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Automotive Innovation 2022年第1期5卷 29-42页

作者： Zhenpo Wang Lantian Li Junjun Deng Baokun Zhang Shuo Wang School of Mechanical Engineering Beijing Institute of TechnologyNO.5 Zhongguancun StreetBeijingChina Collaborative Innovation Center of Electric Vehicles in Beijing NO.5 Zhongguancun StreetBeijingChina

Fleets of autonomous vehicles including shuttle buses,freight trucks,and road sweepers will be deployed in the Olympic Vil-lage during Beijing 2022 Winter Olympics.This requires intelligent charging infrastructure based on wireless power transfer technology to be equipped.To increase the misalignment tolerance of a high-power wireless charger,the robustness of the magnetic coupler should be optimized.This paper presents a new type of unipolar coupler,which is composed of three con-nected coils in series.The dimensional configuration of the coils is analyzed by the finite element method.The characteristic parameters of the coil are identified with their influence on the self-inductance and coupling coefficient.An expert model is built,whose feasibility can be verified in the aimed design domain.Combined with the expert model,an improved simulated annealing algorithm with a backtracking mechanism is proposed.The primary coil can reach the expected characteristics from any starting parameter combination through the proposed optimization algorithm.Under the same conditions in terms of external circuit parameters,ferrite usage,and aluminum shielding,the offset sensitivity of the magnetic coupler can be reduced from 58.79%to 18.89%.A prototype is established,validating the feasibility of the proposed coil structure with the optimized parameter algorithm.

关键词： Wireless power transfer Magnetic coupler simulated annealing algorithm Robust optimization

An Improved Multi-Objective Hybrid Genetic-simulated annealing algorithm for AGV Scheduling under Composite Operation Mode

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Computers, Materials & Continua 2023年第12期77卷 3443-3466页

作者： Jiamin Xiang Ying Zhang Xiaohua Cao Zhigang Zhou School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Hubei Plog Technology Co. Ltd.Wuhan430000China

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

关键词： AGV scheduling composite operation mode genetic algorithm simulated annealing algorithm task advance evaluation strategy

simulated annealing algorithm for detecting graph isomorphism

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Journal of Systems Engineering and Electronics 2008年第5期19卷 1047-1052页

作者： Geng Xiutang Zhang Kai Dept. of Control Science and Engineering Huazhong Univ. of Science and Technology Wuhan 430074 P. R. China

Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.

关键词： graph isomorphism problem simulated annealing algorithm nondeterministic polynomial problem,local search.

Using genetic/simulated annealing algorithm to solve disassembly sequence planning

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Journal of Systems Engineering and Electronics 2009年第4期20卷 906-912页

作者： Wu Hao Zuo Hongfu Coll. of Civil Aviation Nanjing Univ. of Aeronautics and Astronautics Nanjing 210016 P. R. China

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

关键词： disassembly sequence planning disassembly hybrid graph connection matrix precedence matrix binary-tree algorithms simulated annealing algorithm genetic algorithm.

Structural optimization and segregation behavior of quaternary alloy nanoparticles based on simulated annealing algorithm

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Chinese Physics B 2016年第5期25卷 151-158页

作者：陆欣泽邵桂芳许两有刘暾东文玉华 Department of Physics Xiamen University Department of Automation Xiamen University

Alloy nanoparticles exhibit higher catalytic activity than monometallic nanoparticles, and their stable structures are of importance to their applications. We employ the simulated annealing algorithm to systematically explore the stable structure and segregation behavior of tetrahexahedral Pt–Pd–Cu–Au quaternary alloy nanoparticles. Three alloy nanoparticles consisting of 443 atoms, 1417 atoms, and 3285 atoms are considered and compared. The preferred positions of atoms in the nanoparticles are analyzed. The simulation results reveal that Cu and Au atoms tend to occupy the surface, Pt atoms preferentially occupy the middle layers, and Pd atoms tend to segregate to the inner layers. Furthermore, Au atoms present stronger surface segregation than Cu ones. This study provides a fundamental understanding on the structural features and segregation phenomena of multi-metallic nanoparticles.

