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Hybrid genetic algorithm for bi-objective resource-constrained project scheduling
Fikri KUCUKSAYACIGIL, Gündüz ULUSOY
《工程管理前沿(英文)》 2020年 第7卷 第3期 页码 426-446 doi: 10.1007/s42524-020-0100-x
关键词: backward–forward scheduling hybrid bi-objective genetic algorithm injection procedure maximum cash balance multi-objective multi-project multi-mode resource-constrained project scheduling problem
Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization
Mehdi BABAEI,Ebrahim SANAEI
《结构与土木工程前沿(英文)》 2016年 第10卷 第4期 页码 472-480 doi: 10.1007/s11709-016-0368-4
关键词: multi-objective hybrid algorithm ant colony genetic algorithm displacement weighted sum method steel braced frames
A new technique for solving the multi-objective optimization problem using hybrid approach
Mimoun YOUNES,Khodja FOUAD,Belabbes BAGDAD
《能源前沿(英文)》 2014年 第8卷 第4期 页码 490-503 doi: 10.1007/s11708-014-0311-0
关键词: economic power dispatch (EPD) firefly algorithm (FFA) real genetic algorithm (RGA) hybrid method
Aeidapu MAHESH, Kanwarjit Singh SANDHU
《能源前沿(英文)》 2020年 第14卷 第1期 页码 139-151 doi: 10.1007/s11708-017-0484-4
关键词: PV-wind-battery hybrid system size optimization genetic algorithm
Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH
《机械工程前沿(英文)》 2013年 第8卷 第3期 页码 319-332 doi: 10.1007/s11465-013-0269-3
The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (Ra). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.
关键词: hybrid machining processes (HMPs) electrical discharge diamond grinding (EDDG) artificial neural network (ANN) genetic algorithm modeling and optimization
Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey
《机械工程前沿(英文)》 2014年 第9卷 第1期 页码 81-94 doi: 10.1007/s11465-014-0287-9
The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NOx, unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NOx, HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi-objective optimization problem is formulated. Non-dominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.
关键词: jatropha biodiesel fuel properties response surface methodology multi-objective optimization non-dominated sorting genetic algorithm-II
《结构与土木工程前沿(英文)》 页码 812-826 doi: 10.1007/s11709-023-0940-7
关键词: falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms
Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm
Hongbo REN, Yinlong LU, Qiong WU, Xiu YANG, Aolin ZHOU
《能源前沿(英文)》 2018年 第12卷 第4期 页码 518-528 doi: 10.1007/s11708-018-0594-7
In this paper, a multi-objective optimization model is established for the investment plan and operation management of a hybrid distributed energy system. Considering both economic and environmental benefits, the overall annual cost and emissions of CO2 equivalents are selected as the objective functions to be minimized. In addition, relevant constraints are included to guarantee that the optimized system is reliable to satisfy the energy demands. To solve the optimization model, the non-dominated sorting generic algorithm II (NSGA-II) is employed to derive a set of non-dominated Pareto solutions. The diversity of Pareto solutions is conserved by a crowding distance operator, and the best compromised Pareto solution is determined based on the fuzzy set theory. As an illustrative example, a hotel building is selected for study to verify the effectiveness of the optimization model and the solving algorithm. The results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto solutions and the fuzzy set theory picks out a better combination of device capacities with reasonable operating strategies.
