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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 被引量:27
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa... The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm (NSGA)-II
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Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm 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 被引量:7
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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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Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process 被引量:1
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作者 Abdelouahhab Jabri Abdellah El Barkany Ahmed El Khalfi 《Engineering(科研)》 2013年第7期601-610,共10页
In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objecti... In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic;this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique. 展开更多
关键词 genetic algorithms Mutli-objective optimization TURNING Process MACHINING
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A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation 被引量:3
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作者 李国丽 宋钢 +2 位作者 吴宜灿 张建 王群京 《Plasma Science and Technology》 SCIE EI CAS CSCD 2006年第2期234-236,共3页
A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated an... A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach. 展开更多
关键词 inverse planning multi-objective optimization genetic algorithm HYBRID
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Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model
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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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Multiple-Objective Optimization and Design of Series-Parallel Systems Using Novel Hybrid Genetic Algorithm Meta-Heuristic Approach
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作者 Essa Abrahim Abdulgader Saleem Thien-My Dao Zhaoheng Liu 《World Journal of Engineering and Technology》 2018年第3期532-555,共24页
In this study, we develop a new meta-heuristic-based approach to solve a multi-objective optimization problem, namely the reliability-redundancy allocation problem (RRAP). Further, we develop a new simulation process ... In this study, we develop a new meta-heuristic-based approach to solve a multi-objective optimization problem, namely the reliability-redundancy allocation problem (RRAP). Further, we develop a new simulation process to generate practical tools for designing reliable series-parallel systems. Because the?RRAP is an NP-hard problem, conventional techniques or heuristics cannot be used to find the optimal solution. We propose a genetic algorithm (GA)-based hybrid meta-heuristic algorithm, namely the hybrid genetic algorithm (HGA), to find the optimal solution. A simulation process based on the HGA is developed to obtain different alternative solutions that are required to generate application tools for optimal design of reliable series-parallel systems. Finally, a practical case study regarding security control of a gas turbine in the overspeed state is presented to validate the proposed algorithm. 展开更多
关键词 multi-objective optimization Reliability-Redundancy ALLOCATION OVERSPEED Gas TURBINE Hybrid genetic algorithm
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Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction
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作者 Jing Zhang Baocheng Ding 《Journal of Applied Mathematics and Physics》 2023年第6期1806-1815,共10页
To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigera... To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. . 展开更多
关键词 Cold Chain Logistics Customer Satisfaction Elitist Non-Dominated Sorting genetic algorithm multi-objective optimization
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Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam
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作者 Ali Khazaee Hossein Miar Naimi 《Engineering(科研)》 2011年第10期1054-1061,共8页
Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multipl... Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented. 展开更多
关键词 genetic algorithm multi-objective I-BEAM optimization
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A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN
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作者 Dac-Nhuong Le Nhu Gia Nguyen +1 位作者 Dac Binh Ha Vinh Trong Le 《Communications and Network》 2013年第3期223-231,共9页
A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and... A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem. 展开更多
关键词 multi-objectives optimal NEXT Generation Wireless NETWORK NETWORK Design Capacity Planning genetic algorithm Two-populations
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Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV 被引量:3
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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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Overview of multi-objective optimization methods 被引量:2
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作者 LeiXiujuan ShiZhongke 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 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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SELECTION OF OBJECTIVE FUNCTIONS AND APPLICATION OF GENETIC ALGORITHMS IN DAMPING DESIGN OF PIPE SYSTEM 被引量:1
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作者 ChenYanqiu FanQinsban ZhuZigen 《Acta Mechanica Solida Sinica》 SCIE EI 2003年第2期171-178,共8页
The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functio... The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functions for the vibration design of a pipeline or pipe system are introduced,namely,the frequency,amplitude,transfer ratio,curvature and deformation energy as options for the optimization process.The genetic algorithms(GA)are adopted as the opti- mization method,in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial.The optimization procedure for all the above ob- jective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN. The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example,and their characteristics are analyzed.Finally we put forward two new objective functions,curvature and deformation energy for pipe system optimization.The calculations show that using the curvature as the objective function can reflect the case of minimal stress,and the optimization results using the deformation energy represent lesser and more uni- form stress distribution.The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement. 展开更多
关键词 objective function genetic algorithms optimization pipe system
