General Optimization Framework Research Articles

Research on Unmanned Vehicle Positioning Technology Based on Multi-sensor Fusion

This paper is based on multi-sensor fusion technology of unmanned vehicle position to research and design a kind of scheme could choose to use adaptive visual data, GPS, INS, solved the problem of poor application effect of multiple scenarios on unmanned vehicles. This paper will integrate ORB_SLAM2 monocular vision and INS, compared with the traditional method to IMU and visual data separated filtering, we designed a general nonlinear optimization framework could bring visual feature points and the noise of the IMU data into it to optimize the camera position. Not only could scale and sensor bias be accurately estimated, could also improve the robustness and position precision of the unmanned vehicle. When the GPS signal is better, the algorithm combines the GPS data with the kalman filtering algorithm, which could be used to estimate the position of the GPS data more accurately, and can also assist the construction of visual map. In addition, it also shows good performance in data delay handling. By test in Euroc datasets and real vehicle, effect is good, in large scale outdoor scenarios also has higher position accuracy and robustness, the error is less than 0.1 meters, and has a strong practical value.

Routing Cost Minimization and Throughput Maximization of NFV-Enabled Unicasting in Software-Defined Networks

Data transfer in contemporary networks usually is associated with strict policy enforcement for data transfer security and system performance purposes. Such a policy is represented by a service chain consisting of a sequence of network functions such as firewalls, intrusion detection systems, transcoders, etc. Due to the high cost and inflexibility of managing hardware-based network functions, network function virtualization (NFV) has emerged as a promising technology to meet the stringent requirement imposed on the service chain of each data transfer request in a low-cost and flexible way. In this paper, we study policy-aware unicast request admissions with and without end-to-end delay constraints in a software defined network. We aim to minimize the operational cost of admitting a single request in terms of both computing resource consumption for implementing the NFVs in the service chain and bandwidth resource consumption for routing its data traffic, or maximize the network throughput for a sequence of requests without the knowledge of future request arrivals. We first formulate four novel optimization problems and provide a generic optimization framework for the problems. We then develop efficient algorithms for the admission of a single NFV-enabled request with and without the end-to-end delay constraint, where NFV-enabled requests are defined as the requests with policy enforcement requirements. We also devise online algorithms with a guaranteed performance for dynamic admissions of requests without the knowledge of future arrivals. In particular, we provide the very first online algorithm with a provable competitive ratio for the problem without the end-to-end delay requirement. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising and outperform existing heuristics.

Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links

Next generation, i.e., fifth generation (5G), cellular networks will provide a significant higher capacity per area to support the ever-increasing traffic demands. In order to achieve that, many small cells need to be deployed that are connected using a combination of optical fiber links and millimeter-wave (mmWave) backhaul architecture to forward heterogeneous traffic over mesh topologies. In this paper, we present a general optimization framework for the design of policies that optimally solve the problem of where to associate a user, over which links to route its traffic towards which mesh gateway, and which base stations and backhaul links to switch off in order to minimize the energy cost for the network operator and still satisfy the user demands. We develop an optimal policy based on mixed integer linear programming (MILP) which considers different user distribution and traffic demands over multiple time periods. We develop also a fast iterative two-phase solution heuristic, which associates users and calculates backhaul routes to maximize energy savings. Our strategies optimize the backhaul network configuration at each timeslot based on the current demands and user locations. We discuss the application of our policies to backhaul management of mmWave cellular networks in light of current trend of network softwarization (Software-Defined Networking, SDN). Finally, we present extensive numerical simulations of our proposed policies, which show how the algorithms can efficiently trade-off energy consumption with required capacity, while satisfying flow demand requirements.

Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM

In this paper we propose a simultaneous localization and mapping (SLAM) back-end solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive LG-ESDSF and demonstrate that it retains all the good characteristics of the classic Euclidean ESDSF, the main advantage being the exact sparsity of the information matrix. The key advantage of LG-ESDSF in comparison with the classic ESDSF lies in the ability to respect the state space geometry by negotiating uncertainties and employing filtering equations directly on Lie groups. We also exploit the special structure of the information matrix in order to allow long-term operation while the robot is moving repeatedly through the same environment. To prove the effectiveness of the proposed SLAM solution, we conducted extensive experiments on two different publicly available datasets, namely the KITTI and EuRoC datasets, using two front-ends: one based on the stereo camera and the other on the 3D LIDAR. We compare LG-ESDSF with the general graph optimization framework ( g 2 o ) when coupled with the same front-ends. Similarly to g 2 o the proposed LG-ESDSF is front-end agnostic and the comparison demonstrates that our solution can match the accuracy of g 2 o , while maintaining faster computation times. Furthermore, the proposed back-end coupled with the stereo camera front-end forms a complete visual SLAM solution dubbed LG-SLAM. Finally, we evaluated LG-SLAM using the online KITTI protocol and at the time of writing it achieved the second best result among the stereo odometry solutions and the best result among the tested SLAM algorithms.

