Optimal Dynamic Strategy Research Articles

Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

A domain adaptation learning strategy for dynamic multiobjective optimization

Dynamic multiobjective optimization problems (DMOPs) require the robust tracking of Pareto-optima varying over time. Previous transfer learning-based problem solvers consume the most time on complex training of transfer model or applying a plenty of evaluations to find transferred individuals, decreasing computational efficiency. To address this issue, a domain adaptation learning strategy based dynamic multiobjective evolutionary algorithm is proposed in this paper. The mapping matrix learned by subspace distribution alignment (SDA) is utilized to transform the search space between last and current environments for promoting efficient knowledge transfer. Especially, the process of constructing mapping is derived from the simpler calculation, saving computational cost. Based on this model, transferred individuals are generated from a part of historical optima at last time. Additionally, an increment information is defined as the difference between center points of POSs in past two environments, and employed to produce a noise obeying uniform distribution. After adding it on a temporary population consisting of transferred individuals and the rest historical optima, an initial population with good diversity under new environment is formed. Experimental results on 12 benchmark functions indicate that the proposed method outperforms the other six state-of-the-art comparative ones, achieving the promising performance in solving DMOPs.

Rational inference strategies and the genesis of polarization and extremism.

Polarization and extremism are often viewed as the product of psychological biases or social influences, yet they still occur in the absence of any bias or irrational thinking. We show that individual decision-makers implementing optimal dynamic decision strategies will become polarized, forming extreme views relative to the true information in their environment by virtue of how they sample new information. Extreme evidence enables decision makers to stop considering new information, whereas weak or moderate evidence is unlikely to trigger a decision and is thus under-sampled. We show that this information polarization effect arises empirically across choice domains including politically-charged, affect-rich and affect-poor, and simple perceptual decisions. However, this effect can be disincentivized by asking participants to make a judgment about the difference between two options (estimation) rather than deciding. We experimentally test this intervention by manipulating participants’ inference goals (decision vs inference) in an information sampling task. We show that participants in the estimation condition collect more information, hold less extreme views, and are less polarized than those in the decision condition. Estimation goals therefore offer a theoretically-motivated intervention that could be used to alleviate polarization and extremism in situations where people traditionally intend to decide.

A two stages prediction strategy for evolutionary dynamic multi-objective optimization

A two stages prediction strategy for evolutionary dynamic multi-objective optimization

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

This paper introduces the development status of machine vision nondestructive testing technology and industrial IoT supervision mechanism. The study designs and implements a machine vision nondestructive testing system from two aspects: construction of industrial IoT supervision and detection model, and optimization of machine vision nondestructive testing algorithm. In this paper, the random deployment of dynamic and static nodes is adopted. The coverage rate after random deployment and the moving distance of dynamic nodes are two necessary research parameters. To improve the initial coverage and optimize the mobile path of dynamic nodes, this paper proposes a mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network. The optimized machine vision nondestructive detection algorithm is used for mobile path optimization from dynamic nodes to target locations. Simulation results show that a random deployment of 100 static nodes and 20 dynamic nodes in a 400 m × 400 m factory area works best with a coverage rate of 6.719% and an average movement distance of 23.47 m, and the movement path avoids the obstacle area. The average accuracy of the modified machine vision nondestructive testing system is 1.59% higher than that before the modification, and the average detection accuracy of the final experiment reaches 95.46%. Not only is the coverage rate better than that of the cellular structure-based dynamic node optimization scheme, but also the monitoring range of the plant tends to be more comprehensive in the actual deployment environment. Through the analysis of test results, the system achieves the monitoring and display of data on the one hand and provides a natural information access and interaction experience for IoT managers on the other hand, which meets the requirements of real time, accuracy, and stability of industrial IoT information data to a certain extent.

