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Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm 认领 引用 被引量：12
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作者 Chandan Guria Kiran K Goli Akhilendra K Pathak 《Petroleum Science》 SCIE CAS CSCD 2014年第1期97-110,共14页
A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered f... A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are：（i） maximizing drilling depth,（ii） minimizing drilling time and （iii） minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are：（i） equivalent circulation mud density,（ii） drill bit rotation,（iii） weight on bit and （iv） Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth. 展开更多
关键词 Drilling performance rate of penetration abnormal pore pressure genetic algorithm multi-objective optimization
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Multi-objective optimization of membrane structures based on Pareto Genetic Algorithm 认领 引用 被引量：9
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作者 伞冰冰 孙晓颖 武岳 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第5期622-630,共9页
A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization v... A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization variables,which are decision factors of shapes of membrane structures.Three objectives are proposed including maximization of stiffness,maximum uniformity of stress and minimum reaction under external loads.Pareto Multi-objective Genetic Algorithm is introduced to solve the Pareto solutions.Consequently,the dependence of the optimality upon the optimization variables is derived to provide guidelines on how to determine design parameters.Moreover,several examples illustrate the proposed methods and applications.The study shows that the multi-objective optimization method in this paper is feasible and efficient for membrane structures;the research on Pareto solutions can provide explicit and useful guidelines for shape design of membrane structures. 展开更多
关键词 membrane structures multi-objective optimization Pareto solutions multi-objective genetic algorithm
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Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms 认领 引用 被引量：6
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作者 WANG Binggang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2010年第5期537-546,共10页
As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simul... As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm. 展开更多
关键词 mixed-model production system sequencing parallel machine buffers multi-objective genetic algorithm multi-objective simulated annealing algorithm
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MULTI OBJECTIVE OPTIMIZATION USING GENETIC ALGORITHM WITH LOCAL SEARCH  认领 引用
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作者 戴晓晖 李敏强 寇纪淞 《Transactions of Tianjin University》 CAS 1998年第2期31-35,共5页
In this paper,we propose a hybrid algorithm for finding a set of non dominated solutions of a multi objective optimization problem.In the proposed algorithm,a local search procedure is applied to each solution generat... In this paper,we propose a hybrid algorithm for finding a set of non dominated solutions of a multi objective optimization problem.In the proposed algorithm,a local search procedure is applied to each solution generated by genetic operations.The aim of the proposed algorithm is not to determine a single final solution but to try to find all the non dominated solutions of a multi objective optimization problem.The choice of the final solution is left to the decision makers preference.High search ability of the proposed algorithm is demonstrated by computer simulation. 展开更多
关键词 multi objective genetic algorithm Pareto set local search
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Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV 认领 引用 被引量：4
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作者 ZHANG Ming-jun , ZHENG Jin-xing , ZHANG Jing College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001 ,China College of Computer and Information Science, Harbin Engineering University, Harbin 150001 , China 《Journal of Marine Science and Application》 2002年第1期81-86,共6页
To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as... To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible. 展开更多
关键词 AUV multi — objective optimization genetic algorithm selection method
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Multi-objective Optimization of Continuous Drive Friction Welding Process Parameters Using Response Surface Methodology with Intelligent Optimization Algorithm 认领 引用 被引量：6
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作者 P.M.AJITH T.M.AFSAL HUSAIN +1 位作者 P.SATHIYA S.ARAVINDAN 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2015年第10期954-960,共7页
