminate noise such as fog, haze, and poor lighting. This method further improves detection accuracy and reduces error rates. We develop YOLOv4-faster, an accelerated neural network model based on the YOLO (You-Only-Look-Once) object detection method. YOLOv4-faster outperforms existing networks in terms of execution time and detection performance. We conduct a series of experiments on two public datasets (TGRS-HRRSD and NWPU VHR-10) as well as a dataset containing six military target classes provided by IMINT & Analysis and collected from Google Earth. YOLOv4-faster improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.

Hierarchical Scheduling Control Method for Cascade Hydro-PV-Pumped Storage Generation System

High photovoltaic penetration in a power system has significantly challenged its safety and economic operation. To use the complementary characteristics of various renewable energy sources (RESs) fully, a novel hierarchical scheduling control (HSC) method is presented to accommodate the variability and uncertainty of a cascade hydro-PV-pumped storage (CH-PV-PS) generation system. Considering the optimization functions and execution requirements of the CH-PV-PS system, the HSC method is divided into two layers: the dynamic optimization layer and the static optimization layer. The static optimization layer focuses on the economy of the CH-PV-PS system, and the dynamic optimization layer focuses on the safety of the CH-PV-PS system. In the first layer, that is, the static optimization layer, the objectives of the day-ahead and hour-ahead schedules are established, and a heuristic algorithm is combined with a linear programming algorithm to optimize the energy allocation. Considering the uncertainty of the PV power output and hour-ahead load, a real-time schedule is established in the second layer; that is, in the second layer, the dynamic optimization layer, real-time scheduling and prediction of active output are established. Model predictive control methods are introduced to correct for prediction bias at different time scales in order to fully utilize the control capability of hydropower generation. A CH-PV-PS real-world system in Southwest China is chosen as a case study. In the three scenarios, where only PV fluctuations are considered, the simulation results reveal that, compared with the traditional open-loop optimized and hierarchical open-loop optimization methods, the HSC method reduces the average relative deviation of PV and increases the system economics. After a large amount of RESs are connected to the power grid, the HSC method provides a solution for improving the consumption of RESs.

A Practitioner Perspective on Trading and the Implementation of Investment Strategies

In this article, we discuss the fundamentals of the market structure and trading, along with how these impact the execution of investment strategies. From a practitioner perspective, we bring together this information to explore different models and processes to assist in achieving optimal execution of trading strategies. After providing a taxonomy of trading costs, we discuss how these costs, particularly market impact costs, are estimated and forecasted. Proposed models for optimal execution designed to get the best execution of a single trade, or sometimes a sequence of trades, are reviewed.

Concurrent 14. Presentation for: The transformation of Australia’s first commercial CSG field into a major gas project: how innovation and subsurface understanding has driven its success

Presented on Wednesday 18 May: Session 14 Mount Isa Mines Limited discovered coal seam gas (CSG) in the ‘Dawson Valley’ in 1991. It was the first commercial coal seam gas field in Australia, with production and sales of gas commencing circa 1994, then operated by Conoco and known as Meridian SeamGas. In 2010, Westside Corporation acquired a 51% operating interest in Meridian SeamGas from Anglo American (Anglo) and Mitsui Moura Investment Pty Ltd (MMI). Over the last 10 years, Westside has increased sales by 466% from 9 terajoules per day (TJ/d) to 42 TJ/d with gas being sold to both domestic and export markets. Further acreage acquisition to the north and south increased the asset area by 264%; now known as the Greater Meridian Fields (GMF). The steep rise in gas demand, complex market dynamics, regulatory environment, and competition from large global oil and gas operators makes it a challenging environment for smaller gas producers in Australia. Remaining agile and employing innovative approaches to optimise field development are key factors for Westside’s growth. In particular, the pad-based drilling of up dip multi-lateral wells has reduced cost and surface footprint, while enabling more efficient gas drainage. To support this field development, a more robust subsurface framework was required. Adequate knowledge of the distribution and variability of coal thickness, gas content/composition, structure, and geohazards forms the basis of the static model. It is used for resource definition and for the optimal planning and execution of multi-lateral wells, reducing drilling risks. To access the presentation click the link on the right. To read the full paper click here

The transformation of Australia’s first commercial CSG field into a major gas project: how innovation and subsurface understanding has driven its success

Mount Isa Mines Limited discovered coal seam gas (CSG) in the ‘Dawson Valley’ in 1991. It was the first commercial coal seam gas field in Australia, with production and sales of gas commencing circa 1994, then operated by Conoco and known as Meridian SeamGas. In 2010, Westside Corporation acquired a 51% operating interest in Meridian SeamGas from Anglo American (Anglo) and Mitsui Moura Investment Pty Ltd (MMI). Over the last 10 years, Westside has increased sales by 466% from 9 terajoules per day (TJ/d) to 42 TJ/d with gas being sold to both domestic and export markets. Further acreage acquisition to the north and south increased the asset area by 264%; now known as the Greater Meridian Fields (GMF). The steep rise in gas demand, complex market dynamics, regulatory environment, and competition from large global oil and gas operators makes it a challenging environment for smaller gas producers in Australia. Remaining agile and employing innovative approaches to optimise field development are key factors for Westside’s growth. In particular, the pad-based drilling of up dip multi-lateral wells has reduced cost and surface footprint, while enabling more efficient gas drainage. To support this field development, a more robust subsurface framework was required. Adequate knowledge of the distribution and variability of coal thickness, gas content/composition, structure, and geohazards forms the basis of the static model. It is used for resource definition and for the optimal planning and execution of multi-lateral wells, reducing drilling risks.

