Multi-objective Optimization Theory Research Articles

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

Efficiency Improvement Mechanism of Smart Logistics Technology in Rural E-commerce Logistics and Distribution

Abstract This paper addresses the problems existing in the current two-way logistics and distribution model of rural e-commerce and proposes the study of rural e-commerce distribution efficiency with the technical support of intelligent logistics. Based on the knowledge of multi-objective optimization theory, the maximization of distribution efficiency of logistics enterprises and the maximization of demand coverage as the objective function, while the constraints are vehicle load, service time, path constraints, and finally, complete the construction of the multi-objective optimization model of rural e-commerce logistics and distribution efficiency. According to the NSGA-II algorithm with low computational complexity, fast operation speed, and good convergence, the NSGA-II algorithm is used to solve the constructed model, and then the optimal solution of rural e-commerce logistics distribution efficiency is derived. With regional logistics as the research subject, a multi-objective optimization model for rural e-commerce logistics distribution efficiency supported by intelligent logistics technology is simulated and analyzed. The data show that compared with other algorithms, the NSGAII algorithm has a smoother learning curve oscillation, while the final result of convergence (-1.473), the objective function is smaller (20.42), and the optimal total transportation time is smaller (609.22s). Finally, in order to solve the current rural e-commerce logistics and distribution problems in part, from the improvement of rural e-commerce logistics and distribution system, strengthen the rural logistics infrastructure construction of the two aspects of rural e-commerce logistics and distribution efficiency improvement strategy.

Research on rural landscape garden planning and plantscape construction method based on digital twin technology under rural revitalization strategy

Abstract This paper integrates the landscape information model, designs the digital twin rural landscape architecture, and analyzes the advantages of the digital twin rural landscape model. The practical application of digital twin technology is examined from the planning and design stage to the construction stage. Considering the actual construction site layout and the consumption of construction materials, the NSGA-II algorithm is selected to optimize the dynamic layout of temporary facilities by combining multi-objective optimization theory. Using the digital twin to drive the dynamic layout of the construction site, a digital twin-driven construction site layout mechanism is proposed in combination with BIM technology. The actual construction scheduling tasks are used to analyze the feasibility of the construction site layout scheme generated using digital twin technology. Analyze the construction of rural landscape gardens planned using digital twin technology in terms of ecological, social, and economic benefits. The convergence minimum is achieved at 0.15 and 0.09 when comparing the number of iterations between the initialized layout and the digital twin-driven layout. The corresponding scheme reaches its optimal state during the construction stage and can provide the most suitable site arrangement scheme during that stage. The rural landscape garden planned by the digital twin technology can generate an economic return value of 71.756 yuan/m² per year, which can further promote rural revitalization.

A Multi-Objective Optimization-Based Transmission Design for V2V Communication Networks

Data delivery in vehicular networks confronts unique challenges due to vehicle movement and stringent quality-of-service (QoS) demands. This paper investigates the efficient transmission design problem in a typical vehicle-to-vehicle (V2V) communication network, where multiple source-destination pairs with different types of message transmission requirements coexist and the network environment dynamically changes. A sequential transmission decision framework based on the multi-objective optimization (MOO) theory is proposed to maximize the performance of each link, while ensuring the QoS of different messages. We integrate the Lyapunov optimization theory with the weighted Tchebycheff method, as well as other mathematical optimization tools, to transform the original problem into a solvable MOO problem at each time slot. Transmission actions can be determined continuously according to the environment change and time-related design goals. The effectiveness and efficiency of the proposed method are demonstrated through extensive simulation experiments.

Yaw system restart strategy optimization of wind turbines in mountain wind farms based on operational data mining and multi-objective optimization

The wind resources of mountain wind farms are affected by more complex terrain than are found at flat wind farms. Wind turbine (WT) failures caused by frequent operation of the yaw system occur often in mountain wind farms, resulting in significantly reduced economic benefits over the WT life cycle. Considering the problem of frequent yaw motions of WTs in mountain wind farms, this study introduces multi-objective optimization theory into the yaw system restart strategy optimization of WTs for the first time and proposes an optimized yaw system restart strategy with wind speed segmentation in the wind speed region below the rated wind speed. The yaw behavior of mountain wind farms in southern China is analyzed from multiple perspectives; a segmentation scheme of wind speed intervals for yaw control was determined from the results. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the control parameters for each sub-interval with the goals of minimizing yaw operation time and maximizing energy generation. A set of Pareto solutions is obtained and evaluated with the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) method to produce the optimal solution. The simulation results indicated that after the WT adopts the optimized yaw system restart strategy in the wind speed region below the rated wind speed, the number of yaw motions is decreased by 82.9%, with only a slight decrease in energy generation. The optimization method proposed in this study can significantly reduce yaw operation time with minimal loss of energy generation, which provides a new approach to balancing the relationship between reducing the WT failure rate and ensuring power generation efficiency and is expected to significantly improve the economic benefits to wind farms.

