Global Optimization Strategy Research Articles

Analytical approach to online optimal control strategy of energy storage devices in energy system

• Proposing analytical method for global optimal strategy of battery in energy system. • Reducing calculation burden of battery scheduling in energy systems. • Sensitivity analysis of energy system performance in optimal operation mode. This paper presents an analytical approach for the optimal operation of battery-integrated energy systems (BIES). Energy profile sectionalizing, achieved through the offline studies, is proposed in this paper as a framework for optimal energy dispatch in BIES, and an online strategy is then explained to control the energy system optimally. In this paper, a general framework of the battery control is addressed with considering battery characteristics and system electricity tariffs to make the proposed method applicable to real cases. Applying the proposed method to BIES shows great advantages in optimal battery scheduling as it guarantees to achieve the global optimal operation of the battery considering its actual parameters compared to the previous studies focusing on the use of optimization methods whose outputs are near-optimal solutions. Real load profile and electricity prices are used to test the proposed approach through a developed code in MATLAB software. The results show that the cost of the energy system and calculation burden of achieving optimal results are decreased compared with using the genetic algorithm (GA) and harmony search algorithm (HSA).

Research on Transmission Efficiency Oriented Predictive Control of Power Split Hybrid Electric Vehicle

Power split hybrid electric vehicle (HEV), which enables free adjustment of the engine, shows excellent performance in energy saving. The fuel economy of HEV is influenced by the battery charging/discharging. In order to maintain the exact battery sustainability, dynamic programming, a kind of global optimization strategy, is applied offline to derive the optimal solutions with minimal engine fuel consumption and exact battery SOC balance. The mode switching rules are further extracted and used to decide the operation modes for real-time control applications. The operation efficiencies of different components are analyzed, and the system transmission efficiency for the operation mode that both the engine and electric machines are engaged in is obtained. Then, the optimal problem based on model predictive control scheme is constructed with the objective of maximal powertrain transmission efficiency. The optimal problem in the prediction horizon is solved by dynamic programming to obtain the optimal control sequence. Finally, simulation studies are made. Simulation results demonstrate that the transmission efficiency oriented predictive strategy can maintain the battery charging sustainability and improve the equivalent fuel economy of the HEV effectively. The results validate the feasibility and superiority of the proposed controller.

Truck-drone team logistics: A heuristic approach to multi-drop route planning

Recently there have been significant developments and applications in the field of unmanned aerial vehicles (UAVs). In a few years, these applications will be fully integrated into our lives. The practical application and use of UAVs presents several problems that are of a different nature to the specific technology of the components involved. Among them, the most relevant problem deriving from the use of UAVs in logistics distribution tasks is the so-called “last mile” delivery.In the present work, we focus on the resolution of the truck-drone team logistics problem. The problems of tandem routing have a complex structure and have only been partially addressed in the scientific literature. The use of UAVs raises a series of restrictions and considerations that did not appear previously in routing problems; most notably, aspects such as the limited power-life of batteries used by the UAVs and the determination of rendezvous points where they are replaced by fully-charged new batteries. These difficulties have until now limited the mathematical formulation of truck-drone routing problems and their resolution to mainly small-size cases.To overcome these limitations we propose an iterated greedy heuristic based on the iterative process of destruction and reconstruction of solutions. This process is orchestrated by a global optimization scheme using a simulated annealing (SA) algorithm. We test our approach in a large set of instances of different sizes taken from literature. The obtained results are quite promising, even for large-size scenarios.

Optimization-Based Energy Management Strategy for a 48-V Mild Parallel Hybrid Electric Power System

Abstract The application of a 48-V mild hybrid powertrain system is one of the eco-friendly technologies for global CO2 reduction in the present sector of hybrid electric vehicles (HEVs). It is well known that the energy management strategy is crucial for improving the fuel economy of the HEVs. Therefore, the strengths and the limitations of dynamic programming (DP) global optimization strategy and adaptive equivalent consumption minimization strategy (ECMS) for this kind of powertrain system under new European driving cycle (NEDC) and federal test procedure (FTP75) driving cycles are discussed in this paper. The results of the DP global optimization solution have also been adopted as a reference for evaluating the degree of optimality of real-time controllers. The reference start of charge (SOC) was found to be a very important parameter for the real-time ECMS approach. Thus, an adaptive ECMS approach using the SOC obtained from DP global optimization algorithm as a reference SOC in real-time ECMS control was studied. The study results in this paper may provide some theoretical support for future energy management optimization of a 48-V mild hybrid parallel powertrain system.