Optimal Execution: A Review

This review article is intended to collect and summarize many of the results in the field of optimal execution over the last twenty years. In doing so, we describe the general workings of the limit order book so that the sources of costs and risks which need to be optimized are understood. The initial models considered propose simple dynamics for prices which allow easily computable strategies which maximize risk-adjusted profits. Subsequently, the review is divided into two major parts. The first explores several works which investigate how optimal liquidation strategies are modified to account for more complex dynamics, namely other stochastic or non-linear factors. The second presents optimal trading strategies when the agent utilizes benchmarks in addition to risk-adjusted wealth, or when she has objectives beyond optimal liquidation.

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.

DETERMINING THE OVICIDAL EFFICACY OF DISINFECTANTS BY USING A MULTIFUNCTIONAL MICROCHAMBER

In the prevention of parasitic diseases, an important place is occupied by the search for new chemical preparations for disinvasion. The selection criterion is often the ovicidal efficacy of drugs against helminth eggs. The study of ovicidal efficacy is a rather laborious process that requires certain skills. In publications devoted to this topic, very little space is given to methodological issues. The traditional methods of testing and selection of drugs for disinfestation, proposed by the guidelines, have not changed much over the years. This article describes a new improved method for determining the ovicidal effectiveness of disinfectants using a multifunctional microchamber. An experimental comparison of the new method with the traditional one was made. For comparison, the most time-consuming stage of the experiment was chosen - the release of eggs from the action of the disinfectant. The advantages of the proposed method are shown. It is simpler, and to perform the same work requires 3 times less actions than the traditional one. The release of eggs from the action of the disinfectant using a multifunctional microchamber occurs 2,5 times faster than using a centrifuge. The execution time of operations is less dependent on the speed of the operator. And the use of a microchamber in setting up experiments reduces the loss of eggs in the process of their release from the action of the disinfectant by almost 30%.

Hybrid Approach for Botnet Detection Using K-Means and K-Medoids with Hopfield Neural Network

In the last few years, a number of attacks and malicious activities have been attributed to common channels between users. A botnet is considered as an important carrier of malicious and undesirable briskness. In this paper, we propose a support vector machine to classify botnet activities according to k-means, k-medoids, and neural network clusters. The proposed approach is based on the features of transfer control protocol packets. System performance and accuracy are evaluated using a predefined data set. Results show the ability of the proposed approach to detect botnet activities with high accuracy and performance in a short execution time. The proposed system provides 95.7% accuracy rate with a false positive rate less than or equal to 3%.

ОПТИМИЗАЦИЯ КОНСТРУКТИВНЫХ ПАРАМЕТРОВ РОБОТИЗИРОВАННЫХ ПЛАТФОРМ ПОДВИЖНОСТИ ДЛЯ ОБУЧЕНИЯ ОПЕРАТОРОВ МАШИН НА ТРЕНАЖЕРЕ И РЕАЛИЗАЦИИ ТРЕБУЕМЫХ ТРАЕКТОРИЙ

The article discusses and analyzes various options for the design of robotic mobility platforms (RMP), which can be used as part of complete test benches and simulators for training operators of various equipment. Various trajectories of movement of the mobile platform of the RMP are considered. Investigations have been carried out based on a parameterized simulation model (MSC Adams) to determine the optimal location of the attachment points of the joints in the upper movable platform. Minimization of the force parameters in the reactions of the corresponding supports was chosen as an optimization criterion in this case. All the accepted trajectories were worked out during the investigation. Various options of the relative position of the joints of the base and the upper movable platform of the RMP have been investigated. Similarly, for the selected trajectories, an investigation and analysis of the force parameters arising in the joints at different main diameters of the location of the base and upper movable platform joints were carried out. As an initial main version, an option was adopted with the arrangement of the joint supports taken as the optimal one. Modeling and search for the most optimal execution options are carried out based on the developed digital simulation model of the RMP in the MSC Adams system. The results of mathematical and simulation modeling are presented.

Prediction of of heart diseases utilising support vector machine and artificial neural network

The heart, <span lang="EN-US">like a pump, is an organ about the size of a fist, mainly composed of muscle and connective tissue that functions to distribute blood to tissues. The heart is located under the rib cage, above the diaphragm between the lungs, slightly closer to the left. Sometimes a small, unexpected problem with the veins or the valves that supply the heart affects a person's life and can lead to death. Early diagnosis is essential to predict diseases that affect the human heart and lead people to live another period of life. In this context, the authors introduce two methods for early diagnosis of heart disease, the support vector machine and artificial neural network. The medical data is taken from the University of California Irvine (UCI) Machine Learning Repository database, and it contains reports of 170 people. The investigation results confirm that the optimal execution is the support vector machine technique. It gives high-accuracy prediction results. As for the performance of the forward propagation artificial neural networks (ANN) technique is acceptable.</span>