Integrated energy carbon emission monitoring and digital management system for smart cities

In recent years, although China’s economy has continued to grow, the environmental impact is greatly affected by the use of primary energy, such as global warming, which has become more and more serious. Under the background of energy conservation and emission reduction, China’s emission reduction pressure is very great. In this paper, an online monitoring system for carbon emissions is developed for real-time monitoring of carbon emissions, and the ant colony algorithm is used to perform multi-objective optimization based on “construction period-cost-carbon emissions.” Through the organic integration of wireless sensors, communication networks, cloud servers, and mobile devices, a real-time monitoring system for carbon emissions has been developed, which can monitor and visualize the carbon emissions generated by major machinery on site in real time. At the same time, the resource consumption of each process in different modes is sorted out, and the multi-objective optimization problem of “construction period-cost-carbon emission” is designed to seek the optimal solution by combining the multi-objective optimization theory. In this paper, the developed real-time monitoring system is applied in the actual field, the stability and practicability of the system are verified, and the process-related data is obtained by combining the monitoring system and field investigation. The experimental results show that the relative deviations of the two units are consistent, fluctuating between 0.54% and 6.14%, and the overall deviations are 3.61% and 3.63%, respectively. Therefore, the online carbon emission monitoring system has stable data and high accuracy. By comparing the data trends of the online monitoring method and the emission factor method, it is found that the two trends are consistent, which verifies the applicability of the online monitoring method in the field of carbon emission monitoring.

A game-theoretical constructive approach for the multi-objective frequency assignment problem

This study presents the game modeling of the multi-objective frequency assignment problem (FAP) in cellular networks considering two conflicting objectives: interference and separation costs, while respecting separation constraints, and thus improving the QoS for end users. Two types of cooperative games are suggested depending on the TRX and frequency selection strategies each using two players, one for each considered objective. A feasible solution consisting of a frequency assignment plan is built during the constructive process. At each step of the game, a player assigns a frequency to a TRX according to a given strategy. The proposed solution being part of both multi-objective optimization (MOO) and game theory (GT), we used the most important measures in these two fields: the hypervolume (HV) and the Nash value, to evaluate the performance of our solutions to able to compare our results with those of these two communities: MOO and GT. The proposed games achieve a good trade-off between the two considered objectives corroborated by tests on two real-world instances, Denver and Seattle, demonstrating the performance of the proposed approach in solving the problem. Furthermore, the proposed performance evaluation method is innovative and also generic, since it can be adapted to any algorithm based on both GT and MOO, taking into account these two aspects.

Comprehensive Utilization Model of Yarlung Zangbo River Water Resources Based on Discrete Multi-objective Optimization Model

The Yarlung Zangbo River is rich in water resources, which is of great significance to the comprehensive utilization of water resources. The reasonable development of the hydropower resources of the Yarlung Zangbo River is realized, through the water diversion project and the establishment of the trapezoidal hydropower station. A comprehensive utilization model of hydropower resources optimized by discrete objectives is constructed, based on the discrete multi-objective optimization theory. It combined with the reading of geographic data by GPS and Matlab. The number and orientation of the optimal construction of hydropower stations and the proportion of the water transfer volume of the water diversion project are obtained, combining the cost of hydropower station construction and water diversion project implementation and the benefits brought by hydropower resources.

An integrated decision support system for the urban food-water-energy nexus: Methodology, modification, and model formulation

Making cities more sustainable relies on opportunities to optimally integrate and manage food, water, and energy resources, among other essential requirements for the thriving of every society, in a synergistic manner. By means of decision support tools and the development of policy scenarios, cities can better understand how sustainability may be achieved by the optimal integration of the natural resources. Although increasingly employed, the need remains for an integrative decision-making methodology and tool that supports the incorporation of food, water, and energy sectors and the corresponding environmental and social footprints into a general framework, and quantitatively investigating the complicated synergies to optimize nexus strategies from a holistic point of view. This research develops an integrated decision-support system by means of a spatial optimization game model that searches for optimal resource management solutions through a cooperative scenario-building environment. The design of the proposed system relies on an innovative combination of methods capable of navigating decision-making through complex systems modeling and planning. This includes multi-objective optimization and cooperative game theory in the frame of a spatial serious gaming environment for real-world implementation. Relying on such an algorithmic framework, this research provides the foundation for a spatial serious game that enables forecasting the impact of policy interventions based on socio-economic drivers of the demand for the resources, environmental carrying capacity, land management, and primary climate change drivers. The outcomes serve as strategic guidelines for policymakers and encourage effective decision-making related to maximizing socio-economic targets and minimizing environmental burdens.

