scholarly journals Single-reservoir operating rules for a year using multiobjective genetic algorithm

2008 ◽  
Vol 10 (2) ◽  
pp. 163-179 ◽  
Author(s):  
Taesoon Kim ◽  
Jun-Haeng Heo ◽  
Deg-Hyo Bae ◽  
Jin-Hoon Kim
Keyword(s):  
Genetic Algorithm ◽  
Piecewise Linear ◽  
Search Space ◽  
Optimization Method ◽  
Optimal Number ◽  
Current Status ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Operating Rules ◽  
Operating Rule

A monthly operating rule for single-reservoir operation is developed in this study. Synthetic inflow data over 100 years are generated by using a time series model, AR(1), and piecewise-linear operating rules consisting of 4 and 5 linear lines are found using the implicit stochastic optimization method. In order to consider multiobjective functions in reservoir system operation, a multiobjective genetic algorithm (NSGA-II) is adopted to obtain the optimization results. The search space of NSGA-II is carefully refined using frequency analysis of historical data, and the relationship between inflow and constraints is also investigated. It is determined that 4 and 5 segments are the optimal number of segments for the piecewise-linear operating rule, and the effect of random number seeding on NSGA-II is evaluated. Six years of historical inflow data are used for the simulation model and the results show that the developed operating rule would handle various inflow series. As a result, probabilistic reservoir storage forecasts can be provided to a system operator so as to enable the operator to evaluate the current status of a reservoir quantitatively.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Germán Ramos Ruiz ◽  
Eva Lucas Segarra ◽  
Carlos Fernández Bandera
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Search Space ◽  
Building Energy ◽  
Energy Performance ◽  
Hvac Systems ◽  
Convergence Time ◽  
Computational Time ◽  
Nsga Ii

There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II

2016 ◽  
Vol 701 ◽  
pp. 195-199 ◽  
Author(s):  
Masitah Jusop ◽  
Mohd Fadzil Faisae Ab Rashid
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Resource Constraints ◽  
Hybrid Genetic Algorithm ◽  
Assembly Line Balancing ◽  
Search Space ◽  
Line Balancing ◽  
Benchmark Problems ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm

Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

Optimizing Feature Subset and Parameters for Support Vector Machine Using Multiobjective Genetic Algorithm

2015 ◽  
Vol 24 (2) ◽  
pp. 145-160 ◽  
Author(s):  
Jyoti Ahuja ◽  
Saroj Ratnoo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Regularization Parameter ◽  
Kernel Functions ◽  
Support Vector ◽  
Model Parameters ◽  
Feature Subset ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Grid Algorithm

AbstractThe well-known classifier support vector machine has many parameters associated with its various kernel functions. The radial basis function kernel, being the most preferred kernel, has two parameters (namely, regularization parameter C and γ) to be optimized. The problem of optimizing these parameter values is called model selection in the literature, and its results strongly influence the performance of the classifier. Another factor that affects the classification performance of a classifier is the feature subset. Both these factors are interdependent and must be dealt with simultaneously. Following the multiobjective definition of feature selection, we have applied a multiobjective genetic algorithm (MOGA), NSGA II, to optimize the feature subset and model parameters simultaneously. Comparison of the proposed approach with the grid algorithm and GA-based method suggests that the MOGA-based approach performs better than the grid algorithm and is as good as the GA-based approach. Moreover, it provides multiple solutions instead of a single solution. The users can prefer one feature subset over the other as per their requirement and available resources.

scholarly journals MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS

2016 ◽  
Vol 38 ◽  
pp. 90
Author(s):  
Amarísio Da Silva Araújo ◽  
Haroldo De Campos Velho ◽  
Lu Minjiao
Keyword(s):  
Genetic Algorithm ◽  
Good Description ◽  
Synthetic Data ◽  
Search Space ◽  
Adjustment Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Integrated Biosphere Simulator ◽  
Ibis Model

Atmospheric circulation models combine different modules for a good description of the atmospheric dynamics. One of these modules is the representation of surface coverage, since the dynamics depends on the interaction between the atmosphere and the surface of the planet. However, these modules depend on a number of parameters that need to be adjusted. The parameter adjustment process is called model calibration. In this study, the IBIS (Integrated Biosphere Simulator) model is calibrated following a multi-objective strategy. The Pareto set, which embraces the non-dominated solutions in the search space of objective functions, is determined by a version of multi-objective genetic algorithm (NSGA-II). The model sensitivity to the parameters is evaluated by the Morris’ method. Synthetic data for calibration were obtained from the Tapajós National Forest (FloNa Tapajós), located near to the 67 km from Santarém-Cuiabá highway (2,51S, 54,58W).

