Two-level Optimization Algorithm Research Articles

Two-level balancing multi-objective algorithm for trapezoidal type-2 fuzzy flexible job shop problems

Uncertainty remains a critical issue in realistic applications in many fields. However, there is little literature considering trapezoidal interval type-2 fuzzy set (TIT2FS) in scheduling problems. To fill this gap, we propose a two-level balancing multi-objective optimization algorithm for a distributed flexible job shop scheduling problem (DFJSP), wherein two typical constraints are considered: the TIT2FS processing time and the transportation resource limitation. Two objectives are to be minimized: the TIT2FS fuzzy makespan and energy consumption. A two-level evolution mechanism is employed. The first level comprises two populations to optimize the two respective objectives, while the second level is composed of two populations to perform the convergence and diversity balancing tasks. Next, an efficient problem-specific initialization method with several heuristics is presented. Subsequently, different types of mutation and crossover operators are proposed under the consideration of problem knowledge. To further improve the performance of exploration and exploitation abilities, we present a balancing enhanced local search method. Finally, we performed detailed comparisons with four efficient algorithms. The results show that the two-level optimization algorithm was efficient in both convergence and diversity abilities, indicating that our algorithm can efficiently achieve a balance between these abilities.

A two-level optimization algorithm for path planning of bionic robotic fish in the three-dimensional environment with ocean currents and moving obstacles

Path planning is not only the premise of underwater navigation and obstacle avoidance, but also the core and key to improve the operation durability for the bionic robotic fish. In this paper, the path length, smoothness and safety factor are selected as the optimization performance indexes. In order to solve the path planning with ocean currents and moving obstacles, a two-level optimization algorithm is proposed for the bionic robotic fish. In the first level of optimization, it solves the global path planning problem with known environment. In the second level of optimization, based on the optimization results of the first level of optimization, the local optimal path is obtained by the dynamic optimization method in the environment with unknown static and moving obstacles, which can solve the problem of obstacle avoidance for irregular obstacles effectively. The two-level optimization algorithm improves the accuracy of the path planning. Fuzzy adaptive genetic algorithm (FAGA) is given to further improve the optimization performance. The proposed algorithm can improve the optimization performance of the path planning, whose effectiveness is demonstrated by the simulation and experiment results.

A greedy-based hybrid trajectory optimization method for the “Dyson Ring” building problem

The method used and results found by the team “ASTL-NUAA” from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition.

Optimal two-level active and reactive energy management of residential appliances in smart homes

The optimal performance of the smart home energy management system (SHEMS) in coordinating and integrating the equipment in the house plays a significant role in increasing the efficiency and economic benefits of the smart home. This paper proposes a two-level optimization algorithm for the energy management of residential appliances within a smart home, including interruptible, uninterruptible, thermostatically controlled, and non-schedulable loads, as well as the charging/discharging strategies of electric vehicles (EVs) and energy storage systems (ESSs) in the presence of distributed energy resources (DERs). In the first level of optimization, to minimize the cost of electricity, the operating time of smart home components is determined by SHEMS, and active power is exchanged between equipment. At this level, the impact of demand response (DR) constraints on the cost and load factor (LF) of the smart home have also been investigated. In the second level, considering the optimal cost obtained in the first level, in addition to the active power, the reactive power required by loads is also provided by using the additional capacity of EV and ESS inverters and improves the power factor (PF) of the house at the connection point to the grid. Day-ahead electricity costs are calculated depending on different pricing signals (TOU, RTP) with the power exchange capacity (buying/ selling) with the grid. The effectiveness of the proposed strategy is examined on a typical smart home over a 24-h. By reviewing the numerical results obtained for the cost and the LF in different scenarios, and observing the increase of the PF of home from 0.65 to 0.94 and also examining the performance of the equipment during the planning time, the efficiency of the model is proved.

Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking

Treatment design for musculoskeletal disorders using in silico patient-specific dynamic simulations is becoming a clinical possibility. However, these simulations are sensitive to model parameter values that are difficult to measure experimentally, and the influence of uncertainties in these parameter values on the accuracy of estimated knee contact forces remains unknown. This study evaluates which musculoskeletal model parameters have the greatest influence on estimating accurate knee contact forces during walking. We performed the evaluation using a two-level optimization algorithm where musculoskeletal model parameter values were adjusted in the outer level and muscle activations were estimated in the inner level. We tested the algorithm with different sets of design variables (combinations of optimal muscle fiber lengths, tendon slack lengths, and muscle moment arm offsets) resulting in nine different optimization problems. The most accurate lateral knee contact force predictions were obtained when tendon slack lengths and moment arm offsets were adjusted simultaneously, and the most accurate medial knee contact force estimations were obtained when all three types of parameters were adjusted together. Inclusion of moment arm offsets as design variables was more important than including either tendon slack lengths or optimal muscle fiber lengths alone to obtain accurate medial and lateral knee contact force predictions. These results provide guidance on which musculoskeletal model parameter values should be calibrated when seeking to predict in vivo knee contact forces accurately.

Subregion Multifeature Fusion Oblique Vehicle Detection Algorithm

Reducing traffic accidents and relieving traffic congestion are two main topics for Chinese road traffic management. Vehicle vision-based vehicle detection and tracking technology is the primary link in road environment perception. The detection technology for forward vehicles is becoming increasingly mature, and commercial products have appeared. Detection is more complicated for oblique vehicles. This research mainly studies the detection and tracking of oblique vehicles. The main work is as follows: First, the image preprocessing method in image processing technology is mainly studied. With focus on research of lane line detection algorithm, an improved lane line detection algorithm combining Hough transform and K-means clustering is proposed to improve the accuracy. With the lane line taken as the standard, the vehicle detection area is divided and combined with the ROI area extraction to further narrow the detection range. With the aim of performing vehicle detection, a two-level optimization algorithm is proposed. First, the method of initial inspection and machine learning detection verification of feature detection is presented. The vehicle target feature and image vertical edge feature fusion are used to generate the suspected target region. An improved dual threshold segmentation algorithm is then proposed to improve the effect of shadow feature extraction. The cascading AdaBoost classifier is combined to generate the target verification area. To address the real-time problem, kernel principle component analysis using Gaussian kernel function is applied to reduce the dimensionality of image feature and improve real-time performance. Experimental results show that the identification accuracy is up to 90% for oblique vehicles when the optimization algorithm of the mixing of shadow feature and image vertical edge feature is used. In good weather, the accuracy is up to 95% when using the AdaBoost classifier combined with HOG+Haar-like feature; The detection time is reduced by 26% when kernel principal component analysis is used. The result of this project is valuable for dynamic oblique vehicle detection.

Optimization of Electric Grids with Stochastic Elements

The paper proposes a methodology for modeling and optimization of electric grids with stochastic elements. The stochastic properties of modern electric grids and electric grids of the near future are associated with the development of Smart Grids. Electric vehicles are especially good at highlighting the problems that arise when modeling smart grids. Therefore, the optimization algorithm is described by the example of a urban electric grid with the presence of charging stations for electric vehicles and storage devices using V2G technology. The author has proposed a two-level optimization algorithm. At the lower level of the algorithm, the problem of optimizing the economic efficiency of the charge and power output in the case of connecting the EV using V2G technology is solved. At the top level, the optimization problem is considered from the point of view of the grid company. Much attention is paid to the problem of dynamic optimization of electric grid with stochastic elements, since this task is relevant for the automatic and automated control of normal modes of EPS. The proposed methodology for modeling and optimization of electric grids with stochastic elements is implemented as embedded software and can be used in automatic optimal and emergency control systems

Secure Energy-Efficient Transmission for SWIPT Intelligent Connected Vehicles With Imperfect CSI

