Dynamic Optimization Method Research Articles

Kalman filtering-based decision threshold dynamic optimization in photonics-aided millimeter-wave systems

We propose a novel, to the best of our knowledge, decision threshold dynamic optimization (DTDO) method based on Kalman filtering to mitigate the nonlinear effect impacts of the time-domain jitter and saturation distortion on a bit error rate (BER) performance. This dynamic optimization method was validated in a photonics-aided W-band millimeter-wave system. Compared to nonlinear algorithms and clustering classification methods, DTDO offers lower complexity and superior dynamic tracking capabilities when optimizing the BER performance in photonics-aided systems. The four-level pulse amplitude modulation (PAM4) signal, after being transmitted over a 50-m W-band link, was equalized using a low-complexity filter, maintaining an exceptionally low BER. This approach effectively leverages the inherent low-complexity advantages of single-carrier transmission over the link.

Research on time-varying path optimization for multi-vehicle type fresh food logistics distribution considering energy consumption

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges.

A method for optimizing the capacity allocation of a photovoltaic-pumped hydro storage system in an abandoned coal mine

The enormous potential of the photovoltaic (PV) sector has aided in the faster attainment of China's “dual-carbon” aim. However, due to a scarcity of land resources and intermittent fluctuations in solar energy, it has been difficult to build large-scale PV bases, and existing PV systems also suffer from unstable power generation and seasonal fluctuations and are unable to be directly connected to the grid for use. However, abandoned mines with huge surface collapse zones and a large underground mining area offer a potential possibility for constructing photovoltaic-pumped hydro storage (PV-PHS) systems and limiting PV variations. Based on the abandoned mine pumbed hydro storage (AMPHS) potential assessment model and the optimized discrete wavelet decomposition algorithm, this study proposes a dynamic cycle optimization method for the PHS regulation capacity in an abandoned mine PV-PHS hybrid system. This approach is able to determine the optimal regulation capacity of AMPHS on a daily scale while limiting the volatility of the PV-PHS hybrid system. The method was applied to an abandoned mine PV-PHS hybrid system in Zaozhuang City, Shandong Province, and the optimal regulation capacity of the AMPHS was determined to be 88 MWh and 100 % elimination of PV generation volatility was achieved. This also reduces the economic cost by roughly 1.94 × 108 CNY. In addition, it is possible to store an average of 2.32 % of the PV power generated and to extend the power generation time by approximately 2.5 h per day. This research is critical for boosting the resource use of abandoned mines, and establishing the comprehensive usage model of new energy and abandoned mine resources.

A novel method for concurrent dynamic topology optimization of hierarchical hybrid structures

This paper proposes a feature-decoupled method for concurrent dynamic topology optimization of the Hierarchical Hybrid Structure (HHS) to minimize the steady-state dynamic response. First, a novel single-variable uniform multiphase material interpolation model is established based on the Gaussian function and normalization method, which achieves the decoupled description of the macroscopic topology, substructure topology, and the spatial distribution of the substructures for HHS. Second, by combining the extended multiscale finite element method (EMsFEM), which overcomes the limitations of the scale separation assumption and periodic boundary conditions in HHS response analysis, a concurrent dynamic topology optimization mathematical formulation for HHS is constructed. Finally, the sensitivity scheme is established based on the adjoint method, and the MMA algorithm was employed to update the model. Numerical examples verify the correctness and feasibility of the proposed method, demonstrate its advantages in solving HHS concurrent topology optimization problem compared to traditional methods, and explore the impact of the number of substructure types on the optimization results of HHS.

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

Portfolio Optimization Based on Rolling Window Using 5 Stocks

This paper aims to explore a method of constructing dynamically adjusted investment portfolios through the combination of rolling windows and mean-variance models. The construction of investment portfolios plays a crucial role in financial markets by effectively diversifying risks and achieving stable investment returns. This study analyzed adjusted closing price data from five stocks (AAPL, JPM, JNJ, XOM, and PG), sourced from Yahoo Finance. The rolling window method was employed to predict future stock prices and construct investment portfolios based on these predictions. This study calculated annualized average return and covariance matrix for each window. The mean-variance model and optimization algorithms were then used to determine the optimal weights for daily portfolio composition. The results demonstrate the high accuracy of the rolling window method in predicting short-term stock prices. Notably, Procter & Gamble (PG) and Johnson & Johnson (JNJ) exhibited the lowest prediction errors. Investment portfolios based on rolling window predictions performed well over a certain period. Nevertheless, the S&P 500 index did not show a substantial disadvantage compared to their long-term performance. This research provides dynamic portfolio optimization methods of practical value for investors in financial markets.

