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Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm 认领 引用
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作者 SUN Xilong WANG Dengfeng +1 位作者 LI Ruheng ZHANG Bin 《Journal of Shanghai Jiaotong university(Science)》 EI 2020年第6期727-738,共12页
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... 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. 展开更多
关键词 crashworthiness multi-objective optimization Kriging surrogate model(KSM) simulated annealing algorithm
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A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems 认领 引用 被引量：9
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作者 Mohamed Hamdy Anh-Tuan Nguyen +1 位作者 Jan L.M. Hensen 侯恩哲 《建筑节能》 2016年第6期4-4,共1页
Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms hav... Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. 展开更多
关键词 Multi-objective optimization Algorithms Experimentation Building simulation Comparison
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Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm 认领 引用
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作者 Xiaokan Wang Qiong Wang 《Journal on Internet of Things》 2021年第1期1-9,共9页
A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of op... A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect. 展开更多
关键词 Multi-objective improved genetic algorithm urban rail train train operation simulation multi particle optimization model
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Multi-objective optimization of high-sulfur natural gas purif ication plant 认领 引用 被引量：1
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作者 Jian-Feng Shang Zhong-Li Ji +1 位作者 Min Qiu Li-Min Ma 《Petroleum Science》 SCIE CAS CSCD 2019年第6期1430-1441,共12页
There exists large space to save energy of high-sulfur natural gas purification process.The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption a... There exists large space to save energy of high-sulfur natural gas purification process.The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas.A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax.Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution.To solve the problem that the process model does not converge in some conditions,back-propagation(BP)neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model.The uniform design method and the table U21(107)have been applied to design the experiment points for training and testing BP model.High prediction accuracy can be achieved by using the BP model.Nondominated sorting genetic algorithm-II has been developed to optimize the two objectives,and 100 Pareto optimal solutions have been obtained.Three optimal points have been selected and evaluated further.The results demonstrate that the total comprehensive energy consumption is reduced by 13.4%and the production rate of purified gas is improved by 0.2%under the optimized operating conditions. 展开更多
关键词 High-sulfur natural gas purifi cation plant Multi-objective optimization Process simulation model Thermodynamic analysis BP neural network Genetic algorithm
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Overview of multi-objective optimization methods 认领 引用 被引量：2
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作者 LeiXiujuan ShiZhongke 《Journal of Systems Engineering and Electronics》 2004年第2期142-146,共5页
To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description ab... To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper. 展开更多
关键词 multi-objective optimization objective function Pareto optimality genetic algorithms simulated annealing fuzzy logical.
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Robust design and optimization for autonomous PV-wind hybrid power systems 认领 引用 被引量：1
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作者 Jun-hai SHI Zhi-dan ZHONG +1 位作者 Xin-jian ZHU Guang-yi CAO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第3期401-409,共9页
This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated... This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness. 展开更多
关键词 PV-wind power system Robust design Constraint multi-objective optimizations Multi-objective genetic algorithms Monte Carlo Simulation （MCS） Latin Hypercube Sampling （LHS）
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MULTI-OBJECTIVE OPTIMIZATION DESIGN FOR TRANSONIC TURBINE CASCADES USING SIMULATED ANNEALING ALGORITHM 认领 引用 被引量：2
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作者 Tong Tong Feng Zhen-ping 《Journal of Hydrodynamics》 SCIE EI 1999年第2期20-25,共6页
On the basis of computational of transonic viscid flow, the simulated annealing algorithm, used in statistical mechanics to study solid cooling process, is adaptedfor soving multi-objective cascades design problem. Th... On the basis of computational of transonic viscid flow, the simulated annealing algorithm, used in statistical mechanics to study solid cooling process, is adaptedfor soving multi-objective cascades design problem. The simulated annealing algorithmdraws an analogy between the energy minimization in physical system and the objectivefunction in the real design problem, To model the multi-objective functions, a minimumdeviation method is used. In this paper, the loss and work are considered as the objectivefunctions. Simulated results indicate that this algorithm can be effectively applied to theimprovement of the design for transonic turbine cascades. 展开更多
关键词 simulated annealing algorithm (SAA) turbine cascades multi-objective optimization
Intersection signal control multi-objective optimization based on genetic algorithm 认领 引用 被引量：13
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作者 Zhanhong Zhou Ming Cai 《Journal of Traffic and Transportation Engineering(English Edition)》 2014年第2期153-158,共6页
A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signa... A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combined the Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model （CMEM）, and genetic algorithm. An intersection in Haizhu District, Guangzhou, was taken for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme. 展开更多
关键词 scopic traffic intersection simulation traffic signal control multi-object optimization genetic algorithm micro- CMEM
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Automatic Optimization of Guidance Guardrail Layout Based on Multi-Objective Evolutionary Algorithm 认领 引用
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作者 Wei-Li Liu Yixin Chen +3 位作者 Xiang Li Jinghui Zhong Rongjun Chen Hu Jin 《Complex System Modeling and Simulation》 EI 2024年第4期353-367,共15页
Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places.However,existing methods of the guardrail layout mainly rely on manual design or mathem... Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places.However,existing methods of the guardrail layout mainly rely on manual design or mathematical models,which are not flexible or effective enough for crowd control in large public places.To address this limitation,this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm.The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout.In addition to optimizing pedestrian passage efficiency and safety,the paper also considers the situation of pedestnan counterflow,making the guardrai layout more practical.Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion,as well as its significant improvements in passage effciency and prevention of pedestrian counte rflow. 展开更多
关键词 automatical guardrall layout optimization,crowd simulation multi-objective evolutionary algorithm
A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time 认领 引用 被引量：19
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作者 Xiabao Huang Lixi Yang 《International Journal of Intelligent Computing and Cybernetics》 EI 2019年第2期154-174,共21页
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th... Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper. 展开更多
关键词 Flexible job-shop scheduling problem Transportation time Genetic algorithm,Simulated annealing Multi-objective optimization
A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem 认领 引用
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作者 Wei-Shung CHANG Chiuh-Cheng CHYU 《Journal of Software Engineering and Applications》 2009年第5期323-329,共7页
In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives:... In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method. 展开更多
关键词 Multi-Objective Optimization Unrelated Parallel Machines Scheduling Simulated Annealing Algorithm Integer Programming Models Multi-Criteria Decision Making
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GASA Hybird Algorithm Applied in Airline Crew Rostering System 认领 引用
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作者 张应辉 饶云波 周明天 《Tsinghua Science and Technology》 EI CAS 2007年第S1期255-259,共5页
Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the wo... Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the work efficiency. This paper presents a method of building models for automatic crew rostering mode with computer and advancing the multi-objective optimum scheme. The method to build models for crew rostering system is also discussed. The question to crew rostering system model is solved by genetic algorithms and simulated annealing algorithms. Simulation results show the correctness of algorithms. The actual data of the airways have justified its reasonability and efficiency. 展开更多
关键词 service system crew rostering system multi-objective combinatorial optimization simulated annealing algorithms genetic algorithms crew rostering system model
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