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Double Auction Offloading for Energy and Cost Efficient Wireless Networks

Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically with changing roles leading to a novel market model, where innovative challenges are introduced. In this paper, a novel resource sharing and offloading algorithm is introduced based on a double auction mechanism where MNOs and third parties buy and sell capacity and roam their traffic among each other. For low traffic periods, Base Stations (BSs) and Small Cells (SCs) can even be switched off in order to gain even more in energy and cost. Due to the complexity of the scenario, we adopt the multi-objective optimization theory to capture the conflicting interests of the participating entities and we design an iterative double auction algorithm that ensures the efficient operation of the market. Additionally, the selection of the appropriate time periods to apply the proposed algorithm is of great importance. Thus, we propose a machine learning technique for traffic load prediction and for the selection of the most effective time periods to offload traffic and switch off the Base Stations. Analytical and experimental results are presented to assess the performance of the algorithm.

Research on the Siting Model of Emergency Centers in a Chemical Industry Park to Prevent the Domino Effect

A chemical industry park (CIP) has a wide variety of hazardous chemicals, and once an accident occurs, the level of danger increases geometrically, while the domino effect may bring devastating consequences. To improve the emergency rescue capability of a chemical park and prevent the domino effect, a certain number of emergency centers are built at sites near the park for the purpose of rapid emergency rescue and deployment of emergency supplies. Based on this, in our study, a siting model of the emergency center of the chemical park, which aims to prevent the domino effect, was constructed by considering the timeliness and safety, while adopting the prevention of the domino effect as a constraint. The NSGA-II algorithm is used to solve the siting model, and the CPLEX method is used for the comparison. This study combines the prevention of the domino effect with multi-objective optimization theory, which has a good and simple applicability for solving the considered problem and can obtain solutions in line with science and reality. It also adds the risk radius of the demand point based on the traditional siting model and proposes a model that combines the risk and distance to reduce the risk of accidents across the whole region. Finally, the model is applied to a chemical park in China for an arithmetic analysis to provide decision makers with a targeted reference base for the siting of an emergency center. The experimental results show that the NSGA-II algorithm can effectively solve the model of the emergency center in the chemical park and outperforms the results obtained from the CPLEX solution in terms of its cost and safety.

Effects of different injection timing on the performance, combustion and emission characteristics of diesel/ethanol/n-butanol blended diesel engine based on multi-objective optimization theory

In this work, a three-dimensional model of an engine cylinder was devolved by the commercial simulation software AVL-Fire. The model was combined with a chemical reaction mechanism containing 377 reactions and 81 species to simulate the in-cylinder combustion. The purpose of this work was to study the effects of different diesel/ethanol/n-butanol blend percentages and injection timings on the combustion and emission characteristics of a diesel engine and to optimize the results with multiple objectives. The results showed that suitable injection timing provided the engine with better combustion and fuel economy. In addition, the advanced injection timing significantly reduced CO and soot emissions but increased NOx emissions. Moreover, the increased percentage of ethanol and n-butanol in the fuel mix reduces cylinder pressure and temperature, and increases fuel consumption. However, it improves the brake thermal efficiency and effectively reduces NOx, CO and soot emissions. Finally, the multi-objective parameter optimization of each parameter was performed by the orthogonal experimental design method in Design-Expert software. The results showed that the optimal match was achieved at a diesel blending rate of 88.49% and an injection timing of 12.43°CA BTDC.

Application of Multiobjective Optimization Integrating Numerical and Scientific Computing in Graph Theory Coloring Algorithm

In the current era of increasingly complex social life, as people’s demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency.

Study on Sustainable Agricultural Structure Optimization Method Based on Multiobjective Optimization Algorithm.

Agricultural sustainable development is one of the themes of human and nature harmonious coexistence. Adjusting and optimizing agricultural structure are an important direction to improve the level of agricultural sustainable development. In this paper, related research status of the sustainable development of agriculture is analyzed; it shows that there is lack of scientific theories guidance for agricultural sustainable development. In order to optimize sustainable development of agriculture industry structure, the guidance theory of and its optimization are studied. Based on multiobjective optimization theory, several key factors that affect agricultural sustainable development and the main target indexes of agricultural sustainable development are analyzed, the mathematical model of the evaluation of the sustainable development of agriculture is established, and the solution to optimize the multiobjective model is studied. Finally, the agricultural industry sustainable development in a certain area is taken as the research object in this paper; the mathematical model and solving method of agricultural sustainable development evaluation are studied; it provides a guidance to optimize the regional agricultural industrial structure and improve the quality of agricultural sustainable development.

