Multi-objective Optimization Function Research Articles

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.

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.

Simulation of temperature field and optimization of heat dissipation structure for lithium-ion power battery pack based on multi-objective function optimization

In order to improve the thermal power dissipation of batteries and reduce the maximum temperature difference of batteries, the air conditioning system using battery type was optimized. Using multi-objective optimization method based on orthogonal experiment and multiple rotations, the local optimum values of multi-objective were obtained: the left end height A of entrance area and the height B of air outlet were 9 mm and 28 mm, respectively, with a tolerance value C of 0.4 mm for battery spacing. The simulation results of this optimization model show that the maximum temperature difference of the battery volume is 306.627 K (33.477?C) and 3.815 K (3.815?C), respectively. Compared with the initial model, they are reduced by 9.55% and 25.89%, respectively. Compared with the single factor analysis optimization model, they are reduced by 0.36% and 20.27%, respectively, indicating that the multi-objective optimization results are better. This multi-objective programming solves the problem of different parameters of air conditioning heat dissipation model to some extent, and can provide reference for the optimization design of other models.

Collaborative Motion Planning Based on the Improved Ant Colony Algorithm for Multiple Autonomous Vehicles

To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method. In this method, the ant colony algorithm was introduced and improved to achieve collaborative motion planning for multiple autonomous vehicles. First, independent subpopulations of the same size were generated according to the number of autonomous vehicles. Then, a multi-objective optimization function was established to optimize spatial collaboration and trajectory costs, and to update the pheromone in the ant colony algorithm. Meanwhile, the evaporation coefficient in the algorithm was adaptively adjusted to enhance the global search ability and improve the convergence speed of the algorithm. Finally, a feasible path was planned for each autonomous vehicle based on the path of each subpopulation. Simulation results show that the proposed method is effective and it can achieve stronger adaptability than the artificial potential field motion planning algorithm.

Research on the development of college students’ sports program based on multi-objective optimization algorithm

Abstract In recent years, the physical fitness of college students has been declining year by year, which has become the focus of national concern. In this paper, firstly, the human body motion tracking based on a multi-objective optimization algorithm is applied in the auxiliary training of college students’ sports to provide data support for the formulation of sports plans, and the Chebyshev function evolution calculation is used to obtain the Pareto optimal solution for sports training. Secondly, the similarity function between the two-dimensional projection of the skeleton and the silhouette of the image is established in the constructed human body model so as to calculate the multi-objective optimization function. The optimized training plan was specifically analyzed after analyzing the sports assessment of college students in X school. The results show that compared with the conventional training plan, the optimized training plan has more sports load intensity indexes distributed between 1.5 and 2.0, indicating that the plan is more scientific and effective. The research presented in this paper can be a valuable resource for the creation of sports programs for college students.

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Abstract Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038&lt;0.05) and logic index (P=0.001&lt;0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.

Energy-Efficient Lane Change Trajectory Planning for Highway Traffic Scenarios Considering Different Driving Needs

This paper proposes an energy-saving lane-changing trajectory model for intelligent electric vehicles at high speeds that considers different driving needs under multiple constraints to address the issues of simple lane-changing considerations and poor safety. An economic index is added to construct a multi-objective optimization function based on the comfort, safety, and efficiency of lane changing. The particle swarm optimization algorithm is used to solve the optimal lane-changing time. The weight of the index is obtained by analyzing different driving needs using the Analytic Hierarchy Process in different scenarios. The multi-objective function is adjusted to plan the optimal lane-changing trajectory that meets driving needs. The simulation shows that the proposed model can generate smooth and feasible lane-changing trajectories that meet different driving needs. The energy consumption analysis results indicate that the construction of economic indicators can effectively reduce the energy consumption of vehicles driving on highways. The tracking analysis results indicate that the target vehicle can smoothly and safely change lanes on the planned trajectory, verifying the effectiveness and rationality of the planned trajectory.

Multi-Objective Optimization Research on the Integration of Renewable Energy HVAC Systems Based on TRNSYS

Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings.

Ferrite Pads Gap Thermal-Magnetic Evaluation and Mitigation for 11.1 kW Wireless Power Transfer

Local overheating and airgap magnetic leakage is severe due to the distribution of inter-tile airgap in wireless power transfer (WPT) ferrite pads. In this paper, a thermal-magnetic multi-objective optimization function (MOF) is proposed to evaluate and mitigate the thermal-magnetic deterioration caused by the distributing airgap. The effects of the airgap on temperature and leakage distribution are analyzed based on the magnetic-thermal coupling finite element method (FEM) simulation. It is found that the trend of the local temperature is opposite to that of the magnetic leakage following the increase of the airgap width. An interesting range can also be found by using the proposed MOF strategy. When the airgap width is within the range, the gap thermal-magnetic problems can be mitigated effectively. Subsequently, a full-scale prototype with 11.1kW at 85kHz is fabricated to validate the effectiveness of MOF strategy and the accuracy of evaluation. The pads with optimized airgap achieve greater comprehensive performance than that without the optimization.

A multi-objective optimization design approach of large mining planetary gear reducer

A two-stage computational framework is proposed to optimize the radiated noise and weight of a large mining planetary gear reducer under the rated conditions, based on a combination of response surface methodology and multi-objective optimization. The well-established transient dynamic analysis model of a large mining planetary gear reducer, which is used to analyze the mechanical strength and acoustic characteristics of the gear reducer. A unified experimental design is developed to obtain the response surface of the gearbox radiated noise and the mass of the gearbox housing. After obtaining the multi-objective optimization function, the multi-objective optimization problem for a lightweight and low-noise gearbox is performed using non-dominated sorting from the Genetic Algorithm II (NSGA-II). The research results demonstrates the effectiveness of the proposed optimization method in reducing vibrating amplitude and weight of the gearbox. This is crucial for minimizing energy consumption and enhancing the overall performance of the system. Additionally, the optimized gearbox design not only saves energy but also contributes to the reduction of carbon emissions, making it environmentally friendly.

A New Ammonia Kinetic Model in Ru-Catalyzed Steam-Reforming Reaction Containing N2 in Natural Gas

Hydrogen for building fuel cells is primarily produced by natural-gas steam-reforming reactions. Pipeline-transported natural gas in Europe and North America used to contain about 1% to 5% N2, which reacts with H2 in steam-reforming reactions to form NH3. In the case of Ru, one of the catalysts used in natural-gas steam-reforming reactions, the activity of the NH3-formation reaction is higher than that of Ni and Rh catalysts. Reforming gas containing NH3 is known to poison Pt catalysts in Polymer Electrolyte Membrane Fuel Cells (PEMFCs) and also poison catalysts in preferential oxidation (PROX). In this study, Langmuir–Hinshelwood-based models of the NH3-formation reaction considering H2 and CO were proposed and compared with a simplified form of the Temkin–Pyzhev model for NH3-formation rate. The kinetic parameters of each model were optimized by performing multi-objective function optimization on the experimental results using a tube-type reactor and the numerical results of a plug-flow one-dimension simple SR (steam-reforming) reactor.