Quantifying the robustness of optimal reservoir operation for the Xinanjiang-Fuchunjiang Reservoir Cascade

2015 ◽  
Vol 16 (1) ◽  
pp. 79-86 ◽  
Author(s):  
E. Vonk ◽  
Y. P. Xu ◽  
M. J. Booij ◽  
D. C. M. Augustijn
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Supply And Demand ◽  
Eastern China ◽  
Nsga Ii ◽  
Reservoir Performance ◽  
Operating Rules ◽  
Operation Rules ◽  
Economic Developments ◽  
The Impact

In this research we investigate the robustness of the common implicit stochastic optimization (ISO) method for dam reoperation. As a case study, we focus on the Xinanjiang-Fuchunjiang reservoir cascade in eastern China, for which adapted operating rules were proposed as a means to reduce the impact of climate change and socio-economic developments. The optimizations were based on five different water supply and demand scenarios for the future period from 2011 to 2040. Main uncertainties in the optimization can be traced back to correctness of the assumed supply and demand scenarios and the quality and tuning of the applied optimization algorithm. To investigate the robustness of proposed operation rules, we (1) compare cross-scenario performance of all obtained Pareto-optimal rulesets and (2) investigate whether different metaheuristic optimization algorithms lead to the same results. For the latter we compare the originally used genetic algorithm (Nondominated Sorting Genetic Algorithm II, NSGA-II) with a particle swarm optimization algorithm (MOPSO). Reservoir performance was measured using the shortage index (SI) and mean annual energy production (MAEP) as main indicators. It is found that optimal operating rules, tailored to a specific scenario, deliver at most 2.4% less hydropower when applied to a different scenario, while the SI increases at most with 0.28. NSGA-II and MOPSO are shown to yield approximately the same Pareto-front for all scenarios, even though small differences can be observed.

scholarly journals Structural Strength and Reliability Analysis of Important Parts of Marine Diesel Engine Turbocharger

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Lin Lei ◽  
Ming-ze Ding ◽  
Hong-wei Hu ◽  
Yun-xiao Gao ◽  
Hai-lin Xiong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Multiobjective Optimization ◽  
Turbine Blade ◽  
Structural Strength ◽  
Turbine Disk ◽  
Marine Diesel Engine ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Marine Diesel

Supercharging is the main method to improve the output power of marine diesel engines. Nowadays, most marine diesel engines use turbocharging technology, which increases the air pressure and density into the cylinder and the amount of fuel injected correspondingly so as to achieve the purpose of improving the power. In a marine diesel engine, the turbocharger has become an indispensable part. The performance of turbochargers in a harsh working environment of high temperature and high pressure for a long time will directly affect the performance of diesel engine. Based on the market feedback data from manufacturers, the failure modes of compressor impeller, turbine blade, and turbine disk of marine diesel turbocharger are analyzed, and the statistical model of random factors is established. Using DOE design, the structural strength simulation data of 46 compressors and 62 turbines are obtained, and the response surface model is constructed. On this basis, Monte Carlo sampling is carried out to analyze the reliability of the compressor and turbine. The reliability of the compressor is good, while that of the turbine disk is 0.943 and that of the turbine blade is 0.96, which still has the potential of reliability optimization space. Therefore, a multiobjective optimization method based on the NSGA-II genetic algorithm is proposed to obtain the multiobjective optimization scheme data with the reliability and processing cost of turbine disk and blade as the objective function. After optimization, the reliability of turbine disk and blade is 1, the stress value of turbine blade is optimized by 4.7941%, the stress value of turbine disk is optimized by 3.0136%, the machining cost of the turbine blade is reduced by 15.5087%, and the machining cost of turbine disk is reduced by 3.9907%. At the same time, it is verified by simulation, the data based on NSGA-II multiobjective genetic algorithm are more accurate and have practical engineering reference value. The optimized data based on NSGA-II multiobjective genetic algorithm are used to manufacture new turbine samples, and the accelerated test of simulation samples is carried out. The cycle life of the optimized turbine can reach 101,697 cycles and 118,687 cycles, which is 51.75% and 77.11% longer than that of the unoptimized turbine. It can be seen that the optimized turbine can meet the requirements of the reliability index while reducing the manufacturing cost.

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