Mode Switching Frequency of Electrohydraulic-Power-Coupled Electric Vehicles with Different Delay Control Times

The variability of vehicle operating conditions and the multiplicity of coupler dynamics inevitably increase the frequency and complexity of cooperative power control. In this study, a novel electromechanical–hydraulic-power-coupled electric vehicle is developed and investigated. This vehicle integrates a conventional electric motor with a hydraulic pump/motor to interconvert electrical, mechanical, and hydraulic energies, while a rule-based dynamic optimal energy management strategy is designed to achieve dynamic switching of operating modes according to the operating conditions. Thus, the power-switching sensitivity is reduced by adding a delay determination link to the Stateflow. Results show that the addition of the delay link has a small effect on classical road conditions and significant suppression of road conditions with high-power-switching frequency. Therefore, the method proposed in this paper improves the energy efficiency, stability, and economic performance of electrohydraulic-power-coupled electric vehicles, which will hopefully provide a good reference for the development of electrohydraulic vehicles.

Talking Over Time ‐ Dynamic Central Bank Communication

AbstractThis paper studies the optimal dynamic communication strategy of central banks using a Bayesian persuasion game framework. In a dynamic environment, financial market participants and the general public have misaligned interests because the present and future have different relevance in their optimization problems, leading to a novel trade‐off for the monetary authority. Compared to the static benchmark, I show that the central bank (CB's) optimal dynamic communication policy should put a higher weight on talking about the present state than the future. In addition, the CB should strategically send more noisy signals than in the static benchmark.

Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization

The environments of the dynamic multiobjective optimization problems (DMOPs), such as Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the evolution process. This kind of problem poses a higher challenge for evolutionary algorithms because it requires the population to quickly track (i.e., converge) to the position of a new environment and be widely distributed in the search space. The prediction-based response mechanism is a commonly used method to deal with environmental changes, but it’s only suitable for predictable changes. Moreover, the imbalance of population diversity and convergence in the process of tracking the dynamically changing POF has aggravated. In this paper, we proposed a new change response mechanism that combines a hybrid prediction strategy and a precision controllable mutation strategy (HPPCM) to solve the DMOPs. Specifically, the hybrid prediction strategy coordinates the center point-based prediction and the guiding individual-based prediction to make accurate predictions. Thus, the population can quickly adapt to the predictable environmental changes. Additionally, the precision controllable mutation strategy handles unpredictable environmental changes. It improves the diversity exploration of the population by controlling the variation degree of solutions. In this way, our change response mechanism can adapt to various environmental changes of DMOPs, such as predictable and unpredictable changes. This paper integrates the HPPCM mechanism into a prevalent regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) to optimize DMOPs. The results of comparative experiments with some state-of-the-art algorithms on various test instances have demonstrated the effectiveness and competitiveness of the change response mechanism proposed in this paper.

Energy-saving service management technology of internet of things using edge computing and deep learning

The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30–45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

Dynamic Optimization Strategy of Large Airport Cargo Location based on Virus Evolutionary Genetic Algorithm

The automated stereoscopic warehouse of large airport plays an important role in logistics, in which cargo access efficiency is the most important part. And cargo location optimization is an effective method to improve its efficiency. After comparing and analysing the structure and working characteristics of bulk cargo processing system in large airport cargo station, the dynamic optimization problem of the cargo location was modelled. The virus evolutionary genetic algorithm (VEGA) was selected for optimization simulation, and the time-consuming rule was designed according to the actual optimization conditions. A cargo location numbering rule based on time-consuming rule was designed according to the actual optimization conditions. Simulation results show that both the convergence and calculating speeds of the VEGA have been obviously improved compared with those of the traditional genetic algorithm, which can meet the actual needs of the field better.

BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures

An efficient method is used to optimize indoor air distribution in heating systems of stratum ventilation to be beneficial to develop a potential of energy-saving with providing proper thermal comfort. To save much energy with providing proper thermal comfort, asearch for good optimization methods or strategies is required to optimize indoor air distribution affected the parameters (i.e., supply air temperature, supply vane angle, clothing insulation and outdoor air temperature) in stratum ventilation systems. In this study, the air distribution of stratum ventilation for heating is optimized by using the strategy 1 (the optimal strategy of the predicted mean vote (PMV) between −0.7 and 0.7 based on a technique of order preference by similarity to ideal solution (TOPSIS)) and the strategy 2 (the optimal strategy of PMV between −0.7 and 0.7, and draft rate (DR) less than 20). Firstly, the Computational Fluid Dynamics software is used to perform ventilation operations on the parameters for stratum ventilation systems. Secondly, according to the simulation results, the parameters are set as the input variables to construct the predictive model of ventilation performance (i.e., Energy consumption, PMV and DR) by using the Back Propagation Neural Network (BPNN) method and response surface model (RSM) method, respectively. According to the accuracy analysis of models, the BPNN method is chosen to build the predictive model. Finally, the ventilation performance is output through the optimization of the proposed BPNN-based strategy 1 and strategy 2. The optimized result shows that BPNN can build prediction models of great precision for ventilation performance. The proposed strategy 2 can save more energy by providing thermal comfort than the strategy 1. And the strategies can be beneficial to save energy as the outdoor air temperature varies. Moreover, the clothing insulation instead of supply air temperature indirectly saves energy, while providing proper thermal comfort.

An investigation for integration of deep learning and digital twins towards Construction 4.0

PurposeThe purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.Design/methodology/approachA mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.FindingsBased on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.Practical implicationsThe DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.Originality/valueThe research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach.

A change type-based self-adaptive response strategy for dynamic multi-objective optimization

Dynamic multi-objective optimization (DMO) devotes to search the optimal solutions of each environment. Therefore, it is greatly important to respond effectively to changes in environment, but none of the existing response strategies considers the impact of the type of environmental changes on the response strategies. This paper proposes a novel change type-based self-adaptive response strategy (CTSRS) for handling DMO (CTSRS-DMO). CTSRS could detect whether the Pareto-optimal set (PS) changes or not, in which PS changes is considered as a type of change and PS remains unchanged is another change type. Then, different response strategies are adaptively activated to react to environmental changes on the guidance of different change types. For the change with the PS changes over time, a linear prediction strategy wakes up to respond to environmental changes. While for the changes with PS remains unchanged, a dynamic mutational diversity introduction strategy (DMDIS) works to react to the changes. In DMDIS, the proportion of individuals to be mutated is dynamically determined by the difference between the mappings of PS in the objective space about the current environment and that of the new environment. Empirical results bear out that CTSRS-DMO outperforms the other six advanced algorithms. Finally, CTSRS-DMO commendably solves the parameter-tuning problem of proportional integral derivative (PID) controller on dynamic system.

A Novel Method of Trajectory Optimization for Underwater Gliders Based on Dynamic Identification

For underwater gliders (UGs), high trajectory accuracy is an important factor in improving the observation of ocean phenomena. In this paper, a novel method of trajectory optimization is proposed to increase the trajectory accuracy of UGs, which is approximately based on the nonlinear dynamic model, rather than the linearization model. Firstly, a dynamic model of UGs is established to analyze the effect of the input parameters on the trajectory error, based on some approximate models that replaced the dynamic model due to its strong nonlinearity. Then, an identification strategy for the trajectory error is proposed, and the trajectory optimization strategy is analyzed while considering gliding range loss and observation distance loss. Finally, the identification strategy and trajectory optimization strategy proposed in this paper are verified by a sea trial of Petrel-L. The dynamic model, identification strategy, and optimization strategy are appropriate for other UGs.

