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A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm

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

摘要： In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic (PV)-wind-battery hybrid system using a novel energy filter algorithm. The main objective of this paper is to minimize the total cost of the hybrid system, while maintaining its reliability. Along with the reliability constraint, some of the important parameters, such as full utilization of complementary nature of PV and wind systems, fluctuations of power injected into the grid and the battery’s state of charge (SOC), have also been considered for the effective sizing of the hybrid system. A novel energy filter algorithm for smoothing the power injected into the grid has been proposed. To validate the proposed method, a detailed case study has been conducted. The results of the case study for different cases, with and without employing the energy filter algorithm, have been presented to demonstrate the effectiveness of the proposed sizing strategy.

关键词： PV-wind-battery hybrid system     size optimization     genetic algorithm    

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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

摘要： In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.

关键词： multi-objective     hybrid algorithm     ant colony     genetic algorithm     displacement     weighted sum method     steel braced frames    

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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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

摘要： Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

关键词： economic power dispatch (EPD)     firefly algorithm (FFA)     real genetic algorithm (RGA)     hybrid method    

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A hybrid BFA-PSO algorithm for economic dispatch with valve-point effects

T. Jayabarathi, Prateek Bahl, Harsha Ohri, Afshin Yazdani, V. Ramesh

《能源前沿（英文）》 2012年 第6卷 第2期   页码 155-163 doi: 10.1007/s11708-012-0189-7

摘要： This paper presents a novel and efficient method for solving the economic dispatch (ED) problems with valve-point effects, by integrating the biased velocity of particle swarm optimization (PSO) to the chemotaxis, swarming and reproduction steps of bacterial foraging algorithm (BFA). To include valve point effects sinusoidal terms are added to the fuel cost function. This makes the ED problems highly non-linear. In order to solve such problems the best cell (or particle) biased velocity (vector) is added to the random velocity of the BFA to reduce randomness in movement (evolution) and to increase swarming. This results in the hybrid bacterial foraging algorithm (HBFA). To demonstrate the effectiveness of the proposed HBFA method, numerical studies have been performed for three different sample systems. Comparison of the results obtained by the HBFA with the BFA and other evolutionary algorithms clearly show that the proposed method outperforms other methods in terms of convergence rate and solution quality in solving the ED problems with valve-point effects.

关键词： bacterial foraging algorithm (BFA)     economic dispatch (ED)     particle swarm optimization (PSO)     valve-point effects    

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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

摘要： In this study, we considered a bi-objective, multi-project, multi-mode resource-constrained project scheduling problem. We adopted three objective pairs as combinations of the net present value (NPV) as a financial performance measure with one of the time-based performance measures, namely, makespan ( ), mean completion time (MCT), and mean flow time (MFT) (i.e., min /max , min /max , and min /max ). We developed a hybrid non-dominated sorting genetic algorithm II (hybrid-NSGA-II) as a solution method by introducing a backward–forward pass (BFP) procedure and an injection procedure into NSGA-II. The BFP was proposed for new population generation and post-processing. Then, an injection procedure was introduced to increase diversity. The BFP and injection procedures led to improved objective functional values. The injection procedure generated a significantly high number of non-dominated solutions, thereby resulting in great diversity. An extensive computational study was performed. Results showed that hybrid-NSGA-II surpassed NSGA-II in terms of the performance metrics hypervolume, maximum spread, and the number of non-dominated solutions. Solutions were obtained for the objective pairs using hybrid-NSGA-II and three different test problem sets with specific properties. Further analysis was performed by employing cash balance, which was another financial performance measure of practical importance. Several managerial insights and extensions for further research were presented.

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    

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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

摘要： Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

关键词： photovoltaic     wind turbine     hybrid system     fuzzy logic controller     genetic algorithm     RBFNSM    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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背包问题的混合粒子群优化算法

高尚,杨静宇

《中国工程科学》 2006年 第8卷 第11期   页码 94-98

摘要：

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。

关键词： 粒子群算法     背包问题     遗传算法     变异    

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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

摘要： An economic evaluation of a network of distributed energy resources (DERs) comprising a microgrid structure of power delivery system in an Indian scenario has been made. The mathematical analysis is based on the application of tuned genetic algorithm (TGA). The analyses for optimal power operation pertaining to minimum cost have been made for two cases in Indian power delivery system. The first case deals with the consumers’ individual optimal operation of DERs, while in the second one, consumers altogether form a microgrid with the optimal supply of power from DERs. The total annual costs for these two cases are found to be economically competitive and encouraging. A reduction of approximately 5.7% in the annual cost has been obtained in the case of microgid system than that in the separately operating consumers’ system for a small locality of India. It is observed that the application of TGA results in a reduction of the minimum cost depicting an improved outcome in terms of energy economy.

关键词： distributed energy resources (DERs)     microgrid     tuned genetic algorithm (TGA)    

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

《环境科学与工程前沿（英文）》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1688-y

摘要：

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

关键词： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term memory neural network    

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Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network

Xuewei QI,Ke LI,Walter D. POTTER

《环境科学与工程前沿（英文）》 2016年 第10卷 第2期   页码 341-351 doi: 10.1007/s11783-015-0776-z

摘要： The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M?) than the current literature reported best minimum (1.923M?). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.

关键词： particle swarm optimization (PSO)     diversity control     estimation of distribution algorithm (EDA)     water distribution network (WDN)     premature convergence     hybrid strategy    

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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

摘要： In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.

关键词： micro grid (MG)     multi-objective function     artificial bee colony (ABC)     fuel cost     operation and maintenance cost    

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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

摘要：

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    

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optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid

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

摘要：

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    

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标题 作者 时间 类型 操作

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 optimal design of braced frames using hybrid genetic and ant colony optimization

Mehdi BABAEI,Ebrahim SANAEI

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

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 hybrid BFA-PSO algorithm for economic dispatch with valve-point effects

T. Jayabarathi, Prateek Bahl, Harsha Ohri, Afshin Yazdani, V. Ramesh

期刊论文

Hybrid genetic algorithm for bi-objective resource-constrained project scheduling

Fikri KUCUKSAYACIGIL, Gündüz ULUSOY

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

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

期刊论文

背包问题的混合粒子群优化算法

高尚,杨静宇

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

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

期刊论文

Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network

Xuewei QI,Ke LI,Walter D. POTTER

期刊论文

Hybrid optimization algorithm for modeling and management of micro grid connected system

Kallol ROY,Kamal Krishna MANDAL

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

optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

期刊论文