Multi-objective Optimization Function Research Articles

Design and Test of a Traction Side-Pull Square Straw Bale Pick-Up-and-Stack Truck

To address the issues of limited one-time loading capacity, single functionality, and low automation level in existing square straw bale pickers, a large automated square straw bale pick-up-and-stack truck that integrates picking, stacking, transporting, and bundling functions has been developed, combining the technical advantages of one-time field removal and storage of bales. We innovatively designed a side-pulling traction mechanism that can realize the rapid transition between the transporting state and working state of the machine; a picking device that can complete the continuous action of forking, lifting, turning positioning, and de-forking; and a bundling device that can realize the adjustment of the attitude of square straw bales. Response surface tests were conducted on the prototype to determine the key structural and operational parameters, using the bundle completion rate and regular bale rate as evaluation indicators. Regression and significance tests were performed on the machine’s forward speed, chassis frame offset, and the ground clearance of the fork tine to determine the influence and priority of these factors on the evaluation indicators. Through multi-objective function optimization of the regression model, the optimal parameter combination was found to be a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, resulting in a bundle completion rate of 98.85% and a regular bale rate of 96.96%. Subsequent field tests with the optimized parameters showed that at a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, the bundle completion rate was 98.37% and the regular bale rate was 95.83%, meeting the relevant design requirements. This study can provide a reference for the design and development of straw collection and storage machinery.

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

An innovative quick-decision strategy for evaluating the applicability of extractive distillation process with a preconcentration column in separating high-concentration organic waste liquid

In the face of the high energy consumption challenge in separating high-concentration organic waste liquid through extractive distillation (ED), the concept of preconcentration provides a new strategic direction. Therefore, it has become crucial to determine the suitability of the ED process with a preconcentration column for processing such complex mixture. Taking this into consideration, the study proposes an innovative quick-decision method that utilizes thermodynamic analysis of ternary phase diagram to determine the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate, which serves as the basis for deciding whether to choose the ED process with a preconcentration column for handling such separation tasks. Three azeotropic systems with different thermodynamic characteristics are chosen as case studies: cyclohexane/ethyl acetate/ethanol (Case 1), ethyl acetate/ethanol/water (Case 2), and n-hexane/ethyl acetate/acetonitrile (Case 3). The task of separating the above three systems is accomplished through the application of both three-column extractive distillation configuration (TCED), and four-column extractive distillation configuration (FCED). The objective functions for multi-objective optimization of all separation configurations include the minimum total annual cost (TAC), the minimum entropy generation and the minimum CO2 emissions. The research findings indicate that when the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate is not less than 0.56, or when there is a significant concentration difference among the components in the feed stream, the FCED configuration exhibits clear advantages over the TCED configuration in terms of economic, environmental, and thermodynamic aspects.

Optimal Design of Surface-Mounted Permanent Magnet Synchronous Motors for Robotic Joint Actuation Using Multi-Objective Function Optimization

Optimal Design of Surface-Mounted Permanent Magnet Synchronous Motors for Robotic Joint Actuation Using Multi-Objective Function Optimization

Optimal operation of energy-intensive load considering electricity carbon market

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization

To improve traffic efficiency, adaptive traffic signal control (ATSC) systems have been widely developed. However, few studies have proactively optimized the air environmental issues in the development of ATSC. To fill this research gap, this study proposes an optimized ATSC algorithm to take into consideration both traffic efficiency and decarbonization. The proposed algorithm is developed based on the deep reinforcement learning (DRL) framework with dual goals (DRL-DG) for traffic control system optimization. A novel network structure combining Convolutional Neural Networks and Long Short-Term Memory Networks is designed to map the intersection traffic state to a Q-value, accelerating the learning process. The reward mechanism involves a multi-objective optimization function, employing the entropy weight method to balance the weights among dual goals. Based on a representative intersection in Changsha, Hunan Province, China, a simulated intersection scenario is constructed to train and test the proposed algorithm. The result shows that the ATSC system optimized by the proposed DRL-DG results in a reduction of more than 71% in vehicle waiting time and 46% in carbon emissions compared to traditional traffic signal control systems. It converges faster and achieves a balanced dual-objective optimization compared to the prevailing DRL-based ATSC.

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.

