Collaborative Optimization Research Articles

Collaborative Optimization of English Online Teaching Informatization Based on Intelligent Multimedia Image Technology

With the rapid development of computer technology and the deepening of educational concepts, the traditional English teaching methods have also changed. As a new teaching method, online teaching has received extensive attention. Online teaching has introduced intelligent multimedia image technology in English teaching, which has transformed the essence of English teaching by organically integrating traditional teaching and multimedia teaching. With the rapid growth of Internet and intelligent multimedia image technology, the number of digital images is growing exponentially. How to extract the required image from the large amount of multimedia image data has been an important topic at present. In this case, intelligent multimedia image technology emerges as the times require, which extracts the features of the image from the content of the image itself, and then retrieves it through the features. This article proposed a retrieval and classification method based on intelligent multimedia image technology. Among them, Support Vector Machine (SVM) was used to classify multimedia resources, which had a good effect. The results of this experiment showed that before the experiment, the listening score of the multimedia group was 65.5, and the speaking score was 59.7. After the experiment, the listening score of the multimedia group was 79.8, and the speaking score was 80.6. It can be found that the performance of the multimedia group has been greatly improved after the experiment, which shows that online English teaching based on intelligent multimedia image technology is more conducive to the improvement of students’ academic performance.

A double-Q network collaborative multi-objective optimization algorithm for precast scheduling with curing constraints

Precast scheduling is a special case of optimization problem, in which the curing stage requires parallel processing of a batch of jobs. In this study, distributed flexible job shop scheduling with crane transportation, fuzzy processing, and curing constraints is modeled for precast scheduling. Three objectives are considered simultaneously, namely, fuzzy makespan, energy consumption, and economic cost. To solve this complex problem, a bi-level collaborative multi-objective optimization evolution algorithm (BCMOEA) is developed. First, a double-Q network is designed considering different groups of features to generate valuable solutions for the second-level components. Then, a dynamic-adjusted reference point set is embedded to divide the current population into three sub-populations. Furthermore, a collaboration mechanism is developed to learn different knowledge from these sub-populations to balance the convergence and diversity abilities. Moreover, a knowledge driven curing batching heuristic is designed to improve the fuzzy makespan. Finally, a set of instances generated based on the realistic precast process is tested, and detailed comparisons with the state-of-the-art algorithms show the competitive performances of the proposed algorithm.

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

In order to achieve carbon peak and neutrality goals, many low-carbon operations are implemented in ports. Integrated energy systems that consist of port electricity and cooling loads, wind and PV energy devices, energy storage, and clean fuels are considered as a future technology. In addition, ports are important hubs for the global economy and trade; logistics optimization is also part of their objective, and most port facilities have complex logistics. This article proposes an energy–logistics collaborative optimization method to fully tap the potential of port-integrated energy systems. A logistics–energy system model is established by deeply examining the operational characteristics of logistics systems and their corresponding energy consumption patterns, considering ships’ operational statuses, quay crane distribution constraints, and power balances. To better represent the ship–energy–logistics optimization problem, a hybrid system modeling technique is employed. The case of Shanghai Port is studied; the results show that costs can be reduced by 3.27% compared to the traditional optimization method, and a sensitivity analysis demonstrates the robustness of the proposed method.

Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning

Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning

Parametric optimization scheme of energy dissipation and shock absorption for prefabricated concrete frame

The prefabricated concrete frame structure system has advantages such as short construction period and good seismic performance, but its deformation and energy dissipation capacity are poor under earthquake action, making it prone to damage. By improving the analysis and simulation functions of existing finite element analysis for prefabricated structures, the engineering applicability of the analysis algorithm has been improved. Then, a finite element model has been established for collaborative optimization, and a parameterized optimization scheme that meets the seismic reduction requirements has been obtained. The results show that the optimization method proposed in the study has a better effect in seeking the minimum cost, and meets the design requirements of the specification. The optimization scheme of prefabricated concrete frames designed by the research institute based on finite element analysis can efficiently optimize various parameters, greatly improving the structure energy dissipation and seismic performance.

Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

AbstractIn order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology

Heat energy storage technology plays a significant role in energy systems, and the various technological solutions brought about by digitalization are especially valuable in the field of energy storage. This article proposes an innovative model based on digital twin technology to solve the supply–demand mismatch problem in geothermal heating systems. This model achieves multi-objective optimization of comprehensive cost, geothermal energy utilization rate, and carbon emission by constructing a heat storage geothermal heating system. Digital twin technology integrates data and information models of public buildings and facilitates their sharing and transmission throughout the entire lifecycle of the geothermal heating system. Initially, the proposed method employs a machine learning-based approach to accurately predict heating demand. Subsequently, the operation of the heat storage water tank and heat pump units is optimized to resolve difficulties in matching energy supply and demand. Finally, the method takes full advantage of time-of-use electricity pricing policies to reduce costs. The data utilized were collected from an office building in China over a period of six months. Experimental results demonstrate that: (1) In terms of predicting heating demand, the improved neural network proposed in this study achieved a prediction accuracy of 98%, which is a 10% improvement over comparative algorithms. Additionally, the experimental comparison of four types of errors showed that the machine learning method proposed had smaller errors across the board. (2) The method realized collaborative multi-objective optimization, and in five scenarios, the comprehensive performance index increased by up to 38.03% compared to the benchmark system. This indicates that intelligent technology is an effective means of enhancing the energy sustainability of geothermal heating systems, and the use of geothermal energy as a clean energy source effectively addresses issues related to the storage, utilization, management, and energy conservation of buildings.

Collaborative multi‐scale optimization of molding processes for thermosetting/quartz fiber composites based on balancing between efficiencies and mechanical properties

AbstractIn terms of the interplay between manufacturing cost and quality during the molding of fiber reinforced thermosetting composites, this study proposes a multi‐scale collaborative optimization strategy aimed at shortening molding time and reducing the diminishing defects generated during the process. Macro‐scale thermochemical and micro‐scale thermomechanical calculations are conducted on the composite molding to analyze the origins of temperature gradients and residual stresses. Then, the curing cycle, temperature gradient in the macro‐scale model, and residual stresses in the micro‐scale model are optimized by combining finite element analysis with the Non‐dominated Sorting Genetic Algorithm II. Several curing processes within the Pareto front were selected, and their impact on compressive performance was assessed through experimental analysis. Compared to the standard curing process (SCP), the optimal curing process exhibited a 41.4% reduction in maximum temperature gradient (∆Tmax), a 25.2% decrease in effective residual stress , and a 20.0% increase in compressive strength. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites.Highlights Investigated of interactions among components in composites molding. Optimization of composite molding process with Non‐dominated Sorting Genetic Algorithm II and finite element. Examined the impact of composite molding process on compressive performance.

Co-optimization method for injection strategy of underground natural gas storage integrating aboveground and underground parts

Underground natural gas storage (UNGS) is an important infrastructure to overcome the imbalance between supply and demand of natural gas. During the gas injection period, gas is compressed aboveground and then transported to injection wells for injection into the underground reservoir. Reasonably formulating the compressor operation scheme and injection scheduling scheme is a crucial step. However, “Compressor-pipeline-injection well-reservoir" constitutes a synergistic whole. More efficient and practical operation schemes can only be proposed through the establishment of an optimization method that coordinates the entire gas injection process. A novel two-layer multi-objective collaborative optimization method is proposed to optimize the monthly operation scheme during the injection period. The upper-layer model focuses on optimizing injection volume allocation to minimize reservoir pressure deviation and total compressor energy consumption. The under-layer model optimizes the compressor operation scheme by considering the multi-stage compression (MC) and clearance adjustment (CA) equipped in reciprocating compressors. The synergistic relationship during the injection process is integrated into the traditional model through the interlayer iteration method. Furthermore, a high accuracy and efficiency proxy model based on machine learning algorithms (ML) is developed to predict compressor performance. The results of the case study indicate that, compared with traditional optimization method and the two-stage co-optimization method, the proposed method achieves an average energy consumption reduction of 9.54 × 106 kWh (10.79%) and 2.12 × 106 kWh (2.4%) respectively. The effective utilization of CA and MC can significantly alleviate the deviations between displacement and processing requirements, eliminating 22,120.5 × 104 Nm3(85.62%). The two-layer co-optimization method effectively integrates the aboveground and underground parts of UNGS, providing a solution for safe and efficient operation during the injection period.