关键词： alloy nanoparticle simulated annealing algorithm structural stability segregation

Application of DSAPSO algorithm in Distribution Network Reconfiguration with Distributed Generation

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Energy Engineering 2024年第1期121卷 187-201页

作者： Caixia Tao Shize Yang Taiguo Li School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

关键词： Reconfiguration of distribution network distributed generation particle swarm optimization algorithm simulated annealing algorithm active network loss

Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and simulated annealing algorithm

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Journal of Shanghai Jiaotong university(Science) 2020年第6期25卷 727-738页

作者：孙喜龙王登峰李汝恒张斌 School of Engineering Dali UniversityDali 671003YunnanChina Andi Sales Division FAW-VW Automotive Co.Ltd.Changchun 130011China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130025China Logistics Group Jilin UniversityChangchun 130025China

Multi-objective optimization of crashworthiness in automobile front-end structure was performed,and finite element model(FEM)was validated by experimental results to ensure that FEM can predict the response value with sufficient accuracy.Seven design variables and four crashworthiness indicators were defined.Through orthogonal design method,18 FEMs were established,and the response values of crashworthiness indicators were extracted.By using the variable-response specimen matrix,Kriging surrogate model(KSM)was constructed to replace FEM to refect the function correlation between variables and responses.The accuracy of KSM was also validated.Finally,the simulated annealing optimization algorithm was implemented in KSM to seek optimal and reliable solutions.Based on the optimal results and comparison analysis,the 9096-th iteration point was the optimal solution.Although the intrusion of firewall and the mass of optimal structure increased slightly,the vehicle acceleration of the optimal solution decreased by 6.9%,which fectively reduced the risk of occupant injury.

关键词： crashworthiness multi-objective optimization Kriging surrogate model(KSM) simulated annealing algorithm

Usage of simulated annealing algorithm in Design of Optical Thin Film

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Journal of Shanghai Jiaotong university(Science) 2011年第3期16卷 372-374页

作者：王文梁戎晓红 Department of Physics Nanchang University Xuzhou Institute of Industry Technology

simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimization problem.In this paper,we use the simulated annealing algorithm to design an edge filter,which is composed of 20 dielectric thin film layers with TiO2 and SiO2.The simulated annealing algorithm is a very robust algorithm for optical thin film design.

关键词： simulated annealing algorithm optical thin film edge filter

Application of simulated annealing genetic algorithm-optimized back propagation(BP)neural network in fault diagnosis

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International Journal of Modeling, Simulation, and Scientific Computing 2019年第4期10卷 84-95页

作者： Dawei Zhang Weilin Li Xiaohua Wu Xiaofeng Lv School of Automation Department of Electrical Engineering Northwestern Polytechnical University 710129 Xi’an ShaanxiP.R.China School of Basic Science for Aviation Naval Aviation University 264001 Yantai ShandongP.R.China

Optimal weights are usually obtained in neural network through a fixed network conformation,which affects the practicality of the network.Aiming at the shortage of conformation design and weight training algorithm in neural network application,the back propagation(BP)neural network learning algorithm combined with simulated annealing genetic algorithm(SAGA)is put forward.The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations.The simulated annealing mechanism is incorporated into the Genetic algorithm(GA)to optimize the design and optimization of neural network conformation and network weights simultaneously.The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process,also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon.The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm.The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.

关键词： Neural network genetic algorithm simulated annealing algorithm on-board electrical control box fault diagnosis

AN ASYNCHRONOUS PARALLEL simulated annealing algorithm FOR TSP

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Progress in Natural Science:Materials International 1991年第2期 165-172页

作者：康立山陈毓屏 PRC Parallel Computational Research Group Wuhan University Wuhan 430072

An asynchronous parallel simulated annealing algorithm for solving the TSP(traveling salesmanproblem)on the MIMD machines is proposed.The random k-bond interchange(k=2 or 3)heuristicsintroduced by Press et al.is repla... 详细信息

关键词： TSP(traveling salesman problem) simulated annealing algorithm