Multi-point optimization of transonic airfoils using an enhanced genetic algorithm

2017 ◽  
Vol 232 (7) ◽  
pp. 1347-1360 ◽  
Author(s):  
Narjes Timnak ◽  
Alireza Jahangirian
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fitness Function ◽  
Single Point ◽  
Navier Stokes ◽  
Computational Time ◽  
Adaptive Mutation ◽  
Function Evaluation ◽  
New Techniques ◽  
New Methods

In this study, two new techniques are proposed for accelerating the multi-point optimization of an airfoil shape by genetic algorithms. In such multi-point evolutionary optimization, the objective function has to be evaluated several times more than a single-point optimization. Thus, excessive computational time is crucial in these problems particularly, when computational fluid dynamics is used for fitness function evaluation. Two new techniques of preadaptive range operator and adaptive mutation rate are proposed. An unstructured grid Navier–Stokes flow solver with a two-equation [Formula: see text] turbulence model is used to evaluate the objective function. The new methods are applied for optimum design of a transonic airfoil at two speed conditions. The results show that using the new methods can increase the aerodynamic efficiency of optimum airfoil at each operating condition with about 30% less computational time in comparison with the conventional genetic algorithm approach.

The Optimal Design on Connection Order of Network in Hydraulic Manifold Block

2011 ◽  
Vol 201-203 ◽  
pp. 2190-2194
Author(s):  
Jun Jun Zhang ◽  
Ji Sheng Wang ◽  
Jiang Yong Wang ◽  
Gang Liu ◽  
Jie Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Genetic Manipulation ◽  
Optimal Algorithm ◽  
Single Point ◽  
Adaptive Mutation ◽  
Gene Encoding ◽  
Mutation Strategy ◽  
The Common

As one of the important questions in the design of hydraulic manifold block — connection order of network, give a solution based on genetic algorithm. Genetic algorithm is the common effective intelligent optimal algorithm and suitable for solving a large combinatorial optimal problems. Gene encoding of ordinal representation, single-point crossover strategy and adaptive mutation strategy are used in the design of genetic manipulation.

scholarly journals Using computation to enhance diagnosis and therapy: a novel 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 ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative 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 Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

scholarly journals Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1297
Author(s):  
Md. Shabiul Islam ◽  
Most Tahamina Khatoon ◽  
Kazy Noor-e-Alam Siddiquee ◽  
Wong Hin Yong ◽  
Mohammad Nurul Huda
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Time ◽  
Large Scale ◽  
Fitness Function ◽  
Random Search ◽  
Linear Equations ◽  
Simultaneous Equations ◽  
Function Evaluation ◽  
Solution Sets

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

Genetic Algorithm (GA) and Internal Energy Transfer Calculations Using Neural Network (NN) Methods

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Objective Function ◽  
Fitness Function ◽  
Solution Space ◽  
Large Ensemble ◽  
Nonlinear Functional ◽  
Squared Error ◽  
Time Required

Genetic algorithms (GA), like NNs, can be used to fit highly nonlinear functional forms, such as empirical interatomic potentials from a large ensemble of data. Briefly, a genetic algorithm uses a stochastic global search method that mimics the process of natural biological evolution. GAs operate on a population of potential solutions applying the principle of survival of the fittest to generate progressively better approximations to a solution. A new set of approximations is generated in each iteration (also known as generation) of a GA through the process of selecting individuals from the solution space according to their fitness levels, and breeding them together using operators borrowed from natural genetics. This process leads to the evolution of populations of individuals that have a higher probability of being “fitter,” i.e., better approximations of the specified potential values, than the individuals they were created from, just as in natural adaptation. The most time-consuming part in implementing a GA is often the evaluation of the objective or the fitness function. The objective function O[P] is expressed as sum squared error computed over a given large ensemble of data. Consequently, the time required for evaluating the objective function becomes an important factor. Since a GA is well suited for implementing on parallel computers, the time required for evaluating the objective function can be reduced significantly by parallel processing. A better approach would be to map out the objective function using several possible solutions concurrently or beforehand to improve computational efficiency of the GA prior to its execution, and using this information to implement the GA. This will obviate the need for cumbersome direct evaluation of the objective function. Neural networks may be best suited to map the functional relationship between the objective function and the various parameters of the specific functional form. This study presents an approach that combines the universal function approximation capability of multilayer neural networks to accelerate a GA for fitting atomic system potentials. The approach involves evaluating the objective function, which for the present application is the mean squared error (MSE) between the computed and model-estimated potential, and training a multilayer neural network with decision variables as input and the objective function as output.

Modeling of the time-dependent multi-skilled RCPSP considering learning effect

2019 ◽  
Vol 14 (2) ◽  
pp. 521-558 ◽  
Author(s):  
Amir Hossein Hosseinian ◽  
Vahid Baradaran ◽  
Mahdi Bashiri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Learning Effect ◽  
The Other ◽  
Time Dependent ◽  
Computational Time ◽  
Nsga Ii ◽  
Content Type ◽  
Proposed Model ◽  
Objective Function Values

Purpose The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously. Design/methodology/approach The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method. Findings Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values. Practical implications The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects. Originality/value Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 961-966
Author(s):  
Fei Xiang ◽  
Shan Li
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Adaptive Mutation ◽  
System Response ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Design Scheme

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.

scholarly journals Analyzing student performance using evolutionary artificial neural network algorithm

2018 ◽  
Vol 7 (2.26) ◽  
pp. 67 ◽  
Author(s):  
A S. Arunachalam ◽  
T Velmurugan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Student Performance ◽  
Fitness Function ◽  
Research Work ◽  
Function Evaluation ◽  
Success Factor ◽  
Optimization Approach ◽  
Artificial Neural

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student’s success factor. The ability to predict a student’s performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

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