Optimize data association of point cloud to improve the quality of mapping and positioning

Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

Выбор элемента(ов) прототипа модели медицинской организации в целях оптимизации процессов регионального проекта с использованием методов динамического программирования.

Changes in federal legislation regulating the provision of patients suffering from cardiovascular diseases initiate a revision of the regional legislation, and inevitably raises questions of practical adaptation and optimization of the processes of ongoing projects, taking into account resources in the established management model in medical organizations. Finding elements of a prototype model of a medical organization implementing a regional project as objects of influence for theoretical proof of hypotheses using the dynamic programming method (callback function, recursion) is the task of optimizing the project processes considered in this article. The purposeof the research question was to optimize the processes of a regional project using the dynamic programming method, which is focused on finding the objective function F(i) – the number of ways to return the function. The subgoal was to justify the use of dynamic programming methods. Materials and methods. The information base for the study was federal and regional regulatory legal acts published in the legislative reference and legal systems of the Russian Federation, and electronic resources. The study was carried out using methods of dynamic programming, optimization, recursion, and modeling. Results. We did not find similar studies in the available literature. This study was carried out using the dynamic programming method, which is designed to solve the problem of finding the target function(s) F(i) of regional projects. The authors attempted to find a prototype of an operating procedure at the level of project executors, which is indivisible and elementary for subsequent optimization of the variable (non-constant) elements of the prototype of a medical organization model from the found objective function. Finding F(i) by categories of patients (object of influence) registered at the dispensary, obtained using the callback function F(0), F(1), F(2), F(3), …F(k); F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion) consists of decomposing the problem into smaller subproblems, finding the optimal solution to the subproblems recursively and using the resulting solution subtasks for constructing a solution to the original problem – optimizing an element of a prototype model of a medical organization, a regional project as a whole. Conclusion. The main tasks associated with project optimization are the tasks of minimizing the cost of the project for a given deadline for its implementation and minimizing (maximizing) the project completion period for a given marginal cost of its implementation. In search of a solution to optimization issues, the authors proposed finding F(i) by categories of patients (object of influence) registered as “D”, obtained using the callback function F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion).

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

Dynamic Optimization with Timing Risk

Timing risk refers to a situation in which the timing of an economically important event is unknown (risky) from the perspective of an economic decision maker. While this special class of dynamic stochastic control problems has many applications in economics, the methods used to solve them are not easily accessible within a single, comprehensive survey. We provide a survey of dynamic optimization methods under comprehensive assumptions about the nature of timing risk. We also relax the assumption of full information and summarize optimization with limited information, ambiguity, imperfect hedging, and dynamic inconsistency. Our goal is to provide a concise user guide for specialists and nonspecialists alike.

A Dynamic Topology Optimization Method for Tactical Edge Networks Based on Virtual Backbone Networks.

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method involves two phases: topology reconstruction and topology maintenance, determined by a network coverage threshold. When the coverage falls below the threshold, a virtual backbone network is established using a connected dominating set (CDS) and non-backbone node optimization strategies to reconstruct the network topology, quickly restore network connectivity, effectively improve network coverage, and optimize the network structure. When the coverage is above the threshold, a multi-CDS scheduling algorithm and slight position adjustments of non-backbone nodes are employed to maintain the network topology, further enhancing network coverage with minimal node movement. Simulations demonstrate that this method can improve coverage and optimize network structure under different scales of network failures. Under three large-scale failure operational scenarios where the network coverage threshold was set to 80%, the coverage was enhanced by 26.12%, 15.88%, and 13.36%, and in small-scale failures, the coverage was enhanced by 7.55%, 4.90% and 7.84%.