Optimal setting strategy of electrocoagulation process in heavy metal wastewater treatment plant

With the increasingly stringent environmental protection policies of various countries, the contradiction between the treatment cost and the purification degree of environmental pollutants has become increasingly significant, which has become a major factor restricting the efficient operation of wastewater treatment plants. Hence, keeping the ion concentration at the outlet as low as possible while reducing the cost are the main objectives of treating heavy metal wastewater by electrocoagulation (EC) process. However, due to the complicated mechanism and uncertain production conditions, it is difficult to achieve those goals by manually setting the current through operators’ experience. In this paper, we develop a dynamic multi-objective optimization strategy for EC process to balance these two conflicting production targets. First, we define the removal efficiency (RE) to measure the effectiveness of the EC process. Due to the anodic passivation and cathodic polarization in the EC process, the current reversing period (CRP) is proposed and optimized to ensure the stable performance of the electrodes. Then the current setting problem is formulated as a constrained multi-objective optimization problem with competing objectives of RE and cost. An interval-adjustable control parameterization (CP) approach is developed to reduce the complexity of this optimization problem. To compute this optimization problem, a heuristic method named multi-objective state transition algorithm (MOSTA) with evaluation value is investigated. The effectiveness of our model and optimization strategy is demonstrated by a successful implementation in an EC process of a wastewater treatment plant in Chenzhou, China.

Optimal Dynamic Momentum Strategies

Path Dependence of Optimal Dynamic Momentum Strategies The momentum effect has been studied by a tremendous number of papers, but surprisingly, there is little research on optimal momentum strategies. In “Optimal Dynamic Momentum Strategies,” Li and Liu explicitly solve the optimal dynamic portfolio problem when a risky asset has momentum. They show that, to optimally exploit momentum, one needs to account for path dependence, as well as momentum. In contrast, the momentum strategies discussed in most papers exploit only momentum. The optimal portfolio weight also significantly differs from that in the classic framework of Merton. Due to their path dependence, optimal portfolio weights have a wide distribution for a given level of momentum; for example, investors may short the risky asset if it has rebound price paths but leverage if it has hump-shaped price paths. This effect tends to be the most significant after large price swings. Path dependence is described through explicit formulas as well as heuristic statistics.

DPLRS: Distributed Population Learning Rate Schedule

Deep neural network models perform very brightly in the field of artificial intelligence, but their success is affected by hyperparameters, and the learning rate schedule is one of the most important hyperparameters, while the search for the learning rate schedule is often time-consuming and computationally resource-intensive. In this paper, we proposed Distributed Population Learning Rate Schedule (DPLRS) based on population joint optimization, which uses distributed data parallel deep neural network training to implement a dynamic learning rate schedule optimization strategy based on the population idea, with almost no loss of test accuracy. DPLRS is able to dynamically refine the learning rate schedule during model training instead of following the usual suboptimal strategy. We conducted experiments on typical AlexNet, VGG16, and ResNet18 using the Tianhe-3 supercomputing prototype. The results illustrate that using DPLRS to dynamically update the learning rate can greatly reduce the searching time of the learning rate schedule and meanwhile, can ensure the close performance with the latest population hyperparameter algorithm. Also, In our experiments, DPLRS lead to 123.85x speedup maximum, which prove the effectiveness and robustness of DPLRS.

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.

Discovering governing equations via moving horizon learning: The case of reacting systems

AbstractGoverning equations in the form of differential equations are fundamental modeling elements for understanding, controlling, and optimizing chemical processes. While significant advances in machine learning allow for high‐performance surrogate modeling, the resulting models typically fail to extrapolate to regimes beyond the training data and provide little physical insight regarding the underlying phenomena. In this work, we propose a moving horizon dynamic nonlinear optimization strategy that recovers parsimonious governing equations from large‐scale, noisy data sets. Differently from prior works, our approach does not rely on significant structural assumptions (mainly concerning linearity with respect to estimated model coefficients), which provides greater modeling flexibility and permits distilling governing equations of systems involving chemical reactions occurring under nonisothermal conditions. The main advantages and contributions of our proposed approach are demonstrated through two numerical case studies consisting of a continuously stirred tank reactor operated under isothermal and nonisothermal conditions.