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian processes, deploying two separate kernel widths to better capture rough features of the energy landscape while retaining a good resolution of local minima, an acquisition function is used to decide on which of the resulting structures is the more promising and should be treated at the first-principles level. The method is demonstrated to outperform by 2 orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, global optimization with first-principles energy expressions is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface.

Adjoint-based anisotropic hp-adaptation for discontinuous Galerkin methods using a continuous mesh model

In this paper we propose an adjoint-based hp-adaptation method for conservation laws, and corresponding numerical schemes based on piecewise polynomial approximation spaces. The method uses a continuous mesh framework, similar to that proposed in [1], where a global optimization scheme was formulated with respect to the error in the numerical solution, measured in any Lq norm. The novelty of the present work is the extension to more general optimization targets. Here, any solution-dependent functional, which is compatible with an adjoint equation, may be the target of the continuous-mesh optimization. We present the rationale behind the formulation of the optimization problem, with particular emphasis on the continuous mesh model, and the relevant adjoint-based error estimate. Additionally we combine the adjoint-based error estimates with the polynomial optimization strategy from [2] to present a complete hp-adaptation method which shows exponential convergence in the target function. The h-only mesh adaptation strategy of this work has been presented as a conference proceeding earlier [3]. Numerical experiments are carried out to demonstrate the viability of the scheme.

The Action Mechanism of Rotor–Stator Interaction on Hydraulic and Hydroacoustic Characteristics of a Jet Centrifugal Pump Impeller and Performance Improvement

The aim of this study was to investigate the action mechanism of the rotor–stator interaction (RSI) on the transient flow field and hydrodynamic noise field inside the impeller of jet centrifugal pumps (JCPs) and optimize effects of the guide vane on the hydraulic and hydroacoustic characteristics of the impeller. The numerical method of CFD (computational fluid dynamics) coupled with CFA (computational fluid acoustics) was used to analyze the correlation between the guide vane and the flow/sound performances of the impeller. The orthogonal test method, with the hydroacoustic performance of the impeller taken as the objective, was used to optimize the structural parameters of the guide vane for the stability of the hydraulic performance of the JCP. The results show that the RSI leads to a significant increase in the hydroacoustic level of the impeller, but it is indispensable for improving the hydraulic performance of the pump. The RSI effect on the fluctuation intensity of the transient flow field inside the impeller is much more sensitive than the time-average, and the fluctuation intensity of the flow field is positively correlated with the vortex intensity inside the impeller. When the impeller geometry is constant, the evolution processes of the flow field inside the impeller are mainly related to the blade number of the guide vane; when the number of guide vanes is given, the RSI effect on the hydroacoustic characteristic of the impeller is characterized by a positive correlation between the total sound pressure level (SPL) and the fluctuation intensity of the flow field. The frequency spectrum characteristics of the hydroacoustic SPL of the impeller are not consistent with the pressure fluctuation characteristics inside the impeller. The pressure fluctuation characteristics are related not only to the blade number and speed of the impeller but also to its wake characteristics determined by the guide vane. The optimization scheme for the stable hydraulic performance of the JCP significantly reduced the total SPL of the impeller compared with the original scheme, which verifies the feasibility of using the weight matrix optimization method to obtain the global optimization scheme.

A multi-population differential evolution with best-random mutation strategy for large-scale global optimization

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO.

A global optimization strategy based on the Kriging surrogate model and parallel computing

The computational efficiency and accuracy of the global solution are the main performance indicators of an optimization algorithm to solve the structural and multidisciplinary optimization problems. The Kriging-based optimization algorithm can satisfy certain engineering requirements by applying the single-point sequence sampling method. However, this conventional algorithm does not efficiently apply the parallel performance of high-performance multi-core computers. This study aims to propose a global optimization strategy based on the Kriging surrogate model and parallel computing depending on the multi-peak characteristics of the expect improvement (EI) function. The proposed method searches out the locations of multiple peaks of the EI function by introducing a so-called P-EI function; it then simultaneously includes multiple sampling points located nearby these peaks. Furthermore, an efficient design domain reduction technique is applied to improve the accuracy of the global solution. When compared with the traditional Kriging-based methods, the proposed method can effectively obtain multiple peaks of the EI function without solving complex expressions of the high-dimensional EI function exhibiting a joint probability density distribution. The parallel computation ability, global performance, and solution accuracy of our method are validated via typical test functions and structural optimization problems.