Dynamic optimization design of open-pit mine full-boundary slope considering uncertainty of rock mass strength

Rock and soil strength profoundly influences the stability of open-pit mine slopes. Traditional slope design, based on limited drilling data, often disregards inherent uncertainties. Effectively utilizing new sample information from mining operations poses a challenge, hindering dynamic and differentiated design for the entire perimeter slope. To address this, we propose a dynamic optimization method considering rock mass strength uncertainty for the entire perimeter slope. Our approach involves designing slope angles separately in different zones, while thoroughly considering decision-makers' preferences. Furthermore, we delegate the final adjustment authority of slope angles within the safety permissible range to on-site decision-makers. Compared to traditional methods, our dynamic design method incorporates rock mass strength uncertainty into slope evaluation while also accounting for decision-makers' safety and economic preferences. Through a case study of a specific open-pit mine, our proposed dynamic design method increases the overall slope angle by approximately 2.5°, fully accommodating the influence of on-site decision-making preferences on slope design. This article introduces a new method of dynamic optimization of open-pit mine slope based on simplified observation method, which improves the flexibility of decision-making and realizes the differential design of the whole surrounding slope.

Optimization of Desired Multiple Resonant Modes of Compliant Parallel Mechanism Using Specific Frequency Range and Targeted Ratios

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations.

Multi-objective robust dynamic pricing and operation strategy optimization for integrated energy system based on stackelberg game

The integrated energy system (IES) with hydrogen storage has become one of the most important developments in multi-energy coupling field, where the severe conflict of interests between different entities leads to great challenges to the economy and low-carbon operation. A multi-objective robust dynamic pricing and operation strategy optimization method based on the Stackelberg game is proposed for the hydrogen-containing energy storage (HES) IES. Firstly, the HES-IES trading framework is established based on the introduction of an integrated energy operator (IEO) and a load aggregator (LA). Secondly, a multi-objective robust Stackelberg game model is developed with the IEO as the leader and the LA as the follower, considering the minimization of operating costs and carbon emissions of the IEO and the minimization of integrated energy costs of the LA as the objectives. Finally, the compromise planning and the max-min fuzzy are addressed to solve the multi-objective model, which adopts the adaptive differential evolution (ADE) algorithm. In addition, the robust optimization (RO) with adjustable coefficients is employed to tackle uncertainties of source and load. The results show that this method can effectively balance the operating costs and carbon emissions of the system, improve the benefit of the IEO, reduce the costs of the LA, and avoid the uncertainty risk. Compared with traditional algorithms, the ADE algorithm has significant advantages in the number of iterations and solving time. In summary, the multi-objective robust dynamic pricing and operation strategy optimization proposed in this paper could effectively achieve the benefit balance between the IEO and the LA, also further improve the economy and robustness of the system and reduce carbon emissions.

Dynamic position optimization of submerged array square jet cooling in confined space for chip thermal dissipation

Jet position has a significant influence on the heat transfer characteristics of the chip. Therefore, it is necessary to optimize the jet position to improve its operating performance. Besides, traditional and typical optimization method is to establish abundant experimental/numerical models, which is very complicated and time-consuming, and the accuracy and effectiveness of results still need to be verified. Hence, a dynamic optimization method is proposed in this study and the jet position is directly controlled during the simulation process based on dynamic regulation of user-defined functions, which is directly related to the real-time physical quantities and the user-defined optimization strategy. Results show that compared to traditional jet position, the maximum temperature and temperature dispersion for the optimal jet position decrease by an average of 25.20 °C and 69.88 %, respectively. Furthermore, the optimal jet position efficiently achieves the balance between thermal performance and power consumption. To sum up, compared with manual optimization method, the dynamic optimization method proposed in this study only needs to establish one model and simulate it once to obtain the optimized result. This process is very convenient, high-efficiency and less time-consuming, which will be especially beneficial to effectively enhance the performance of the chip thermal dissipation.

