Dynamic Optimization Method Research Articles

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.

Dynamic reconfiguration optimization of intelligent manufacturing system with human-robot collaboration based on digital twin

In Industry 4.0, the emergence of new information technology and advanced manufacturing technology (e.g., digital twin, and robot) promotes the flexibility and smartness of manufacturing systems to deal with production task fluctuation. Digital twin-driven manufacturing system with human-robot collaboration is a typical paradigm of intelligent manufacturing. When production task changes, manufacturing system reconfiguration with dynamic opeartion task allocation between operator (human) and robot is a key manner to maintain the production efficiency of intelligent manufacturing system with human-robot collaboration. However, the differences between operator and robot are neglected during reconfiguration of manufacturing system with human-robot collaboration. To promote the reconfiguration accuracy and production efficiency, a dynamic reconfiguration optimization method of intelligent manufacturing system with human-robot collaboration based on digital twin is proposed in this paper, which the different characteristics between operator and robot are considered during reconfiguration optimiztion. Firstly, a multi-objectives optimization model is constructed involving minimum production cost, minimum production time, and minimum idle time to assign operation tasks between operator and robot, where human factor is considered to ensure the production efficiency of operator. Second, nondominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the proposed dynamic reconfiguration optimization model. Finally, a case study is provided to demonstrate the effectiveness of the proposed reconfiguration optimization method for intelligent manufacturing system with human-robot collaboration.

A dynamic optimization method for power distribution network operation with high ratio photovoltaics

With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi-equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi-objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi-objective optimization problem (MOP) is transformed into a single-objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust-region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33-bus, PG&E 69-bus, 292-bus and 1180-bus test cases. The experimental results show the rapidity and robustness of the proposed method.

Individual-based self-learning prediction method for dynamic multi-objective optimization

This paper proposes an individual-based self-learning prediction method for dynamic multi-objective optimization problems, called ISPM, to effectively track the time-varying Pareto-optimal set (POS) in a dynamic environment. The ISPM adjusts the reference points by individual-based self-learning, which differs from existing approaches based on fixed reference points. The self-learning reference points are given according to the information from the previous population to divide the Pareto-optimal front (POF) into the objective space as uniformly as possible. One of the ISPM’s advantages is it can improve the influence of the corresponding non-uniform POF for the population’s prediction. Furthermore, it is known that each reference point and the original point can form a vector in the objective space. Each vector can present a subregion in the objective space. The moving direction of each subregion in the last two environments is the reference when improving the population in the new environment to realize local search. Meanwhile, we roughly calculate the change degree of the environment using the self-learning reference point sets at the last two environments to realize the global search. The comprehensive experimental results show that the proposed algorithm can effectively balance convergence and diversity compared with other state-of-the-art methods.

Integrating Dynamic Economic Optimization and Nonlinear Closed-Loop GPC: Application to a WWTP

In this paper, a technique that integrates methods of dynamic economic optimization and real-time control by including economic model predictive control and closed-loop predictive control has been developed, using a two-layer structure. The upper layer, which consists of an economic nonlinear MPC (NMPC), makes use of the updated state information to optimize some economic cost indices and calculates in real time the economically optimal trajectories for the process states. The lower layer uses a closed-loop nonlinear GPC (NCLGPC) to calculate the control actions that allow for the outputs of the process to follow the trajectories received from the upper layer. This paper also includes the theoretical demonstration proving that the deviation between the state of the closed-loop system and the economically time varying trajectory provided by the upper layer is bounded, thus guaranteeing stability. The proposed approach is based on the use of nonlinear models to describe all the relevant process dynamics and cover a wide operating range, providing accurate predictions and guaranteeing the performance of the control systems. In particular, the methodology is implemented in the N-Removal process of a WWTP and the results demonstrate that the method is effective and can be used profitably in practical cases such as the chemical, refinery and petrochemical process industries.