The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input par... The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation. 展开更多
关键词 friction welding response surface methodology genetic algorithm Pareto front multi-objective optimization duplex stainless steel
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Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm 认领 引用 被引量：13
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作者 邱志平 张宇星 《Chinese Journal of Aeronautics》 SCIE EI CAS 2010年第4期430-437,共8页
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ... For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm. 展开更多
关键词 aircraft design conceptual design multi-objective optimization tabu search genetic algorithm Pareto optimal
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基于改进MOGA的原油供应链全球哨点的布局与优化 认领 引用
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作者 刘子玉 常笑笑 赵林度 《供应链管理》 2026年第6期40-54,共15页
在全球能源格局深刻变革的背景下,原油供应链韧性已成为国家经济安全的核心议题。针对传统静态关键节点识别方法难以捕捉动态冲击下系统功能衰退过程的局限,文章将公共卫生领域的“哨点”概念引入供应链韧性研究,构建了一个融合复杂网... 在全球能源格局深刻变革的背景下,原油供应链韧性已成为国家经济安全的核心议题。针对传统静态关键节点识别方法难以捕捉动态冲击下系统功能衰退过程的局限,文章将公共卫生领域的“哨点”概念引入供应链韧性研究,构建了一个融合复杂网络理论和多目标优化方法的动态哨点识别框架。基于2015-2024年全球原油贸易数据,采用改进多目标遗传算法(MOGA),同步优化网络效率、最大强连通子图规模、初始崩溃斜率和尾部损失四个系统韧性目标,从而反向识别对扰动最敏感的哨点国家。在方法层面,引入熵正则化和相关性惩罚机制,有效缓解了高维指标冗余和权重塌陷问题,提升了识别结果的稳健性。研究结果表明:从全球视角看,原油供应链韧性呈现显著的规模主导特征,哨点布局演化体现了从集中依赖向多极均衡的结构性转变。从中国视角看,供应安全高度依赖直接贸易规模,结构性指标贡献较弱,呈现典型的高集中度-高依赖度风险模式。进一步依据系统影响度与供应链适配性,将关键哨点划分为四类,并据此提出差异化的风险监测和布局优化策略。文章构建的框架能够有效识别原油供应链中的关键脆弱节点,为构建前瞻性预警机制、提升能源安全韧性提供量化工具和决策支持。 展开更多
关键词 复杂网络 原油供应链 多目标遗传算法 哨点国家 网络韧性
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基于博弈思想改进MOGA的混流式水轮机叶片表面磨损优化研究 认领 引用
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作者 刘洋 王波 +1 位作者 吕传宝 熊敏杨 《模具技术》 2026年第2期119-125,共7页
为减小混流式水轮机叶片表面的磨损率,提出一种基于博弈思想改进多目标遗传算法(MOGA)的混流式水轮机叶片表面磨损优化方法。首先以叶片表面磨损率最小化和水轮机效率最大化,建立多目标函数并设置约束条件;然后采用博弈论思想改进的MOG... 为减小混流式水轮机叶片表面的磨损率,提出一种基于博弈思想改进多目标遗传算法(MOGA)的混流式水轮机叶片表面磨损优化方法。首先以叶片表面磨损率最小化和水轮机效率最大化,建立多目标函数并设置约束条件;然后采用博弈论思想改进的MOGA进行多目标函数求解;最后开展数值模拟实验对方法进行验证。结果表明,相较于优化前,本方法优化后的叶片表面磨损率减少约20%,效率差距约为0.5%。由此得出,本方法可降低混流式水轮机叶片表面磨损率,并有效保障混流式水轮机效率,具有有效性和可行性。 展开更多
关键词 混流式水轮机 叶片优化 数值模拟 多目标遗传算法(MOGA)算法 磨损率 效率
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Multi-objective Optimization Conceptual Design of Product Structure Based on Variable Length Gene Expression 认领 引用 被引量：7
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作者 WEI Xiaopeng ZHAO Tingting +2 位作者 JU Zhenhe ZHANG Shi LI Xiaoxiao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第1期42-49,共8页
It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure acc... It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure accords with the requirement of design. For the requirement generally is dynamic variety as time passes, new requirements will come, and some initial requirements can no longer be used. The number of product requirements, the gene length expressing requirements, the structure of the product, and the correlation matrix are varied with individuation of customer requirements of the product. By researching on the calculation mechanisms of dynamic variety, the approaches of gene expression and variable length gene expression are proposed. According to the diversity of structure selection in conceptual design and mutual relations between structure and function as well as structure and structure, the correlation matrixes between structure and function as well as structure and structure are defined. By the approach of making the sum of the elements of correlation matrix maximum, the mathematical models of multi-object optimization for structure design are provided based on variable requirements. An improved genetic algorithm called segment genetic algorithm is proposed based on optimization preservation simple genetic algorithm. The models of multi-object optimization are calculated by the segment genetic algorithm and hybrid genetic algorithm. An example for the conceptual design of a washing machine is given to show that the proposed method is able to realize the optimization structure design fitting for variable requirements. In addition, the proposed approach can provide good Pareto optimization solutions, and the individuation customer requirements for structures of products are able to be resolved effectively. 展开更多
关键词 gene expression multi-object optimization conceptual design genetic algorithm
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基于MOGA优化注入谐波电流的永磁同步电机振动抑制策略 认领 引用 被引量：4
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作者 刘锟 高剑 +1 位作者 刘晨旭 高世诚 《电机与控制应用》 2025年第10期1039-1049,共11页