In this paper, the secure energy-efficient transmission problem for an artificial-noise (AN)-aided simultaneous wireless information and power transfer (SWIPT) and vehicle-to-infrastructure (V2I) communication system with the imperfect channel state information (CSI) is studied. The secure energy efficiency problem is formulated with the imperfect CSI, where the constraints contain the minimum secrecy rate request of the legitimate vehicle (LV), the minimum energy harvesting (EH) request, the maximum transmit power, and the power splitting (PS) value. By exploiting the maximum ratio transmission (MRT) scheme and the norm-bounded matrix theory, we convert the original intractable problem to a fractional optimization one with three variables. Then, a two-level optimization algorithm is presented for the nonconvex fractional optimization problem. By introducing an auxiliary slack variable, the first level problem can be solved via one-dimensional optimization algorithm. The second level fractional optimization problem was furhther converted into an integer optimization one by the Dinkelbach method, and the closed-form solutions of the transmit power and the AN power are provided. Simulation results are given to verify the performance of our presented algorithm.

An Online Energy Management System for a Grid-Connected Hybrid Energy Source

An online energy management system (EMS) for a grid-connected hybrid energy source is proposed in this paper. The hybrid source combines renewable energy resources (wind and photovoltaic), battery storage, variable speed diesel generator, and load management system. The proposed EMS consists of two-level optimization algorithm: 1) the rolling optimization and 2) the feedback intrasample correction. The rolling optimization part is established to schedule operation based on the forecast data using the model-predictive control approach. The rolling dispatch scheduling is then adjusted based on an intrasample feedback correction that compensates for the prediction error of the forecast data. The optimization problem was formulated as mixed-integer linear programming framework with two objectives: 1) to minimize the total operating cost and 2) to minimize the pollutant gas emissions. The battery daily number of cycles and the minimum state of charge are considered as decision variables that are optimally determined by the EMS to minimize the total system operating cost while considering all the practical constraints of the different energy sources. Different case studies with different market profiles demonstrate the effectiveness of the proposed approach, and the results have showed a significant reduction in the total system cost.

Reliability-based topology optimization under shape uncertainty modeled in Eulerian description

This paper presents a reliability-based topology optimization method under geometrical uncertainties. First, we briefly introduce the concept of topology optimization. Then, we explain how shape uncertainty is modeled in Eulerian description, using an advection equation and a Karhunen-Loeve expansion. Based on the shape uncertainty modeling, we formulate a reliability measure for the shape uncertainty, briefly introducing the inverse reliability method. Two optimization problems, a minimum mean compliance problem and an optimum design problem for a compliant mechanism, are then formulated using the proposed shape uncertainty modeling. The design sensitivity analysis for the reliability analysis and optimization procedure, performed using the adjoint variable method, is then explained. A two-level optimization algorithm is constructed next, in which the inner iteration is used for reliability analysis and the outer is used for updating design variables. Finally, three numerical examples are provided to demonstrate the validity and the utility of the proposed method.

Secure transmission for multi-antenna wireless powered communication with co-channel interference and self-energy recycling

In this paper, we investigate secure transmission resource allocation problem for multi-antenna wireless powered communication networks (WPCNs) where a full-duplex (FD) wireless device (WD) sends confidential signals to an information receiver (IR), and an energy transmitter (ET) is employed to provide radio energy to the FD WD and to jam the multiple eavesdroppers at the same time. In order to improve energy efficiency, the FD WD also collects energy from its own loop interference signals. The energy FD WD harvested is supplied to its information transmission in the same time slot. We aim to maximize the achievable secrecy rate at the IR by designing the transmit covariances of the WD and ET under the energy harvesting constraint of WD and transmit power constraints of WD and ET, respectively. Firstly, we consider perfect channel state information (CSI) of all links. Due to the non-convexity of the optimization problem, we propose a two-level optimization algorithm, the inner level optimization problem is a semi-definite programming (SDP) problem and the outer level optimization problem is a single-variable optimization problem. The optimal solution is derived and the sufficient condition to ensure that transmit beamforming having rank-one structure is also provided. Then, we consider robust secure transmit scheme design where the CSI uncertainties are molded by the worst-case model, and S-procedure and its extension are employed to transform the semi-infinite constraint problem into finite constraint problem. Similarly, the optimal robust solution and the sufficient condition to ensure that robust transmit beamforming is optimal are provided. Simulation results demonstrate the effectiveness of our proposed strategies.