Carbon Footprint of Spent Ternary Lithium-Ion Battery Waste Recycling

Road transport is the primary source of greenhouse gas emissions in China's transportation field. As an important means to achieve the "double carbon" goal in the transportation field, the new energy automobile industry will face a large number of power battery scrapping in the future. In order to quantitatively assess the carbon emission reduction benefits generated by the spent ternary lithium-ion battery waste recycling industry, the carbon footprint accounting model of spent ternary lithium-ion battery waste recycling and utilization was constructed from the life cycle perspective. By optimizing the power structure and transportation structure, the carbon emission reduction potential of spent ternary lithium-ion battery waste recycling was predicted and evaluated. In addition, the uncertainty analysis was conducted using the propagation of uncertainty equation to ensure the reliability and effectiveness of the carbon footprint results. The results showed that the current carbon footprint of Chinese enterprises using wet technology to recover 1 kg waste lithium batteries was -2 760.90 g (directional recycling process) and -3 752.78 g (recycling process), and the uncertainty of the carbon footprint was 16 % (directional recycling process) and 15 % (recycling process), respectively. From the analysis of carbon emission contribution, the regenerated product stage was the primary source of carbon reduction in the wet recycling and utilization of waste ternary lithium batteries, whereas the battery acquisition, disassembly, and end treatment stages were the main sources of carbon increase. Compared to optimizing the transportation structure, optimizing the power structure could effectively achieve greater carbon emission reduction potential. Under the collaborative optimization scenario, compared to that before optimization, 14 %-19 % carbon emission reduction could be achieved. Compared with native products, the directional circulation process and recycling process could achieve 9 % and 11 % emission reduction potential, respectively.

Corrugation parameters and mechanical performance of corrugated shells under the same weight and volume

Cylindrical shells are easy to buckle under external pressure and need to be strengthened before they can be used in engineering. At present, the cylindrical shells are mainly enhanced by welding stiffeners inside them. However, for underwater pressure vessels, it is important to reduce the welding area. Some scholars have proposed various forms of corrugated shells, such as longitudinally corrugated shells, and studied the buckling behavior of corrugated shells under hydrostatic pressure. However, few studies have been conducted on such shells under the same weight conditions. Because an increase in the height of the corrugation necessarily leads to an increase in weight. So this reduces the comparability between the corrugated shells. The mechanism of non-uniform wall thickness on the improvement of shell material utilization is not explained. It is worth exploring how to achieve the best buckling performance under the limited weight. In this paper, the shape parameters of corrugations are explored, including the number, height and width of corrugations, and the coupling between them. The mechanical behavior of corrugated shell is studied from theory, FEA and experiment. In addition to hydrostatic pressure, the collapse of corrugated shells under concentrated pressure is also studied and comparative tests are carried out. The whole process of the collapse of the corrugated shell under concentrated pressure can be directly observed, and this is also an important performance index. In order to ensure the dimensional accuracy of the test samples, 3D printing technology was used for processing. In this paper, higher corrugations are not always better for the same weight. Because corrugation troughs tend to crack before buckling. This is like the short board effect, which requires collaborative optimization of peak and trough thickness.

Optimization of ecological network function and structure by coupling spatial operators and biomimetic intelligent algorithm

Ecological networks (ENs) can maintain regional ecological security and reconcile the contradiction between development and conservation. Optimization of ENs is crucial for promoting sustainable development and human well-being. Although various valuable methods have been explored to optimize ENs, collaborative optimization research on the EN function and structure through a quantitative simulation at the patch level is yet to be conducted. This study integrated spatial function and structural operators into a multi-type ant colony optimization model and constructed a high-performance land-use conversion framework for EN optimization. Results showed that: (1) the optimized ecological source exhibits heightened functionality and improved connectivity, resulting in a more stable and rational network structure; (2) the proposed ecological nodes emergence mechanism prevents fragmentation of local ENs and improves the overall structural integrity and resilience; and (3) the introduction of graphics processing unit (GPU) parallelization improves the efficiency of the model at city level by 13.49 times, resolving the paradox between the computational efficiency of high-precision land-use simulation and the construction of large-scale ENs. This study provides methodological support for patch-level landscape planning and reference for formulating reasonable ecological restoration strategies.

Construction and Application of Energy Footprint Model for Digital Twin Workshop Oriented to Low-Carbon Operation.

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects.