Delay-Aware Optimization Framework for Proportional Flow Delay Differentiation in Millimeter-Wave Backhaul Cellular Networks

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

Topology-Optimized Multilayered Metaoptics

We propose a general topology optimization framework for metasurface inverse design that can automatically discover highly complex multi-layered meta-structures with increased functionalities. In particular, we present topology-optimized multi-layered geometries exhibiting angular phase control, including a single-piece nanophotonic metalens with angular aberration correction as well as an angle-convergent metalens that focuses light onto the same focal spot regardless of the angle of incidence.

Continuous multi-task Bayesian Optimisation with correlation

This paper considers the problem of simultaneously identifying the optima for a (continuous or discrete) set of correlated tasks, where the performance of a particular input parameter on a particular task can only be estimated from (potentially noisy) samples. This has many applications, for example, identifying a stochastic algorithm’s optimal parameter settings for various tasks described by continuous feature values. We adapt the framework of Bayesian Optimisation to this problem. We propose a general multi-task optimisation framework and two myopic sampling procedures that determine task and parameter values for sampling, in order to efficiently find the best parameter setting for all tasks simultaneously. We show experimentally that our methods are much more efficient than collecting information randomly, and also more efficient than two other Bayesian multi-task optimisation algorithms from the literature.

Benchmarking Based General Algorithm Framework

This paper proposed a general framework for intelligent optimization. It was based on organizing tactics rather than probability rules, i.e., it needs no probability tuning. This is different from the existing intelligent optimization algorithms.

Achieving High Scalability Through Hybrid Switching in Software-Defined Networking

Traditional networks rely on aggregate routing and decentralized control to achieve scalability. On the contrary, software-defined networks achieve near optimal network performance and policy-based management through per-flow routing and centralized control, which, however, face scalability challenge due to: 1) limited ternary content addressable memory and on-die memory for storing the forwarding table and 2) per-flow communication/computation overhead at the controller. This paper presents a novel hybrid switching (HS) design, which integrates traditional switching and software-defined networking (SDN) switching for the purpose of achieving both scalability and optimal performance. We show that the integration also leads to unexpected benefits of making both types of switching more efficient under the hybrid design. We also design the general optimization framework via HS and propose an approximation algorithm for load-balancing optimization as a case study. Testing and numerical evaluation demonstrate the superior performance of HS when comparing with the state-of-the-art SDN design.

Intelligent Optimization Framework based on Benchmarking

A general framework for intelligent optimization was proposed in this paper, which was based on the guiding principles and specific methods of benchmarking.

Constrained Optimization for Image Reshaping With Soft Conditions

Conventional image resizing problems demand hard conditions on size and aspect ratio, which must be met with no tolerance. In this paper, a generalized optimization framework is presented, which can handle soft conditions as well as the hard ones. The soft condition can be given by an allowable range of the image parameter, which is incorporated as an inequality condition in the constrained optimization framework. Given the soft constraints, the proposed framework seeks to find the set of image parameters that minimize the cost function. A constrained optimization via a linear programming framework is employed to manage a diverse combination of soft and hard conditions for the target image. The optimization is based on the image line, which optimally selects a set of image lines (columns and rows) to be deleted for size reduction in accordance with the cost function and the constraints. As a case study, the line-based optimal image resizing method based on the linear programming framework is applied for the pre-processing of VGG-19 convolutional neural network (CNN). Although the target input size is a hard condition of $224\times 224$ for the VGG-19 CNN, the proposed optimization framework with a soft condition on the image size firstly finds an optimal near-square image with a tradeoff against the saliency level of image features. Then, the optimal near-square image is linearly scaled to the final image size to meet the hard condition.

Achievable Rate Optimization for Massive MIMO Enabled SWIPT Systems Over Downlink Rician Channels

This paper investigates the downlink transmission of massive multiple-input multiple-output-enabled simultaneous wireless information and power transfer systems in Rician fading channels. In the system, the base station is equipped with massive antennas to concurrently deliver information and energy to the users. While a portion of the harvested energy is utilized by the users to support the channel estimation, and the remaining is reserved as the supplement to the user battery for signal processing. The asymptotic expressions of the harvested energy and achievable rate are, respectively, derived, in the context of downlink scenario and line-of-sight propagation. With respect to the proposed system, two optimization problems are formulated, with the purposes of maximizing the system sum rate and the minimum rate among individual users, respectively. As both original problems are nonconvex and hard to solve, we propose a general iterative optimization framework to first decompose each of them into three subproblems, where the power allocation profiles, the channel estimation duration, and the power split ratios are optimized independently. The iteration is then carried out to update the solutions to each subproblem and finally approach the optimum of the original ones. The convergence and the effectiveness of the proposed algorithms are proved and evaluated through both theoretical analysis and simulations. Results demonstrate that the proposed algorithms can achieve the optimal transmission performance of the system with moderate complexity. Moreover, it can be further concluded that the maximum rate can be achieved if the harvested energy is fully devoted to pilot transmission.

Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.