Optimization and Application for Hydraulic Electric Hybrid Vehicle

Targeting the application of medium and heavy vehicles, a hydraulic electric hybrid vehicle (HEHV) was designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink was applied to establish the pure electric vehicle and HEHV models, and backward simulation was adopted for the simulation, to get the variation of torque and battery state of charge (SOC) through New York City Cycle of the US Environmental Protection Agency (EPA NYCC). Based on the simulation, the energy management strategy was designed. In this research, the rule-based control strategy was implemented as the energy distribution management strategy first, and then the genetic algorithm was utilized to conduct global optimization strategy analysis. The results from the genetic algorithm were employed to modify the rule-based control strategy to improve the electricity economic performance of the vehicle. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle was improved by 36.51% compared to that of a pure electric vehicle. The performance of energy consumption after genetic algorithm optimization was improved by 43.65%.

An efficient parallel global optimization strategy based on Kriging properties suitable for material parameters identification

Material parameters identification by inverse analysis using finite element computations leads to the resolution of complex and time-consuming optimization problems. One way to deal with these complex problems is to use meta-models to limit the number of objective function computations. In this paper, the Efficient Global Optimization (EGO) algorithm is used. The EGO algorithm is applied to specific objective functions , which are representative of material parameters identification issues. Isotropic and anisotropic correlation functions are tested. For anisotropic correlation functions, it leads to a significant reduction of the computation time. Besides, they appear to be a good way to deal with the weak sensitivity of the parameters. In order to decrease the computation time, a parallel strategy is defined. It relies on a virtual enrichment of the meta-model, in order to compute q new objective functions in a parallel environment. Different methods of choosing the qnew objective functions are presented and compared. Speed-up tests show that Kriging Believer (KB) and minimum Constant Liar (CLmin) enrichments are suitable methods for this parallel EGO (EGO-p) algorithm. However, it must be noted that the most interesting speed-ups are observed for a small number of objective functions computed in parallel. Finally, the algorithm is successfully tested on a real parameters identification problem.

Research on Performance of Artificial Bee Colony Algorithm Based on Benchmark Test Function

Because the artificial bee colony(ABC) algorithm has problems of poor development ability, slow convergence rate and easy to fall into local optimum, an improved ABC algorithm is proposed. The algorithm combines the global optimal guidance strategy, we propose a new location update formula to improve the efficiency of the iterative optimization process, and introduce the mining coefficient in the formula to improve the accuracy of global optimization. In addition, the beta distribution is introduced during the scouting bee phase, which improves the disturbance ability of the algorithm and prevents it from falling into local extremum effectively. Finally, the simulation results of four benchmark functions of Sphere, Rastrigin, Rosenbrock and Griewank show that compared with the traditional algorithm and other improved artificial bee colony algorithm, the proposed algorithm has faster convergence speed and better optimization ability.

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a $\gamma $ -information map. The $\gamma $ -information map can transform the continuous dynamic coverage process into a discrete $\gamma $ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage.

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

A primary challenge to the implementation of hybrid electric vehicles (HEVs) is the design of the energy management strategy for the vehicle. Most conventional strategies have been designed for passenger vehicles using rule-based or optimization-based control strategies that rely on navigation support; therefore, the optimal performance of heavy-duty HEVs that lack navigation support cannot be achieved using conventional strategies. In this study, we propose a nonlinear model predictive control (NMPC) for heavy-duty HEVs based on a random power prediction method. To obtain the models of multiple power sources, we analyzed the structure and powertrain of the vehicle using mathematical modeling methods. To account for the lack of navigation support, we used the data-driven prediction method by combining the grey model and Markov chain methods to obtain higher-accuracy ultra-short-term power prediction. Considering the predicted disturbance power, we established a multi-objective optimization function with explicit constraints to optimize fuel consumption, bus voltage, and battery state of charge. Under these constraints, a nonlinear programming problem based on the NMPC could be restricted to find an optimal numerical solution in real time. We validated the control strategy on a hardware-in-the-loop simulation platform and compared its results with those obtained using thermostat control, fuzzy, and dynamic programming approaches. The proposed control strategy achieved a considerably better all-round performance than rule-based control strategies; moreover, the results were considerably similar compared with those of offline global optimization strategies. Furthermore, the proposed method achieved excellent real-time operation capability, thereby providing a valuable reference for practical engineering applications.