Compression and Storage Algorithm of Key Information of Communication Data Based on Backpropagation Neural Network

This paper presents a backpropagation neural network algorithm for data compression and data storage. Data compression or establishing model ten coding is the most basic idea of traditional data compression. The traditionally designed ideas are mainly based on reducing the redundancy in the information and focus on the coding design, and its compression ratio has been hovering around dozens of percent. After the traditional coding compression of information, it is difficult to further compress by similar methods. In order to solve the above problems, the information that takes up less signal space can be used to represent the information that takes up more signal space to realize data compression. This new design idea of data compression breaks through the traditional limitation of relying only on coding to reduce data redundancy and achieves a higher compression ratio. At the same time, the information after such compression can be repeatedly compressed, and it has a very good performance. This is the basic idea of the combination of neural network and data compression introduced in this paper. According to the theory of multiobjective function optimization, this paper puts forward the theoretical model of multiobjective optimization neural network and studies a multiobjective data compression method based on neural network. According to the change of data characteristics, this method automatically adjusts the structural parameters (connection weight and bias value) of neural network to obtain the largest amount of data compression at the cost of small information loss. This method has the characteristics of strong adaptability, parallel processing, knowledge distributed storage, and anti-interference. Experimental results show that, compared with other methods, the proposed method has significant advantages in performance index, compression time and compression effect, high efficiency. and high-quality robustness.

Optimization of Construction Program Schedule Plans Based on Multi-Objective Optimization Theory

Optimization of Construction Program Schedule Plans Based on Multi-Objective Optimization Theory

Research on Hydropower Allocation Based on Multi-Objective Optimization and Bankruptcy Game Theory

In recent decades, droughts have become more frequent and severe due to global warming, and the amount of water in dams and reservoirs has decreased in many regions. To alleviate the water shortage problem, we modeled the water allocation in dams to achieve the optimal combination of water and electricity. A multi-objective optimization model for water allocation in dams was developed. A grid method was used to rasterize the dams and calculate the reservoir capacity of the dams. The multi-objective optimization model was used to improve water distribution schemes in five states based on the collection of water and electricity consumption data from five states in recent years. The consumption time for water transport and power generation, as well as the amount of water supplied, is minimized while meeting the water supply and power demand and providing surplus water for Mexico. In the next step, game theory was used to solve the conflict problem of allocating water among the four uses and to transform the problem into a utility non-transferable game model. Considering the differences in water use by state, the states were typed into three categories: hydro, industrial and non-industrial states. Different weights were assigned to the four types of water use in different types of states, taking into account the different importance attached to different water use in different regions. The allocation results for the different states were obtained.

An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations

Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes. First, an automatic operation framework, which can drive the MADYMO program and perform results analysis automatically, was built with four multi-objective optimization algorithms available – NSGA-Ⅱ, NCGA, AMGA, and MOPS; The optimization condition was controlled with 12 design variables, 5 objective functions, and 3 constraints. Then, a real e-bike accident with surveillance video was reconstructed through the proposed framework to verify its validity using comparisons of simulated and actual rest positions, initial variables, kinematic response, and head injury. Lastly, the simulation data were used to study the effects of the initial variables on objectives with a multiple linear regression model. The results showed that it took only about 24 h in total for optimization with 480 automatic operations. Optimal conditions were searched at run numbers of 469, 430, 323, and 474 for NSGA-Ⅱ, NCGA, AMGA, and MOPS, respectively. NSGA-Ⅱ had the best performance for e-bike accident reconstruction with average errors of objectives below 5%; Good consistencies for the rider’s kinematic response in three stages after collision were observed between simulations and screenshots from the surveillance video, as well as for velocities between the simulation and those estimated from the surveillance video and for head injury between the simulation and the medical report. In contrast to the subjective trial-and-error method that highly depends on the analyst’s intuition and experience, this intelligent method is based on multi-objective optimization theory, with which results can be optimized in terms of the automatic change of initial variables. All the above comparisons demonstrate that the method is valid for effectively improving efficiency without simultaneously compromising accuracy. This intelligent method, coupling automatic simulation and multi-objective optimization, can also be applied to other accident reconstructions, and the significant order of initial variables’ effects on objectives can provide recommendations for further reconstructions.

Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimization of MCS construction and operation was constructed. To solve the model effectively, the existing non-dominated sorting genetic algorithm III (NSGA-III) was improved by the authors on the basis of previous research, which could enhance the global search ability and convergence speed of the algorithm. To effectively improve and strengthen the reliability of energy supply, and increase the comprehensive energy utilization of the system, the effects of carbon transaction cost and renewable energy penetration were considered in the optimization process. Based on an engineering example, the bi-level model was solved and analyzed. It should be noted that the optimization results of the model were verified to be applicable and effective by comparison with the single-level multi-objective programming optimization. The findings of this paper could provide theoretical reference and practical guidance for the planning and operation of MCSs, making them significant for social application.