A general optimization framework for the design and planning of energy supply chain networks: Techno-economic and environmental analysis

A general spatial optimization framework that relies on the use of a modified state-task network representation for design and planning problems in material and energy supply chain networks is presented. In brief, the proposed optimization framework considers for the tasks and states of the network: (i) the optimal selection and sizing of conversion, transfer and storage technologies, (ii) the capacity expansion for each technology over time, (iii) the inventory levels for storable states, (iv) the quantities of states converted or transferred through tasks, and (v) the optimal energy mix. Several variations of an illustrative design and planning problem of a mixed material and energy supply chain network have been solved effectively to study the trade-off between costs and emissions levels and different emissions regulation policies. A sensitivity analysis study with respect to alternative emissions caps and a multi-objective optimization example considering the conflicting objectives of total cost and emissions are also presented. The case studies showed that a more efficient way for emissions reductions is through regulation and emissions caps rather than increased emissions costs (i.e., 3.3% emissions reductions). Overall, the proposed optimization framework could be used to integrate various types of material and energy supply chain operations using a unified modeling representation towards the more efficient management of such interdependent networks under techno-economic and environmental aspects.

Resilient design and operations of process systems: Nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement

This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system.

Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.

An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint

Energy system contains multiple uncertainties, and it is hard to express all its uncertainties by only one method. In order to solve this problem, an interval-fuzzy possibilistic programming (IFPP) method was developed based on the interval parameter programming (IPP), the fuzzy possibilistic programming (FPP) and fuzzy expected value equation within a general optimization framework. In this model, uncertainties presented in terms of crisp intervals and fuzzy-boundary intervals in both the objective function and constraints can be effectively addressed, and decision maker can choose the credibility degree of constraints based on his preference. The method was applied to optimize China energy management system with CO2 emission constraint, in which a CO2 emission coefficient model was employed to estimate the CO2 emission of each province. The study set two CO2 emission scenarios to analyze China energy system planning. The optimization results showed the approach could be used for generating a series of optimization schemes under multiple credibility levels, ensuring the energy system could meet the society demand, considering a proper balance between expected energy system costs and risks of violating the constraints of CO2 emission. Strengthening the CO2 emission constraint suggests the increasing of non-fossil energy generation and a higher system costs.

A CAMERA-BASED MEASUREMENT SYSTEM FOR OPTIMIZATION OF DIFFERENTIAL-ALGEBRAIC ECOTOXICOLOGICAL MODELS

We present a general framework for measurements and optimization of differential-algebraic models. Moreover, we propose an application of the considered methodology in ecotoxicology. The differential-algebraic models can be used to describe different ecotoxicological relations. One of them is the influence of the environmental pollution on the Daphnia's movement characteristics. Changes in these characteristics can be used as a tool for assessment of neurotoxicity. The camera-based measurement and optimization system enable us to obtain the differential-algebraic ecotoxicological relations in a fully automated way.

Integrated condition-based planning of production and utility systems under uncertainty

A general rolling horizon optimization framework for the integrated condition-based operational and maintenance planning of production and utility systems in process industries is presented. In brief, the proposed optimization framework considers for the production and utility units: (i) improved unit performance degradation and recovery models that depend on both the cumulative time of operation and the unit operating levels deviation of units; (ii) modified operating capacities under online cleaning periods; (iii) different types of cleaning tasks (flexible time-window and online or offline condition-based); (iv) alternative options for offline cleaning tasks; (v) limited availability of cleaning resources; (vi) the initial state of the overall system at the beginning of each planning horizon; and (vii) terminal constraints for the rolling horizon problem. Total cost constitutes the objective function of the resulting problem and includes unit operating costs, cleaning costs, energy consumption costs and resource purchases costs. The case studies solved show that when compared to solutions obtained by sequential approaches the proposed integrated approach provides significantly better solutions in terms of total costs (reduction from 5% to 32%), and especially in cost terms related to utility units operation, energy consumption, cleaning and startup/shutdown operations. Unnecessary cleanings and purchases of resources can be avoided by the proposed integrated approach. Overall, the significant reduction in total costs is a direct result of the enhanced energy efficiency of the overall system through the efficient generation and use of energy, the improved utilization of energy and material resources resulting in a more sustainable and cleaner production practices.

A general framework for population-based distributed optimization over networks

This paper presents a population-based solution for the distributed optimization problem where the overall objective function is defined as an average of local cost functions corresponding to the nodes of a network. Populations are introduced for the nodes to cooperatively find the global optimum of the overall objective function. The main challenge is that each population cannot know the overall objective function and thus cannot directly evaluate the quality of their individuals in each iteration. To overcome this difficulty, we introduce consensus methods to design a message-passing protocol under which the local estimates can converge to the same value. We present a general framework that consists of consensus search, consensus evaluation, population evolution and local stopping steps. Compared with mathematical methods, this framework solves in-network distributed optimization problems without requiring a convexity assumption on the objective functions. The standard PSO and one of its variants, called MCO, are introduced for testing and comparison. Two simulated examples under different network topologies illustrate the feasibility of our approach.