【目的】针对永磁同步电机(PMSM)运行中产生的电磁振动噪声,注入谐波电流是一种有效的主动抑振方法。然而,传统方法在确定谐波电流的参数上存在困难,且忽略了径向电磁力与转矩性能的耦合。【方法】首先,本文以10极60槽PMSM为研究对象。... 【目的】针对永磁同步电机(PMSM)运行中产生的电磁振动噪声,注入谐波电流是一种有效的主动抑振方法。然而,传统方法在确定谐波电流的参数上存在困难,且忽略了径向电磁力与转矩性能的耦合。【方法】首先,本文以10极60槽PMSM为研究对象。考虑开槽作用,通过二维快速傅里叶变换分析出主导电磁力分量为0阶12倍频,并分析其来源。其次,分析了谐波电流与径向电磁力的关联机制。针对谐波电流与电磁性能间的强耦合特性,构建以抑振性能与转矩性能兼顾的多目标优化模型,采用多目标遗传算法(MOGA)优化电流谐波的幅值和相位。最后,建立电磁-结构耦合的多物理场仿真模型,对优化前后的电机振动响应进行对比验证。【结果】结果表明,电枢反应槽谐波和永磁体磁场的作用对主导径向电磁力贡献最大。此外,注入由MOGA优化的幅值和相位谐波电流后,电机振动加速度可削弱12.75%,转矩脉动降低2.61%,验证了优化算法的有效性和注入谐波电流抑振策略的可行性。【结论】本文所提基于MOGA的谐波电流参数优化方法,在抑制振动与提高转矩性能之间实现了有效平衡,为电磁力成因分析及其主动抑制提供了理论和实践参考。 展开更多
关键词 永磁同步电机 径向电磁力 多目标遗传算法 电磁振动 注入谐波电流 转矩脉动
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Cleaner production for continuous digester processes based on hybrid Pareto genetic algorithm 认领 引用
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作者 JIN Fu\|jiang, WANG Hui, LI Ping (Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China. 《Journal of Environmental Sciences》 SCIE EI CAS 2003年第1期129-135,共7页
Pulping production process produces a large amount of wastewater and pollutant emitted, which has become one of the main pollution sources in pulp and paper industry. To solve this problem, it is necessary to implemen... Pulping production process produces a large amount of wastewater and pollutant emitted, which has become one of the main pollution sources in pulp and paper industry. To solve this problem, it is necessary to implement cleaner production by using modeling and optimization technology. This paper studies the modeling and multi\|objective genetic algorithms for continuous digester process. First, model is established, in which environmental pollution and saving energy factors are considered. Then hybrid genetic algorithm based on Pareto stratum\|niche count is designed for finding near\|Pareto or Pareto optimal solutions in the problem and a new genetic evaluation and selection mechanism is proposed. Finally using the real data from a pulp mill shows the results of computer simulation. Through comparing with the practical curve of digester,this method can reduce the pollutant effectively and increase the profit while keeping the pulp quality unchanged. 展开更多
关键词 cleaner production multi\|objective optimization genetic algorithm Pareto stratum concentration of residual alkali Kamyr continuous digester
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Method of Designing Missile Controller Based on Multi-Objective Opti mization 认领 引用
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作者 林波 孟秀云 刘藻珍 《Journal of Beijing Institute of Technology》 EI CAS 2006年第2期152-155,共4页
A method of designing robust controller based on genetic algorithm is presented in order to overcome the drawback of manual modification and trial in designing the control system of missile. Specification functions wh... A method of designing robust controller based on genetic algorithm is presented in order to overcome the drawback of manual modification and trial in designing the control system of missile. Specification functions which reflect the dynamic performance in time domain and robustness in frequency domain are presented, then dynamic/static performance, control cost and robust stability are incorporated into a multi-objective optimization problem. Genetic algorithm is used to solve the problem and achieve the optimal controller directly. Simulation results show that the controller provides a good stability and offers a good dynamic performance in a large flight envelope. The results also validate the effectiveness of the method. 展开更多
关键词 controller design multi-objective optimization genetic algorithm
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联合MOGA和响应面的砂轮架主轴多目标优化设计 认领 引用 被引量：1
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作者 赵志明 薛傲元 +2 位作者 张萌 王浩宇 沈禾凯 《轻工机械》 CAS 2025年第6期8-15,共8页
为探究数控磨齿机的核心部件砂轮架主轴的静、动态特性对机床加工精度的协同影响,课题组采用ANSYS Workbench软件对磨齿机砂轮架主轴静、动态特性进行了分析与评估。以降低静态主轴前端最大变形,增加主轴系统的二阶固有频率和降低激振... 为探究数控磨齿机的核心部件砂轮架主轴的静、动态特性对机床加工精度的协同影响,课题组采用ANSYS Workbench软件对磨齿机砂轮架主轴静、动态特性进行了分析与评估。以降低静态主轴前端最大变形,增加主轴系统的二阶固有频率和降低激振力所引起的主轴前端面Z轴方向振动幅值最大值为目标,展开基于参数敏感性筛选的响应面和多目标遗传算法(Multi Objective Genetic Algorithm,MOGA)的优化设计。实验结果表明:优化后,主轴静刚度增加了29.09%,二阶固有频率和临界转速均提高了2.73%,主轴前端面Z轴方向振动幅值最大值降低了22.34%,有效提升了机床的静、动态特性和加工精度。 展开更多
关键词 磨齿机 砂轮架主轴 响应面法 多目标遗传算法 ANSYS Workbench软件
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Chaotic Genetic Algorithm-Based Forest Harvest Adjustment 认领 引用
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作者 李金铭 王梅芳 《Journal of Donghua University(English Edition)》 EI CAS 2010年第2期148-151,共4页
Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adj... Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers. 展开更多
关键词 forest harvest adjustment multi-objective planning chaotic genetic algorithm optimal model