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Multi-objective Optimization Conceptual Design of Product Structure Based on Variable Length Gene Expression 被引量:6
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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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A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions 被引量:4
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作者 YANG Yun WU Jianfeng +2 位作者 SUN Xiaomin LIN Jin WU Jichun 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2012年第1期246-255,共10页
In this paper, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under va... In this paper, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA, the niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources. 展开更多
关键词 seawater intrusion multi-objective optimization niched Pareto tabu search combined with genetic algorithm niched Pareto tabu search genetic algorithm
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Emission-Reductive and Multi-Objective Coordinative Optimization of Binary Feed for Atmospheric and Vacuum Distillation Unit 被引量:3
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作者 Huang Xiaoqiao Zhao Tianlong +3 位作者 Li Na Ma Zhanhua Song Lijuan Li Jun 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS 2017年第4期101-112,共12页
A genetic algorithm based multi-objective coordinative optimization strategy is developed to optimize the operation of a binary feed atmospheric and vacuum distillation system, in which the objective functions cover t... A genetic algorithm based multi-objective coordinative optimization strategy is developed to optimize the operation of a binary feed atmospheric and vacuum distillation system, in which the objective functions cover the economic benefit, the furnace energy consumption and the CO_2 emissions, and meanwhile the simultaneous effect of binary feed composition is also investigated. A cross-call integration of software is developed to implement the optimization algorithm,and once the maximum economic benefit, the minimum furnace energy consumption and the minimum CO_2 emissions are obtained, the Pareto-optimal solution set is worked out, with the practical problems of the refinery being solved. The optimization result shows that under the same furnace energy consumption and the CO_2 emissions as the existing working condition, the economic benefit still allows for a considerable potential of increment by adjusting the heavy oil proportion of the binary feed crude oil. 展开更多
关键词 multi-objective optimization atmospheric and vacuum DISTILLATION system genetic algorithm CO2 emissions BINARY FEED composition
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Three-Objective Programming with Continuous Variable Genetic Algorithm
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作者 Adugna Fita 《Applied Mathematics》 2014年第21期3297-3310,共14页
The subject area of multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually, there does not exist a single solution that optimizes all f... The subject area of multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually, there does not exist a single solution that optimizes all functions simultaneously;quite the contrary, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision made by taking elements of nondominated set as alternatives, which is given by analysts. Since it is important for the decision maker to obtain as much information as possible about this set, our research objective is to determine a well-defined and meaningful approximation of the solution set for linear and nonlinear three objective optimization problems. In this paper a continuous variable genetic algorithm is used to find approximate near optimal solution set. Objective functions are considered as fitness function without modification. Initial solution was generated within box constraint and solutions will be kept in feasible region during mutation and recombination. 展开更多
关键词 CHROMOSOME CROSSOVER HEURISTICS Mutation optimization Population Ranking genetic algorithms multi-objective PARETO optimal Solutions PARENT Selection
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Multi Objective Multireservoir Optimization in Fuzzy Environment for River Sub Basin Development and Management 被引量:6
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作者 D. G. REGULWAR P. Anand RAJ 《Journal of Water Resource and Protection》 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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Optimal Operation of Multi-Objective Hydropower Reservoir with Ecology Consideration 被引量:1
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作者 Xuewen Wu Xianfeng Huang +1 位作者 Guohua Fang Fei Kong 《Journal of Water Resource and Protection》 2011年第12期904-911,共8页
Aiming at the problem that traditional optimal operation of hydropower reservoir pays little attention to ecology, an optimal operation model of multi-objective hydropower reservoir with ecology consideration is estab... Aiming at the problem that traditional optimal operation of hydropower reservoir pays little attention to ecology, an optimal operation model of multi-objective hydropower reservoir with ecology consideration is established which combines the ecology and power generation. The model takes the maximum annual power generation benefit, the maximum output of the minimal output stage in the year and the minimum shortage of ecological water demand as objectives, and water quantity balance of reservoir, reservoir storage, discharge flow, output and so on as constraints. Chaotic genetic arithmetic is developed to solve the optimal model. An example is studied, showing that the annual generation of the proposed model is 8 million kW?h less than that model without ecology consideration, which is about 0.28 percent. But the proposed model is in favor of river ecology protection, and can promote the sustainable utilization of water resources. So it is worthy and necessary for the optimal operation of hydropower reservoir with ecology consideration. 展开更多
关键词 HYDROPOWER Station RESERVOIR optimal Operation chaotic genetic algorithm ECOLOGY
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Optimal Seat and Suspension Design for a Half-Car with Driver Model Using Genetic Algorithm 被引量:3
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作者 Wael Abbas Ashraf Emam +2 位作者 Saeed Badran Mohamed Shebl Ossama Abouelatta 《Intelligent Control and Automation》 2013年第2期199-205,共7页
This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters... This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems. 展开更多
关键词 Biodynamic Response genetic algorithms multi-objective optimization MATHEMATICAL Model
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