A Diverse Ensemble Deep Learning Method for Short-Term Traffic Flow Prediction Based on Spatiotemporal Correlations

In this paper, considering spatiotemporal correlations, we propose a novel short-term traffic flow prediction method that is based on diverse ensemble deep learning. First, a new measurement function of spatial correlation is established, quantifying the spatial correlation of traffic flow parameters. Second, the convolutional neural network model embedded long short-term memory (LSTM-CNN) is used, enhancing the learning ability of the spatiotemporal correlation of traffic flow parameters. Third, according to the network structure, ensemble rules of diverse LSTM-CNNs are constructed, combining a series of different and moderately accurate LSTM-CNN models and improving the robustness of the algorithm. Finally, a dynamic optimization method for the weight parameters of the ensemble elements is proposed to accommodate the changes in the actual road networks. The experiments show that the measuring method of spatial correlations, the ensemble combination rules of diverse LSTM-CNN and the dynamic optimization method of ensemble element weight parameters, which are proposed in this paper, have positive effects on improving the performance of the prediction algorithm. In addition, the short-term traffic flow prediction method proposed in this paper can adapt to changes in the traffic flow. It can obtain a better prediction effect even with a small sample, and the prediction performance is better than that of the other four classical short-term traffic flow prediction methods.

Optimal Emergency Evacuation Route Planning Model Based on Fire Prediction Data

For the emergency evacuation of cruise ships in case of sudden fire, this research proposes a dynamic route optimization method based on the improved A∗ algorithm for real-time information, in order to obtain the real-time optimal evacuation route. Initially, a basic network topology diagram is established according to the internal structure of the cruise ship. Before the occurrence of the accident, the A∗ algorithm can be applied to obtain an a priori evacuation network consisting of all the optimal routes from each node to the exit. At the time of the accident, the dynamic diffusion of fire can be simulated using Fire Dynamics Simulator (FDS) based on the preliminary information of the fire, so as to estimate the impact of the fire domain on each node of the network. Then, according to the fire dynamic diffusion data, the evacuation route planning is carried out by the improved A∗ algorithm applying the breadth-first search strategy, so as to determine the optimal route from the current node to the safety exit and to reduce the possibility of casualties due to the uncertainty of the fire during the evacuation. This model allows for both people’s safety and evacuation time to dynamically avoid fire-affected nodes and helps people to reach the safe area as soon as possible. Finally, the evacuation model is established according to the open-source cruise ship structure, and the evacuation process of people under the dynamic spread of cruise ship fire is simulated. The results show that the route planning method proposed in this research works out well in evacuating mass people, which can effectively reduce the evacuation time and improve the safety of the evacuation process.

An animal dynamic migration optimization method for directional association rule mining

In the area of association rule mining, many optimization algorithms have been proposed to improve the computational efficiency of rule mining or the quality and diversity of association rules. However, in real applications, since the user may have prior knowledge and research trends for some key items, the association rules containing key items are more valuable and meaningful for these users. This contributes to a new issue that association rules related to key items should be mined in a targeted manner. To solve this issue, this paper proposes a novel animal dynamic migration optimization (ADMO) method to realize directional rule mining as well as maintain high mining efficiency and high rule quality. Taking the support and confidence of frequent itemsets as input, the method first identifies valuable rules and then initializes and updates the animal population to search for the best animal. The support and confidence of the best animal are defined as threshold values to delete unnecessary rules and discover more key rules. During the optimization, the population size value is dynamically generated. The effectiveness and applicability of ADMO are validated on 11 open-source datasets and a real-world elevator case. The results reveal that the ADMO method has a faster mining speed and obtains more key rules than the ARM-PSO, ARM-AMO, ARM-MOPSO, ARM-WOA, and ARM-DE methods. In the elevator case, the association rule generated by ADMO can provide a higher success rate and accuracy for requirement transformation.

Adjoint sensitivity analysis method for dynamic optimization of multibody systems considering collision and friction

Adjoint sensitivity analysis method for dynamic optimization of multibody systems considering collision and friction

Dynamic Gate Delay Time Control of Si/SiC Hybrid Switch for Loss Minimization in Voltage Source Inverter

The voltage source inverter (VSI) based on the Si/SiC hybrid switch (HyS) offers a better cost/performance tradeoff than the full-SiC VSI design. However, the Si/SiC hybrid VSI bears huge power losses with fixed gate turn-off delay time. In this article, a novel dynamic optimal gate turn-off delay time control method of Si/SiC HyS-based VSI is proposed to achieve the maximum efficiency over a wide load range. The optimal gate turn-off delay time is obtained by a load current-based mathematic power loss model. A 20-kHz 5-kW prototype of the single-phase constant current (CC) VSI using a 1200-V/25-A insulated gate bipolar transistor (IGBT) and a 1200-V/12.5-A SiC MOSFET is built to validate the proposed method. In comparison with the fixed gate turn-off delay time control method, the proposed method achieves 8% total power loss reduction and 0.24% conversion efficiency improvement at 1.5-kW output power.

