scholarly journals Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

2018 ◽  
Vol 5 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Akshay Kumar ◽  
H K Rangavittal
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Global Optimum ◽  
Research Field ◽  
Optimization Techniques ◽  
Truss Optimization ◽  
Function Evaluation ◽  
Selection Function ◽  
Nonlinear Constraint ◽  
Discrete Area

The Genetic Algorithm is one of the advanced optimization techniques frequently used for solving complex problems in the research field, and there are plenty of parameters which affect the outcome of the GA. In this study, a 25-bar truss with the nonlinear constraint is chosen with the objective to minimize the mass and variables being the discrete area. For the same, GA parameter like Selection Function, Population Size, Crossover Function, and Creation Function are varied to find the best combination with minimum function evaluation. It is found that the Uniform selection gives the best result irrespective of the creation function, population size or crossover functions. But this is at the cost of a large number of function evaluations, and the other selection function fails to reach the global optimum and has a smaller number of function evaluation count. If the analysis of selection function is done one at a time, it is seen that all Cases performs better in Roulette but, Case A which is non-integer type with 200 population size being computationally cheaper than Case B and C of population size 300. In the Tournament selection, Case A, B with smaller population size and Case C with higher population size performs better. Case C performs better at Remainder selection with smaller population size, and Case A and B for Stochastic Uniform with higher population size. And, it is clear that the function evaluation count increases with the population size in every Case from this study.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

2019 ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Active Learning ◽  
Design Space ◽  
Particle Swarm ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Ic Engine ◽  
Weak Learner

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (μGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster, with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

scholarly journals Finding Plausible Optimal Solutions in Engineering Problems Using an Adaptive Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muslum Kilinc ◽  
Juan M. Caicedo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Global Optimum ◽  
Engineering Problem ◽  
Truss Optimization ◽  
Adaptive Genetic Algorithm ◽  
Engineering Optimization ◽  
Unique Problem ◽  
Local Optima ◽  
Engineering Problems

In engineering, optimization applications are commonly used to solve various problems. As widely known, solution of an engineering problem does not have a unique result; moreover, the solution of a unique problem may totally differ from one engineer to another. On the other hand, one of the most commonly used engineering optimization methods is genetic algorithm that leads us to only one global optimum. As to mention, engineering problems can conclude in different results from the point of different engineers’ views. In this study, a modified genetic algorithm named multi-solution genetic algorithm (MsGA) based on clustering and section approaches is presented to identify alternative solutions for an engineering problem. MsGA can identify local optima points along with global optimum and can find numerous solution alternatives. The reliability of MsGA was tested by using a Gaussian and trigonometric function. After testing, MsGA was applied to a truss optimization problem as an example of an engineering optimization problem. The result obtained shows that MsGA is successful at finding multiple plausible solutions to an engineering optima problem.

scholarly journals Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 955
Author(s):  
Zhiyuan Li ◽  
Ershuai Peng
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Network ◽  
Parameter Tuning ◽  
Global Optimum ◽  
Research Field ◽  
Edge Computing ◽  
Transmission Delay ◽  
Smart Vehicles ◽  
Network Bandwidth

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

scholarly journals Optimization of Two-Stage IPD-(1+I) Controllers for Frequency Regulation of Sustainable Energy Based Hybrid Microgrid Network

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 919
Author(s):  
Abdul Latif ◽  
S. M. Suhail Hussain ◽  
Dulal Chandra Das ◽  
Taha Selim Ustun
Keyword(s):  
Sustainable Energy ◽  
Research Field ◽  
Optimization Techniques ◽  
Frequency Regulation ◽  
Water Heater ◽  
Two Stage ◽  
Domain Specific ◽  
Hybrid Microgrid ◽  
Main Challenge ◽  
Algorithmic Technique

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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