scholarly journals Improved Genetic Algorithm with Two-Level Approximation Method for Laminate Stacking Sequence Optimization by Considering Engineering Requirements

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Haichao An ◽  
Shenyan Chen ◽  
Hai Huang
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Fitness Function ◽  
Industrial Applications ◽  
Improved Genetic Algorithm ◽  
Computational Costs ◽  
Adaptive Penalty ◽  
Practical Engineering ◽  
Adaptive Crossover ◽  
Analysis Models

Laminated composites have been widely applied in aerospace structures; thus optimization of the corresponding stacking sequences is indispensable. Genetic algorithms have been popularly adopted to cope with the design of stacking sequences which is a combinatorial optimization problem with complicated manufacturing constraints, but they often exhibit high computational costs with many structural analyses. A genetic algorithm using a two-level approximation (GATLA) method was proposed previously by the authors to obtain the optimal stacking sequences, which requires significantly low computational costs. By considering practical engineering requirements, this method possesses low applicability in complicated structures with multiple laminates. What is more, it has relatively high dependence on some genetic algorithm control parameters. To address these problems, now we propose an improved GA with two-level approximation (IGATLA) method which includes improved random initial design, adaptive penalty fitness function, adaptive crossover probability, and variable mutation probability, as well as enhanced validity check criterion for multiple laminates. The efficiency and feasibility of these improvements are verified with numerical applications, including typical numerical examples and industrial applications. It is shown that this method is also able to handle large, real world, industrial analysis models with high efficiency.

Xiangjiaba Reservoir Ecological Operation Preliminary Study Based on Genetic Algorithm

2014 ◽  
Vol 1010-1012 ◽  
pp. 1037-1041
Author(s):  
Min Qin ◽  
Yang Peng ◽  
Xian Liang Yu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Threshold Value ◽  
Lower Threshold ◽  
Ecological Environment ◽  
Improved Genetic Algorithm ◽  
Environment Impact ◽  
Jinsha River ◽  
Adaptive Crossover

Traditional reservoir scheduling guarantees efficient utilization of water resources and maximizes hydropower station operation efficiency. However it doesn’t consider the surrounding ecological environment demand. Ecological operation which is based on Traditional reservoir scheduling makes the reservoir achieve long-term, sustainable operation and reduce adverse impacts on the ecological environment. Xiangjiaba reservoir is the ties of the downstream of jinsha river and the Yangtze river which is located in jinsha river basin ecological sensitive area. So the completion of the Xiangjiaba reservoir must result in unhealthy ecological environment impact. With traditional genetic algorithm (GA) encoding complexity and being easy to fall into local convergence limitation, put forward a kind of improved convergence of genetic algorithm, the improved genetic algorithm with real number coding and fitness function which is converted into a nonlinear trigonometric function selection operator. Select the adaptive crossover probability and mutation probability adjust, in order to improve the convergence of the algorithm. RVA method is used to calculate the IHA index upper and lower threshold value and the upper and lower threshold could value as the constraint conditions of ecological scheduling objective function model. The results show that the improved genetic algorithm converge makes the global optimal solution ability stronger and faster; RVA method is suitable for the jinshajiang river and results are more comprehensive and more reasonable.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

The Attacking Dispersion Optimization in Multi Launch Rocket System Base-On Improved Genetic Algorithm

2014 ◽  
Vol 670-671 ◽  
pp. 1499-1502
Author(s):  
Wei Wang ◽  
Wei Dong Chen ◽  
Shu Qiang Zhang ◽  
Jiang Long Li ◽  
Ya En Xie
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Fitness Function ◽  
Reference Value ◽  
Weapon System ◽  
Improved Genetic Algorithm ◽  
Optimal Model ◽  
Overall Design ◽  
Mixed Coding ◽  
Rocket System

Firing dispersion of multi-launch rocket system is affected by launch sequence and firing interval significantly. Firing order and firing interval of the existing multi launch rocket system (MLRS) are optimized to improve the firing performance of the existing weapon system without changing the overall design of the weapon system. On one hand, based on optimization problem, the firing dispersion optimal model is established and the genetic algorithm is improved therefore, a sequence of mixed coding genetic algorithm is designed. On the other hand, simulation optimization of firing dispersion has been finished by the aid of fitness function which is based on the optimal model. Meanwhile, it testifies this algorithm’s validity and the simulation results can provide a certain reference value for engineering experiment.