Constructing convex energy landscapes for atomistic structure optimization

We propose a global optimization strategy for atomistic structure determination based on two new concepts: a few-atom complementary energy landscape and atomic role models. Global optimization of costly energy expressions may be aided by performing some of the optimization on model energy landscapes. These are often based on a sum-of-atomic-contributions form that accurately reproduces every local energy minimum of the true energy expression. However, we propose that, by not including all atomic contributions, the resulting energy landscapes may become more convex, making the search for the global optimum more facile. A role model is someone we aspire to be more like; in the same vein we define the role model of an atom to be another atom whose local environment the first atom seeks to obtain itself. Basing a complementary energy landscape on the distance of some atoms from their role models in a feature space, we arrive at a useful few-atom complementary energy landscape. We show that relaxation in this landscape is an effective mutation when employed in an evolutionary algorithm used to identify the bulk cristobalite structure of ${\mathrm{SiO}}_{2}$ and the $(1\ifmmode\times\else\texttimes\fi{}4)$ surface reconstruction of anatase ${\mathrm{TiO}}_{2}$(001).

An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks

In future wireless communication, a large number of devices equipped with several different types of sensors will require access networks with diverse quality-of-service constraints. In cellular network evolution, the long term evolution advanced (LTE-A) networks has standardized Machine-to-Machine (M2M) features. Such M2M technology can provide a promising infrastructure for Internet of things (IoT) sensing applications, which usually require real-time data reporting. However, LTE-A is not designed for directly supporting such low-data-rate devices with optimized energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. This paper investigate the maximum energy efficient data packets M2M transmission with uplink channels in LTE-A network. We formulate it into a joint problem of Modulation-and-Coding Scheme (MCS) assignment, resource allocation and power control, which can be expressed as a non-deterministic polynomial hard (NP-hard) mixed-integer linear fractional programming problem. Then we propose a global optimization scheme with Charnes-Cooper transformation and Glover linearization. The numerical experiment results show that with limited resource blocks, our algorithm can maintain low data packets dropping ratios while achieving optimal energy efficiency for a large number of M2M nodes, comparing with other typical counterparts.

Beam Line Optimization using Derivative-Free Algorithms

The present study focuses on the beam line optimization from the heavy-ion synchrotron SIS18 to the HADES experiment. BOBYQA (Bound Optimization BY Quadratic Approximation) solves bound constrained optimization problems without using derivatives of the objective function. The Bayesian optimization is another strategy for global optimization of costly, noisy functions without using derivatives. A python programming interface to MADX allow the use of the python implementation of BOBYQA and Bayesian method. This gave the possibility to use tracking simulation with MADX to determine the loss budget for each lattice setting during the optimization and compare both optimization methods.

Predictive Energy Management for Hybrid Electric Vehicle Considering Driver’s Intention

The driver's intention determines the vehicle's Macrodriving state. On the premise of ensuring that the driving state of the vehicle conforms to the driver's intention, it is of practical significance to study the energy-saving of the vehicle. Through the correlation analysis of acceleration / brake pedal signal with vehicle speed and acceleration under real working conditions, the strong correlation between driver input and vehicle speed appears in the range of 4-6 seconds. The mapping relationship between driver’s intention and driver's expected speed is constructed by extreme learning machine. Based on this, the model predictive control for hybrid electric vehicle is carried out. It is compared with the global optimal control strategy solved by dynamic programming and the instantaneous optimal control strategy under the same discrete precision. The results show that compared with the instantaneous optimal control strategy, model predictive control based on driver’s intention can save 9.92% of the energy consumption while meeting the driver’s intention (RMSE 0.9995m/s).

Confidence-based dynamic optimization model for biomedical image mosaicking.

Biomedical image mosaicking is a trending topic. It consists of computing a single large image from multiple observations and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur, or either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/feature-based methods perform poorly. In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mosaicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well.