N-Propanol Dehydration with Distillation and Pervaporation: Experiments and Modelling.

This work is motivated by a fine chemical industry task where n-propanol should be separated from its aqueous mixture. To accomplish this problem, the pervaporation process intends to apply PERVAP™ 1201 type dehydration membranes and to obtain information about the water removal from an aqueous mixture of n-propanol. Different evaluation parameters (selectivities, separation factors, and total fluxes) were experimentally determined. First in the literature, this binary system’s Membrane Flash Index (MFLI) is also determined, confirming the efficiency of pervaporation against flash distillation. The experimental data from pervaporation measurements were evaluated with the improved model by Szilagyi and Toth. It has been established that the model can also be used for this case. The hybrid distillation and pervaporation system is rigorously modelled in a professional flowsheet environment (ChemCAD) and optimized with the dynamic programming optimization method. The distillation-based hybrid method without an extra added extractive agent for separating the n-propanol–water mixture has not yet been published in this computer program. The main objective functions of the hybrid method are the number of minimal theoretical stages and the minimal membrane area. It can be concluded that the process can dehydrate n-propanol with a purity of 99.9 percent.

Optimum design of sandwich composite structures under various dynamic loads using the equivalent static load method

Sandwich composite structures are high-performance materials with greater specific strength and specific stiffness properties, than widely used conventional metals. In this study, sandwich composite structures under dynamic loads were optimized using the equivalent static load method (ESLM). First, a finite element analysis was performed on the sandwich composite structure, using the high-order sandwich panel theory (HSAPT), considering the core material to be compressible. Further, it was optimized by applying the ESLM to three different sandwich composite structures under different types of dynamic loads. The results of the proposed method and conventional dynamic response optimization method were compared. The ESLM was observed to attain a convergence at a rate 1/24 times faster than the conventional method.

Dynamic Optimization Method of Wireless Network Routing Based on Deep Learning Strategy

Wireless sensor network is a network that integrates sensor technology, computer technology, information processing technology, and communication technology. This paper aims to study how to analyze and study the routing optimization of wireless sensor network based on deep learning and describe the neural network. This paper puts forward the problem of routing optimization, which is based on the dynamic programming of wireless sensor network, and then elaborates around its concept and related algorithms and designs and analyzes the case of wireless sensor network optimization. Through the comparative analysis of the five algorithms in computer simulation, although the average network delay performance of DPER reached 0.47 s, it could effectively prolong the life cycle of the network. The DPER algorithm not only improves the network life but also improves the network energy utilization rate, shortens the average path length of the network, and reduces the standard deviation of the remaining energy of the node.

An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

Dynamic optimization with flexible heat integration of a solar parabolic trough collector plant with thermal energy storage used for industrial process heat

There is an increasing need to reduce fossil fuel consumption used for industrial process heat to slow the effects of climate change. Using solar thermal heat is a viable way to replace fossil fuel use, but solar industrial process heat plants have limited implementation due to large upfront costs and inefficiencies from the inherent variability from solar energy. Having more flexible and optimized control over these plants can enable them to be more efficient. In this work, a solar industrial process heat plant with thermal energy storage that can flexibly collect and deliver heat to two industrial processes (flexible heat integration) is dynamically modeled with control setpoints found by a dynamic optimization method. The optimized case can increase the solar efficiency of the plant by 7.5% on average relative to a base case. Results show that it is best to collect heat at lower temperatures for all but ideal solar conditions, only medium to high quality heat should be stored in large quantities, and exchanging heat with a lower temperature heat sink is generally more efficient. The optimized case is able to reduce the levelized cost of heat of the plant to $31.83/MWhth compared to a base case value of $34.10/MWhth. The optimized case can reduce emissions by 22.2% compared to a plant which uses only natural gas. This work shows that dynamic optimization with flexible heat integration can be a cost-effective way to increase efficiency so that more of these types of plants can be implemented.