DivideMerge: A multi-vessel optimization approach for cooperative operation and maintenance scheduling in offshore wind farm

The optimization of maintenance scheduling and routing in offshore wind farms is crucial for the intelligent operation and maintenance of offshore wind energy. It involves determining the optimal timing, assigning vessels, planning routes, and efficiently completing maintenance tasks with maximum efficiency and minimal cost. Traditional branch-and-bound search methods, often used in multi-vessel cooperative maintenance scheduling models, face limitations in finding optimal solutions efficiently, while classic heuristic methods may compromise on global optimality. This paper introduces DivideMerge, a high-performance hybrid algorithm that synergizes heuristic and exact algorithms to address large-scale multi-vessel cooperative maintenance scheduling challenges in offshore wind farms. Initially, a maintenance task constraint decomposition heuristic method is utilized to break down the collaborative optimization scheduling problem into individual vessel scheduling sub-problems, ensuring adherence to the constraints of the original problem. Subsequently, the CP-SAT solver is employed to sequentially solve these sub-problems rapidly. The solutions for individual vessel scheduling are then merged to form a comprehensive solution for the multi-vessel cooperative maintenance scheduling problem. Computational results validate the effectiveness and robustness of DivideMerge, demonstrating a solution speed nearly 1000 times faster than the commercial Gurobi solver, thus offering a significant advancement in the field of offshore wind farm maintenance optimization.

A two-stage collaborative optimization method for emergency repair station planning and recovery strategy of cyber-physical-transportation networks

In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in this paper to enhance the resilience under extreme scenarios. In the first stage, based on the principle of hierarchical portioning, the responsible area for the emergency repair station is divided by the community theory, and the emergency repair station planning model is developed. In the second stage, the time sequence of repairing the faulty components is first established based on the graph theory. Then, based on the spatial distribution of adjacent time-series faulty components on the transportation network, a faulty component restoration time model is established to accurately reflect the faulty restoration time. On this basis, the functional coupling effects of economic dispatch control and substation automation system failures on load recovery are analyzed to establish a load recovery model. The case studies of the IEEE RTS-79 test system demonstrate that, compared with traditional independent recovery model, the model proposed in this paper performs better in enhancing resilience under extreme scenarios.

Call for Papers: Special Issue on Cross-Layer and Collaborative Optimization Techniques in Space-Air-Ground-Sea Integrated Networks

Call for Papers: Special Issue on Cross-Layer and Collaborative Optimization Techniques in Space-Air-Ground-Sea Integrated Networks

Research on low-carbon optimization methods for regional energy interconnection systems considering multi-energy coupling characteristics

Abstract With the increasing application of multi-energy cogeneration, distributed renewable power generation, and multi-energy storage in various multi-energy systems, their efficient collaborative optimization has become the key to further improving emissions. This article utilizes energy hubs to integrate energy equipment with multi-energy coupling and multi-source supply characteristics and constructs a regional interconnected energy system that considers multi-energy coupling characteristics. A multi-objective optimization model for the above-mentioned energy system is established, including economic, low-carbon, and safe aspects such as operating costs, process emissions, and operational safety, and the raccoon optimization algorithm is applied to solve the model. The experimental results show that compared with classical scheduling algorithms, the proposed method can effectively reduce carbon emissions at a certain operating cost.

S[formula omitted]: Self-supervised learning with sparse-dense sampling

Self-supervised visual representation learning (SSL) attempts to extract significant features from unlabeled datasets, alleviating the necessity for labor-intensive and time-consuming manual labeling processes. However, existing contrastive learning-based methods typically suffer from the underutilization of datasets, consume significant computational resources, and employ longer training epochs or large batch sizes. In this study, we propose a novel method aimed at optimizing self-supervised learning that integrates the advantages of sparse-dense sampling and collaborative optimization, thereby significantly improving the performance of downstream tasks. Specifically, sparse-dense sampling primarily focuses on high-level semantic features, while leveraging the spatial structure relationship provided by the unlabeled dataset to ensure the incorporation of low-level texture features to improve data utilization. Besides, collaborative optimization, including contrastive and location tasks, further enhances the model’s ability to perceive features of different dimensions, thereby improving its utilization of features in the embedding space. Furthermore, the combination of sparse-dense sampling and collaborative optimization strategies can reduce computational consumption while improving performance. Extensive experiments demonstrate that the proposed method effectively reduces the computational requirements while delivering favorable results. The codes and model weights will be available at https://github.com/AI-TYQ/S4.