关键词: multi-objective optimization hybrid distributed energy system non-dominated sorting generic algorithm II fuzzy set theory Pareto optimal solution
Economic analysis of a hybrid solar-fuel cell power delivery system using tuned genetic algorithm
Trina SOM, Niladri CHAKRABORTY
《能源前沿(英文)》 2012年 第6卷 第1期 页码 12-20 doi: 10.1007/s11708-012-0172-3
关键词: distributed energy resources (DERs) microgrid tuned genetic algorithm (TGA)
Xin LI,Jin SUN,Fu XIAO,Jiang-shan TIAN
《信息与电子工程前沿(英文)》 2016年 第17卷 第2期 页码 160-172 doi: 10.1631/FITEE.1500168
随着收缩技术的发展,工艺,电压和温度(PVT)参数的可变性显着影响了芯片设计的成品率分析和优化。先前的产量估计算法已经限于预测时序或功率产量。但是,忽略功率和延迟之间的相关性将导致明显的产量损失。这些方法中的大多数都还具有较高的计算复杂度和较长的运行时间。我们提出了一种基于Chebyshev仿射算术(CAA)和自适应加权和(AWS)方法的新型双目标优化框架,在该框架中将功率和时序收益两者均设置为目标函数。同时优化两个目标以保持它们之间的相关性。所提出的方法首先在任意相关性的假设下预测泄漏和延迟分布的保证概率边界。然后,通过计算累积分布函数(CDF)边界来建立功率延迟双目标优化模型。最后,将AWS方法应用于功率延迟优化,以生成分布良好的一组Pareto最优解。在ISCAS基准电路上的实验结果表明,该双目标框架能够在功率和时序产量之间提供足够的权衡信息。
Multi-objective optimization of molten carbonate fuel cell system for reducing CO
Ramin ROSHANDEL,Majid ASTANEH,Farzin GOLZAR
《能源前沿(英文)》 2015年 第9卷 第1期 页码 106-114 doi: 10.1007/s11708-014-0341-7
关键词: molten carbonate fuel cell (MCFC) multi-objective optimization (MOO) Pareto curve genetic algorithm CO2 separation
高尚,杨静宇
《中国工程科学》 2006年 第8卷 第11期 页码 94-98
经典的粒子群是一个有效的寻找连续函数极值的方法,结合遗传算法的思想提出的混合粒子群算法来解决背包问题,经过比较测试,6种混合粒子群算法的效果都比较好,特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法,并成功地运用在投资问题中。对于目前还没有好的解法的组合优化问题,很容易地修改此算法就可解决。
Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and
Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN
《能源前沿(英文)》 2019年 第13卷 第1期 页码 131-148 doi: 10.1007/s11708-017-0446-x
关键词: photovoltaic wind turbine hybrid system fuzzy logic controller genetic algorithm RBFNSM
Pengxing YI,Lijian DONG,Tielin SHI
《机械工程前沿(英文)》 2014年 第9卷 第4期 页码 354-367 doi: 10.1007/s11465-014-0319-5
To improve the dynamic performance and reduce the weight of the planet carrier in wind turbine gearbox, a multi-objective optimization method, which is driven by the maximum deformation, the maximum stress and the minimum mass of the studied part, is proposed by combining the response surface method and genetic algorithms in this paper. Firstly, the design points’ distribution for the design variables of the planet carrier is established with the central composite design (CCD) method. Then, based on the computing results of finite element analysis (FEA), the response surface analysis is conducted to find out the proper sets of design variable values. And a multi-objective genetic algorithm (MOGA) is applied to determine the direction of optimization. As well, this method is applied to design and optimize the planet carrier in a 1.5 MW wind turbine gearbox, the results of which are validated by an experimental modal test. Compared with the original design, the mass and the stress of the optimized planet carrier are respectively reduced by 9.3% and 40%. Consequently, the cost of planet carrier is greatly reduced and its stability is also improved.
关键词: planet carrier multi-objective optimization genetic algorithms wind turbine gearbox modal experiment
Hybrid optimization algorithm for modeling and management of micro grid connected system
Kallol ROY,Kamal Krishna MANDAL
《能源前沿(英文)》 2014年 第8卷 第3期 页码 305-314 doi: 10.1007/s11708-014-0308-8
关键词: micro grid (MG) multi-objective function artificial bee colony (ABC) fuel cost operation and maintenance cost
标题 作者 时间 类型 操作
Hybrid genetic algorithm for bi-objective resource-constrained project scheduling
Fikri KUCUKSAYACIGIL, Gündüz ULUSOY
期刊论文
Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization
Mehdi BABAEI,Ebrahim SANAEI
期刊论文
A new technique for solving the multi-objective optimization problem using hybrid approach
Mimoun YOUNES,Khodja FOUAD,Belabbes BAGDAD
期刊论文
A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm
Aeidapu MAHESH, Kanwarjit Singh SANDHU
期刊论文
Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based onANN-NSGA-II hybrid technique
Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH
期刊论文
Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodieselengine using Non-dominated sorting genetic algorithm-II
Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey
期刊论文
Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive
期刊论文
Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm
Hongbo REN, Yinlong LU, Qiong WU, Xiu YANG, Aolin ZHOU
期刊论文
Economic analysis of a hybrid solar-fuel cell power delivery system using tuned genetic algorithm
Trina SOM, Niladri CHAKRABORTY
期刊论文
Multi-objective optimization of molten carbonate fuel cell system for reducing CO
Ramin ROSHANDEL,Majid ASTANEH,Farzin GOLZAR
期刊论文
Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and
Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN
期刊论文
Multi-objective genetic algorithms based structural optimization and experimental investigation of the
Pengxing YI,Lijian DONG,Tielin SHI
期刊论文