Autonomous Two-Tier Cloud-Based Demand Side Management Approach with Microgrid

Demand side management (DSM) is an important application of the future smart grid. DSM programs allow consumers to participate in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. Therefore, in this paper, we propose a two-tier cloud-based DSM to control the residential load of customers equipped with local power generation and storage facilities as auxiliary sources of energy. We consider a power system consisting of multiple regions and equipped with a number of microgrids. In each region, an edge cloud is utilized to find the optimal power consumption schedule for customer appliances in that region. We propose a two-level optimization algorithm with a linear multilevel cost function. At the edge cloud, the power consumption level of local storage and the amount of power being demanded from both local storage facilities and power grid are scheduled by using a bi-level optimization approach. The core cloud then gathers information of the total demand from consumers in different Regions and finds the optimal power consumption schedule for each microgrid in the power system. Simulation results show that the proposed model reduces consumption cost for the customers and improves the power grid in terms of peak load and peak-to-average load ratio.

Simultaneous Wireless Information Power Transfer for MISO Secrecy Channel

This paper investigates simultaneous wireless information and power transfer (SWIPT) for a multiple-input–single-output (MISO) secrecy channel. First, transmit beamforming without artificial noise (AN) is designed to achieve the secrecy rate maximization problem subject to the transmit power and the energy harvesting (EH) constraints. This problem is not convex, but this can be solved by applying a bisection search to a sequence of the associated power minimization problems, each of which can be solved by using a novel relaxation approach. Moreover, we extend our proposed algorithms to the robust case by incorporating channel uncertainties. Then, transmit beamforming with AN is investigated for the secrecy rate maximization problem, where two relaxation approaches are presented, i.e., a two-level optimization algorithm and a successive convex approximation (SCA), to solve the secrecy rate maximization problem. In addition, tightness analyses of rank relaxation are provided to show that the optimal transmit covariance matrix exactly returns rank one. Simulation results are provided to validate the performance of the proposed algorithms.

Multidisciplinary Optimization of a Turbocharger Radial Turbine

This paper presents a multidisciplinary design optimization of a turbocharger radial turbine for automotive applications with the aim to improve two major manufacturer requirements: the total-to-static efficiency and the moment of inertia of the radial turbine impeller. The search for the best design is constrained by mechanical stress limitations, by the mass flow and power, and by aerodynamic constraints related to the isentropic Mach number distribution on the rotor blade. The optimization of the radial turbine is performed with a two-level optimization algorithm developed at the von Karman Institute for Fluid Dynamics. The system makes use of a differential evolution algorithm, an artificial neural network (ANN), and a database as a compromise between accuracy and computational cost. The ANN performance predictions are periodically validated by means of accurate steady state 3D Navier-Stokes and centrifugal stress computations. The results show that it is possible to improve the efficiency and the moment of inertia only in a few numbers of iterations while limiting the stresses to a maximum value. Based on the large number of evaluated designs during the optimization, this paper provides design recommendations of a turbocharger radial turbine at least for a good preliminary design.

A Two-level Optimization Model and Algorithm for Energy-Efficient Urban Train Operation

Metro takes the most part of energy consumption in urban public service systems, it's important to make the train movement in an energy-efficient way. Based on the characteristics of train movement and control rules of locomotive, an urban train running on an uneven rail with the specific run-time for minimal energy consumption can be formulated as a two-level hierarchical problem. On the first leve1, an optimization model is designed to decide the appropriate coasting point(s) and number(s) of inter-station run for energy-efficient urban train operation. On the second leve1, an optimization model of arranging the train travel time of inter-station run is presented for minimal energy consumption. Algorithms for solving the two-level optimization model are developed based on Genetic Algorithm. A case study shows that the two-level optimization model and algorithm are effective for energy-efficient urban train operation on a long-distance line with several sections. The result indicates that the two-level method can save energy 19.06% within scheduled run-time and enhance the recovery ability of train movement compared with traditional method.