The Bisection Method and the Accelerated Dynamical Mean Value Optimization Method in Comparison of Different Topology Optimization Approaches

The Bisection Method and the Accelerated Dynamical Mean Value Optimization Method in Comparison of Different Topology Optimization Approaches

Research on dynamic balance optimization method of flexible rotor based on GWO

Abstract Aiming at the problem that the dynamic balance process of a flexible rotor needs to start and stop frequently to add test weight, which is time-consuming and labor-consuming, and the balance accuracy is difficult to guarantee, a dynamic balance optimization method of flexible rotor based on grey wolf optimizer (GWO) is proposed. In this paper, a virtual prototype model is established based on a power turbine rotor for a certain type of turboshaft engine, and a rotor test platform is built. The transfer function is used to find the relationship between unbalance and vibration response, and the equilibrium equations are established to solve the problem. In the process of solving the problem that the equilibrium equations are mostly contradictory, GWO is used to solve the contradictory equations to obtain the optimal counterweight scheme at the full working speed of the rotor. The results show that the method proposed in this paper eliminates the cumbersome trial weight process of traditional dynamic balance. The average vibration reduction rate of this method is 33.8% at 5100 r min−1, 5400 r min−1 and 5700 r min−1, which is better than the conventional field dynamic balance. The work of this paper can improve the efficiency and accuracy of flexible rotor dynamic balance and provide technical reference for the vibration control of aero-engine.

A PCM-based train post-derailment dynamics model and its application

Understanding post-derailment behaviours is critical and fundamental to preventing derailment escalation and developing containment methods, thereby avoiding catastrophic consequences in a derailment accident. However, modelling and simulating the train post-derailment behaviours is a significant challenge because of the complexity and unpredictability of the train post-derailment contact interaction. This paper presents a novel post-derailment contact model built upon the Polygon Contact Model (PCM), aimed at enabling accurate and efficient simulation of train post-derailment contact interactions. Expanding upon the PCM-based post-derailment contact model, we introduce a framework for simulating train post-derailment behaviours and utilise it to develop a train post-derailment dynamics model that considers complex contact interactions between the bogie and the track structure. Additionally, we introduce a dynamic optimisation method to determine the essential parameters for this model. The feasibility and accuracy of the PCM-based train post-derailment dynamics model have been validated through a comparative study with an actual derailment test. Subsequently, the verified model is used to reproduce different post-derailment scenarios with varying combinations of vehicles and tracks, yielding valuable insight and understanding into the train post-derailment behaviour.

Dynamic and static fusion mechanisms of infrared and visible images

This paper propose a dynamic fusion mechanism of infrared and visible images, named DIFM, capable of solving the static fusion optimization problem. The DIFM correlates the image fusion quality with the image restoration quality to construct a unified optimization loss function. According to the DIFM, a dynamic image fusion network of infrared and visible images is constructed and is therefore denoted with DF-Net. Specifically, the DF-Net comprises two modules, i.e., the dynamic fusion module (DFM) and the self-learning dynamic restoration module (SLDRM). In order to solve the static fusion problem of existing methods, the DFM is proposed to learn the fusion weight dynamically. Specifically, the DFM comprises a classification module (CM) and an image fusion module (IFM), which determine whether and how to fuse source images. In addition, a unified fusion loss function is introduced to obtain more hidden features of infrared and visible images in complex environments. Therefore, the stumbling block of deep learning in image fusion, i.e., static fusion, is significantly mitigated. Extensive experiments demonstrate that the dynamic fusion optimization method neatly outperforms the state-of-the-art methods in most metrics.

Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning

Data-driven soft sensors have become popular tools for estimating critical quality variables in the process industry. However, in practical applications, it is very common that the unlabeled data are abundant but the labeled data are scarce, which poses a great challenge for developing high-performance data-based soft sensors. Thus, a dynamic dimensionality reduction-assisted large-scale pseudo label optimization method (DDR-LSPLO) is proposed for achieving sample expansion. This method repeatedly converts the LSPLO issue into a reduced-dimension pseudo label optimization problem with the low-confidence pseudo labels as new decision variables during the evolutionary optimization process. Meanwhile, to tackle the sample imbalance problem resulting from the inclusion of large-scale pseudo-labeled samples, a sample expansion and weighting-based quality-relevant autoencoder (SEWQAE) is developed for semi-supervised soft sensor modeling. The effectiveness and superiority of the proposed DDR-LSPLO and SEWQAE methods are verified through an industrial chlortetracycline (CTC) fermentation process and a simulated Tennessee Eastman (TE) chemical process.