Blower Resource Scheduling Based on Elite Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 591-595 ◽  
Author(s):  
Guang Nian Yang ◽  
Wei Qi ◽  
Jun Zhou
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Sewage Treatment ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Toxic Substances ◽  
Improved Genetic Algorithm ◽  
Grid Resource

Now, our sewage treatment industry mainly depends on the blower of aeration act as metabolic, absorbed in the toxic substances. Blower resources management is the key issue of sewage treatment. Traditional resource scheduling algorithm exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

Blade Arrangement Optimization for Mistuned Bladed Disk Based on Gaussian Process Regression and Genetic Algorithm

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Wangbai Pan ◽  
Meiyan Zhang ◽  
Guoan Tang
Keyword(s):  
Genetic Algorithm ◽  
Gaussian Process ◽  
High Efficiency ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Optimal Arrangement ◽  
Bladed Disk ◽  
Gp Model

Abstract Mistuning phenomena exist in the bladed disk due to the inevitable deviations among blades' properties, e.g., stiffness, mass, geometry, etc., leading to localization and response amplification. The dynamic performance of mistuned bladed disk is sensitive to the arrangement of blades. The blade arrangement optimization aims to obtain the optimal arrangement that minimizes the influence of mistuning. In this paper, a framework of high efficiency is raised to deal with the challenge of high computational cost this optimization. It comprehensively utilizes mixed-dimensional finite element model (MDFEM), Gaussian process (GP) regression, and genetic algorithm (GA). The MDFEM can perform mistuned modal analysis efficiently and provides the training set of GP regression rapidly. The GP model, as a surrogate model, predicts the desired dynamic performance directly without calculating the numerical model and can function as fitness function in optimization. GA has the capability to deal with combinatorial problems and is a good option for problems with large search domains and several local maxima/minima. The techniques and processes of three methods are illustrated in detail. Case studies, based on a real turbine, are concretely presented in a gradually progressive manner to test and verify the effectiveness, accuracy, and efficiency of methods and entire framework step by step. The results show the satisfactory optimal arrangement for a randomly chosen set of mistuned blades, and the influence of mistuning is reduced indeed. The time cost of the optimization has been reduced several orders of magnitude. This framework can be a promising approach for the blade arrangement optimization problem.

Application of Improved Genetic Algorithm to Traffic Equipment’s Rush-Repair in the Wartime

2012 ◽  
Vol 605-607 ◽  
pp. 49-52
Author(s):  
Geng Sheng Wu ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Assignment Model ◽  
Practical Engineering ◽  
Poor Stability

Traffic equipment’s rush-repairs in the wartime optimal assignment model was established. Combining the features of Job-shop scheduling problems, described the complexity of this problem. In order to find global optimal results efficiently, traditional GAs were improved and used for study of this problem. Though genetic algorithm, as an effective global search method, had been used in many problems, it had the disadvantages of slow convergence and poor stability in practical engineering. In order to overcome these problems, an improved genetic algorithm was proposed in terms of creation of the initial population, genetic operators, etc. At the end, the steps to solve the optimal model were put forward. With this model we had obtained ideal results. This shows that the method can offer a scientific and effective support for a decision maker in command automation of the traffic equipment’s rush-repairs in battlefield.

scholarly journals Improved Genetic Algorithm Tuning Controller Design for Autonomous Hovercraft

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 66 ◽  
Author(s):  
Huu Khoa Tran ◽  
Hoang Hai Son ◽  
Phan Van Duc ◽  
Tran Thanh Trang ◽  
Hoang-Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Fitness Function ◽  
Tracking Error ◽  
Biological Evolution ◽  
Operation Time ◽  
Design Criterion ◽  
Improved Genetic Algorithm ◽  
Fuzzy Pid Control ◽  
Speed Up

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time by neglecting chromosome decoding step is proposed to find the optimized fuzzy-proportional-integral-derivative (fuzzy-PID) control parameters. Due to minimizing tracking error of the controller design criterion, the fitness function integral of square error (ISE) was employed to utilize the advantages of the modified GA. The proposed method was then applied to a novel autonomous hovercraft motion model to display the superiority to the standard GA.

Study of Multi-Project Conflicts by IGA Based on the Improvement of Fitness Function

2013 ◽  
Vol 756-759 ◽  
pp. 2768-2773
Author(s):  
Zhi Feng Lv ◽  
Xiang Dong Ma
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Selection Methods ◽  
Improved Genetic Algorithm ◽  
Resource Conflicts ◽  
Simple Genetic Algorithm ◽  
The Family ◽  
Family Orientation ◽  
Standard Genetic Algorithm ◽  
Mutation And Selection

In the multi-project resource conflicts exist in the application of standard genetic algorithm fitness function exist "premature" problem, Genetic algorithm can not find the convergence of these issue. Based on the above issues ,an improved genetic algorithm (IGA) are appropriate, From the fitness function, mutation and selection methods to improve two aspects are described, the Improved genetic algorithm for simple genetic algorithm has the advantage of generations of each evolution, offspring parent always retains the best individual to the "high-fitness model for the ancestors of the family orientation" search out better samples, and verified through experiments the effectiveness of the algorithm

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