Exergoeconomic analysis and optimization of wind power hybrid energy storage system

The hybrid energy storage system of wind power involves the deep coupling of heterogeneous energy such as electricity and heat. Exergy as a dual physical quantity that takes into account both 'quantity' and 'quality, plays an important guiding role in the unification of heterogeneous energy. In this paper, the operation characteristics of the system are related to the energy quality, and the operation strategy of the wind power hybrid energy storage system is proposed based on the exergoeconomics. First, the mathematical model of wind power hybrid energy storage system is established based on exergoeconomics. Then, wind power experiments of three forms of thermal-electric hybrid energy storage are carried out, and RSM is used to analyze the power quality and exergoeconomic characteristics of the system, and the optimal working conditions of the experiment are obtained. Finally, an optimization strategy is proposed by combining experiment and simulation. The system efficiency, unit exergy cost and current harmonic distortion rate are multi-objective optimization functions. The three algorithms evaluate the optimal solution based on standard deviation. The results show that the exergoeconomics can effectively judge the production-storage-use characteristics of the new system of ' wind power + energy storage'. The thermal-electric hybrid energy storage system can absorb the internal exergy loss of the battery, increase the exergy efficiency by 10%, reduce the unit exergy cost by 0.03 yuan/KJ, and reduce the current harmonic distortion rate by 8%. It provides guidance for improving the power quality of wind power system, improving the exergy efficiency of thermal-electric hybrid energy storage wind power system and reducing the unit cost.

Topology optimization of the wheel frame of the tracked robot with multiple working conditions and multiple objectives

Based on ensuring the static stiffness and dynamic vibration frequency of the caterpillar robot wheel frame under various working conditions, the lightweight design of the caterpillar robot wheel frame is realized. The compromise programming approach is used to establish the multi-objective optimization function and three typical target conditions are simulated and analyzed. An analytic hierarchy process is used to determine the weight coefficient of each target working condition, and the topology optimization of the wheel frame of the tracked robot is carried out comprehensively, considering multi-working conditions and multi-target. The secondary design of the optimized rear wheel frame was carried out, as a result of which, the mass was reduced by 8.4% after optimization, and the first-order frequency was increased by 20 Hz after optimization. The optimized rear wheel frame strength and stiffness met the requirements.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

Data-Driven Multi-Objective Optimization Approach to Loaded Meshing Transmission Performances for Aerospace Spiral Bevel Gears.

Loaded meshing transmission performance optimization has been an increasingly significant target for the design and manufacturing of aerospace spiral bevel gears with low noise and high strength. An innovative data-driven multi-objective optimization (MOO) method is proposed for the loaded meshing transmission performances of aerospace spiral bevel gears. Data-driven tooth surface modeling is first used to obtain a curvature analysis of loaded contact points. An innovative numerical loaded tooth contact analysis (NLTCA) is applied to develop the data-driven relationships of machine tool settings with respect to loaded meshing transmission performance evaluations. Moreover, the MOO function is solved by using an achievement function approach to accurate machine tool settings output, satisfying the prescribed requirements. Finally, numerical examples are given to verify the proposed methodology. The presented approach can serve as a powerful tool to optimize the loaded meshing transmission performances with higher computational accuracy and efficiency than the conventional methods.

Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

Sensor Placement Optimization of Visual Sensor Networks for Target Tracking Based on Multi-Objective Constraints

With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.

Multi-Intersection Ecological Cooperative Control of a Pure Electric Vehicle Platoon with Dual-Motor Dual-Axis Drive

The advent of intelligent transportation system technology has expedited the advancement of eco-driving controls. Research on energy-saving cooperative control of intelligent connected vehicles in intelligent transportation environments is still in need of ongoing refinement. This article proposes a dual-level ecological driving control strategy for a pure electric vehicle platoon with dual-motor dual-axis drive in an urban multi-intersection environment. The upper-level design incorporates optimal platoon speed decision-making based on the nonlinear model predictive control algorithm. The multi-objective optimization function considers three scenarios: energy-optimal, time-optimal, and energy-time-optimal. It also takes into account platoon following control and passing efficiency, ensuring smooth passage through multi-intersections without interruptions. Built on the upper level’s optimal speed design, an energy management strategy is proposed for achieving optimal torque distribution of pure electric vehicles with front and rear independent drives. Finally, the upper and lower levels are jointly simulated in real-time. The results indicate that, compared to the energy-optimal mode, the average passage time decreased by 14.6% and 5.97% in the time-optimal and energy-time-optimal modes, respectively. Under average torque distribution, the time-optimal and energy-time-optimal modes increased the energy consumption of the vehicle platoon by 21.05% and 5.44%, respectively, compared to the energy-optimal mode. Under the optimal torque allocation strategy, the time-optimal and energy-time-optimal modes increased energy consumption by 15.31% and 6.11%, respectively, compared to the energy-optimal mode. In contrast to the average torque allocation strategy, the optimal torque allocation strategy for the dual-motor vehicles reduced energy consumption by 10.18%, 14.44%, and 9.61% in the energy-optimal mode, time-optimal mode and energy-time-optimal mode, respectively.