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Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model 认领 引用
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作者 S.Muthukumaran P.Geetha E.Ramaraj 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期215-230,共16页
Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty per... Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield. 展开更多
关键词 ANN back propagation algorithm genetic algorithm multi objective particle swarm optimization algorithm
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Multi Objective Multireservoir Optimization in Fuzzy Environment for River Sub Basin Development and Management 认领 引用 被引量：7
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作者 D. G. REGULWAR P. Anand RAJ 《Journal of Water Resource and Protection》 CAS 2009年第4期271-280,共10页
In this paper, a multi objective, multireservoir operation model is proposed using Genetic algorithm (GA) under fuzzy environment. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFU-OPT) model for ... In this paper, a multi objective, multireservoir operation model is proposed using Genetic algorithm (GA) under fuzzy environment. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFU-OPT) model for the present study is developed in ‘C’ Language. The GA parameters i.e. population size, number of generations, crossover probability, and mutation probability are decided based on optimized val-ues of fitness function. The GA operators adopted are stochastic remainder selection, one point crossover and binary mutation. Initially the model is run for maximization of irrigation releases. Then the model is run for maximization of hydropower production. These objectives are fuzzified by assuming a linear membership function. These fuzzified objectives are simultaneously maximized by defining level of satisfaction (?) and then maximizing it. This approach is applied to a multireservoir system in Godavari river sub basin in Ma-harashtra State, India. Problem is formulated with 4 reservoirs and a barrage. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. This optimal operation policy so deter-mined is compared with the actual average operation policy for Jayakwadi Stage-I reservoir. 展开更多
关键词 Optimization Multi Objective Analysis Multireservoir Genetic Algorithms Fuzzy Logic Reservoir Operation
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Multi-objective route planning approach for timely searching tasks of a supervised robot 认领 引用
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作者 刘鹏 熊光明 +2 位作者 李勇 姜岩 龚建伟 《Journal of Beijing Institute of Technology》 EI CAS 2014年第4期481-489,共9页
To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planni... To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm （MOGA） for multiple objectives traveling salesman problem （MOTSP）. Then, the path between two route nodes is generated based on the heuristic path planning method A ＊. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration. 展开更多
关键词 multiple objective optimization multi-objective genetic algorithm supervised robots route planning timeliness
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Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)approaches 认领 引用 被引量：7
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作者 Behzad Elhami Asadollah Akram Majid Khanali 《Information Processing in Agriculture》 EI 2016年第3期190-205,共16页
Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related... Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)techniques.Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014-2015.The results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy consumption.Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,respectively.MOGAwas capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,respectively.Overall,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system. 展开更多
关键词 Data envelopment analysis Energy Life cycle assessment Multi objective genetic algorithm
基于响应面法和遗传算法的混凝土配合比多目标优化设计 认领 引用 被引量：5
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作者 余志刚 孙仁发 +2 位作者 刘洋 朱必洋 何倍 《建筑材料学报》 EI CAS CSCD 北大核心 2026年第1期129-139,共11页
为实现混凝土配合比多目标优化设计,通过单因素试验确定C40混凝土中矿粉掺量、粉煤灰掺量和水胶比的最佳范围;采用响应面法构建二次多项式回归模型,系统研究不同矿粉掺量、粉煤灰掺量及水胶比对混凝土坍落度和抗压强度的影响规律,并进... 为实现混凝土配合比多目标优化设计,通过单因素试验确定C40混凝土中矿粉掺量、粉煤灰掺量和水胶比的最佳范围;采用响应面法构建二次多项式回归模型,系统研究不同矿粉掺量、粉煤灰掺量及水胶比对混凝土坍落度和抗压强度的影响规律,并进一步应用非支配排序遗传算法(NSGA-Ⅱ)结合逼近理想解排序(TOPSIS)综合评价法实现混凝土配合比多目标优化设计。结果表明:通过响应面法建立的混凝土坍落度和28 d抗压强度回归模型相关系数分别为0.9447和0.9604,预测精度良好;粉煤灰掺量对坍落度的影响显著,而抗压强度主要受水胶比影响;优化后得到最优配合比方案为矿粉掺量8.66%、粉煤灰掺量25.00%、水胶比为0.34,预测值与试验值之间的相对误差小于5%。 展开更多
关键词 混凝土 响应面法 遗传算法 配合比设计 多目标优化
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