Dexterity Based Viscous Resistance Optimization of a Deep-Sea Manipulator

With persistent ocean exploration, the complexity of deep-sea intervention is gradually increasing. The deep-sea manipulator is the primary tool to complete complex intervention. The manipulator dexterity determines the complexity of the task it can perform. First, a dynamic dexterity evaluation method is proposed based on the kinematics and dynamics characteristics of the deep-sea manipulator. This method takes into account the dynamic torque boundary and Jacobian mapping constraint, which are different from terrestrial manipulators. The concepts of the dynamic dexterity ellipsoid and dynamic dexterity measure are defined. Second, the effect of viscosity resistance on dexterity is analyzed. The viscosity resistance is optimized by selecting the most suitable compensation oil. Finally, the methods of dynamic dexterity evaluation and viscosity resistance optimization are verified by a simulated deep-sea experiment. The method proposed in this paper effectively improves the dynamic dexterity of the deep-sea manipulator by optimizing the viscosity resistance. The proposed method can also be used to evaluate and improve the dexterity of other underwater manipulators.

Dynamic Optimization in Fast-Changing Environments via Offline Evolutionary Search

Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world problems. For its robustness with respect to noise, Evolutionary Algorithms (EAs) have been expected to have great potential for dynamic optimization. Many dynamic optimization methods such as diversity-driven methods, memory methods, and prediction methods have been proposed based on EAs to deal with environmental changes. However, they face difficulties in adapting to fast changes in dynamic optimization as EAs normally need quite a few fitness evaluations to find a near-optimum solution. To address this issue, this paper proposes a new framework of applying EAs in the context of dynamic optimization to deal with fast changing environments. We suggest that, instead of online evolving (searching) solutions for the ever-changing objective function, EAs are more suitable for acquiring an archive of solutions in an offline way, which could be adopted to construct a system to provide high-quality solutions efficiently in a dynamic environment. To be specific, we formulate the offline search as a static set-oriented optimization problem. Then, a set of solutions is obtained by an EA for this set-oriented optimization problem. After this, the obtained solution set is adopted to do fast adaptation to the corresponding dynamic optimization problem. The general framework is instantiated for continuous dynamic constrained optimization problems, and the empirical results show the potential of the proposed framework. The superiority of the framework is also verified on a dynamic vehicle routing problem with changing demands.

Dynamic Optimization Method of High-speed Solenoid Valve Parameters

The high-speed solenoid valve is the core component of the cotton foreign fiber machine, and its dynamic response characteristics of high-speed air flow at the inlet and outlet are the key to determine the accuracy of foreign fiber strike. In order to improve the dynamic response performance of high-speed solenoid valve under variable working conditions, based on the electromagnetic action mechanism of solenoid valve, the parameter analysis model of high-speed solenoid valve for outlet pressure optimization is established in COMSOL. Taking the inlet pressure and driving voltage of solenoid valve as the system optimization variables, the high-speed response law of outlet pressure rise lag time and outlet pressure change is obtained. The simulation result shows that when the driving voltage of solenoid valve changes from 21V to 27V, the lag time of outlet pressure rise decreases by 57.8%. When the inlet pressure increases from 2 bar to 7 bar, the lag time of outlet pressure rise decreases by 48.14% and the change rate of inlet and outlet differential pressure decreases by 8.4%. The dynamic optimization test system of high-speed solenoid valve parameters is built to verify the simulation results, and the change trend consistent with the simulation results is obtained.

Energy Efficient Communication Design in UAV Enabled WPCN Using Dome Packing Method in Water Distribution System

The water distribution system has deployed several low-power IoT devices on an uneven surface where battery power is a major concern. Therefore, this paper focuses on using a UAV-enabled wireless powered communication network capable of directing energy to a target location and using it for communication, thereby reducing battery issues. In this paper, a static optimization was applied to find the initial height values using 3D clustering and beamforming method and dynamic optimization using extremum seeking method was applied to find the optimized height. The optimized height values were calculated and Travelling Salesman Problem (TSP) was applied to create the trajectory of the UAV. The overall energy consumption of the UAV was minimized by integrating dynamic optimization and dome packing method, which can find an optimal position and trajectory where the UAV will be hovering to direct energy and collect data. Moreover, we also minimized the total flight time of the UAV.