A Direct Yaw Moment Control Framework Through Robust T-S Fuzzy Approach Considering Vehicle Stability Margin

The direct yaw moment control system of distributed drive electric vehicles provides a flexible control scheme to enhance the vehicle stability through four independently driven in-wheel motors. However, the vehicle nonlinearities may lead to the invalidation of control. To this end, a Takagi–Sugeno (T-S) fuzzy-based robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> control method is proposed to ensure the vehicle performance while addressing the nonlinear challenge. First, thanks to the T-S fuzzy modeling technology, the tire nonlinear characteristics are described by fuzzy rules, based on which the vehicle lateral dynamics model is established. Next, the safety region represented by the tire slip angles phase plane is presented to evaluate the vehicle stability performance. Considering the control priority of vehicle handling performance and stability control with different stability margins, a multiobjective optimization function is transformed into a standard robust performance optimization problem with dynamic weight coefficients. A T-S fuzzy-based robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> state feedback controller is then designed to ensure the system stability and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> performance. Finally, the hardware-in-the-loop tests are conducted to validate the proposed controller. Comparative results show the effectiveness to improve the vehicle handling performance while ensuring the stability.

Novel data encryption scheme and its application to secure multi-party communication

Recent advances in cellular mobile telecommunication field have dramatically facilitated the multi-party collaborative work in social networks. However, the privacy issues exposed by insecure network channels and semi-trusted service providers, such as underlying data analysis and mining, have gradually aroused public concerns. In this context, a novel Multi-Party Privacy Data Encryption (MP-PDE) scheme built upon the deep learning framework is proposed. In this scheme, a four-dimensional autonomous chaotic system is initially leveraged to configurate the key-controlled cipher streams. Under the guidance of a multi-objective optimization function, the proposed encryption network manipulates the multi-party private data into a cipher image with the statistical pseudo-randomness. At the recipient side, distinct participants can decrypt the corresponding data availing their own licensing key from the identical cipher image. Furthermore, the encryption and decryption networks are equivalent except for their trainable network parameters. Finally, numerous experiments are conducted to verify the effectiveness and security of the proposed scheme.

Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

Power system differentiation planning based on an improved marine predator algorithm

Abstract Effective power system differentiation planning is crucial for enhancing the resilience of power grid infrastructure and bolstering the ability of power systems to manage blackouts. At the heart of power system differentiation planning lies core backbone grid planning. This study involves modeling core backbone grid planning as a multi-objective 0–1 planning problem, which enables the formulation of a multi-objective function that incorporates various factors such as operational and maintenance costs, the significance of nodes and transmission lines, as well as compliance with connectivity and security operation constraints. Moreover, the basic marine predator algorithm was upgraded into a multi-objective optimization algorithm for core backbone grid planning by implementing file management and enhancing the top predator selection mechanism, which managed to fulfill the multi-objective function optimization standards. The results show that in the IEEE 39-node system, the algorithm successfully forms a core backbone grid comprising 22 nodes and 19 transmission lines, achieving economic feasibility with a node-to-line ratio of 1.158. Similarly, for the IEEE 300-node system, the algorithm constructs a larger core backbone grid consisting of 81 nodes and 80 transmission lines, maintaining economic efficiency with a node-to-line ratio of 1.0125. This expanded grid covers a significant number of critical nodes and transmission lines, ensuring optimal network connectivity. Furthermore, the algorithm’s load satisfaction analysis showcases its ability to effectively balance active and reactive power demands, with maximum outputs meeting the respective load demands.

A surrogate shale oil model based on a multi-objective fusion adaptive optimization considering its pyrolysis characteristics

A surrogate shale oil model was proposed based on model compounds with optimal proportions to reduce the complexity of Huadian shale oil to analyze its pyrolysis kinetics. There are mainly n-alkanes and n-olefins with C8–C36 in shale oil according to GC-MS results, a small amount of aromatics and alcohols, as well as trace of esters, ketones and nitriles, etc. Pyrolysis behavior of shale oil was investigated by Py-GC-MS and FTIR, including the evolution of pyrolytic products and functional groups. Here, n-eicosane, 1-octadecene, pentadecanol and nonylbenzene were selected as surrogates based on GC-MS result. To obtain the optimal surrogate model, a novel multi-objective fusion adaptive optimization function with constraints was proposed to determine the optimal weighting factor of each component from the pareto optimal set. Then, the detailed pyrolysis kinetic mechanisms containing 745 species and 19392 reactions were developed to study its thermochemical conversion. Moreover, the skeletal mechanism reduced by decoupling and directed relation graph (DRG) was used to describe the thermal flow reactivity of these four components. In sum, this work provides an integrated description of shale oil pyrolysis, and gives a new method for studying the pyrolysis of oil shale intrinsically.