COMPUTATIONAL OPTIMIZATIONS TOWARDS AN ACCURATE AND RAPID ELECTROMAGNETIC EMISSION MODELING

In this paper, a parametric electromagnetic radiated emis- sion model has been explored. Several mathematical improvements with respect to its extraction and computational performance have been deployed. The model, represented with an array of radiating electric dipoles, predicts the electromagnetic emission of components and systems. Core-level changes have been made in order to extract the model parameters: the dipole positions, their orientations and cur- rents, and the efiective relative permittivity from near-fleld measure- ments. Thresholding and windowing techniques are used to detect and optimize dipole positions, directly from the fleld data. A fast and mem- ory e-cient two-level optimization algorithm based on the Levenberg- Marquardt non-linear least squares technique is implemented for para- metric extraction. All the constraints of the previous model have been overcome and the system is validated for mono-substrate and multi- substrate devices from measurements and/or simulations, with promis- ing results. A tremendous improvement in modeling capability and performance has been obtained when compared with that of its erst- while counterpart.

Configuration Optimization of Underground Cables for Best Ampacity

This paper presents a method for configuring the locations of any number of cables, for the best total ampacity. The optimal configuration is determined through a proposed two-level optimization algorithm. At the outer level, a combinatorial optimization based on a genetic algorithm explores the different possible configurations. The performance of every configuration is evaluated according to its total ampacity, which is calculated by using a convex optimization algorithm. The convex optimization algorithm, which forms the inner level of the overall optimization procedure, is based on the barrier method. The proposed approach is tested for a duct bank installation containing 12 cables and 15 ducts, comprising two circuits and two cables per phase, and compared with a brute force method of considering all possible configurations. The proposed approach is also applied to an installation consisting of a single circuit inside a large magnetic steel casing.

Two-Level Optimization Algorithm for Heat Exchanger Networks Including Pressure Drop Considerations

A mixed-integer nonlinear programming (MINLP) algorithm to account for the effects of the allowable pressure drops for process streams on the structure and cost of heat exchanger networks is presented. Because MINLP models for large problems typically show significant convergence problems, the synthesis model is decomposed into two levels. For a given energy recovery level, an MINLP model is formulated in an inner loop using assumed values for the film heat-transfer coefficients for each stream. In the outer loop, with the use of the areas provided by the MINLP model and thermal-hydraulic models that relate the exchanger area, pressure drops, and film coefficients, updated values of the film coefficients are calculated for each stream according to the specified values for allowable pressure drops. The algorithm provides a network that makes use of the allowable pressure drops specified for the process streams at the total minimum yearly cost. The algorithm shows excellent convergence properties, can incor...

Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic

Semiconductor gas sensors are essentially not selective to detect a single chemical species in a gaseous mixture and also the response of the sensor in most cases is influenced by the variations of ambient humidity and temperature. One of the solutions is the analysis of the dynamic response of a single sensor with modulated temperature. For the non-linear output signal the fast Fourier transform transform was calculated. The zero-order amplitude and the phases of higher harmonics were selected. These quantities served as input data for the fuzzy model of the sensor. The hybrid fuzzy sensor model, based essentially on Takagi-Sugeno-Kang (TSK) theory, comprised a two-level optimization algorithm. The authors elaborated that algorithm, utilizing conjugate gradient and genetic algorithm methods. At the output of the fuzzy model the concentrations of ethanol with minimized influence of humidity variations were obtained.

Two-level optimization for decentralized production and advertising planning

Production and advertising decisions are often made in a decentralized fashion; that is, the decisions are made somewhat independently by the corresponding departments of a firm, and these decisions are modified gradually to cope with the best interest of the firm. We treat the joint production and advertising problem as a serial system, where the advertising expenditure is an input to the production level, and we present a two-level optimization algorithm for this problem. With this approach the original is first divided into two subproblems and then the solution to the overall problem is obtained by solving the subproblems iteratively. We later show numerically that the decisions made by this approach are consistent with those planned in a centralized fashion.