scholarly journals Distant Relative Genetic Algorithm–Based Structural Reliability Optimization

2021 ◽  
Vol 9 ◽  
Author(s):  
Hu Cheng ◽  
Xin-Chi Yan ◽  
Li Fu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Structural Reliability ◽  
Safety Margin ◽  
Optimal Solution ◽  
Reliability Optimization ◽  
Search Performance ◽  
Explicit Equation ◽  
Local Optimal Solution ◽  
Distant Relative

In this study, safety margin explicit equation has been established using random variables (i.e., the engineering conditions, structure parameters, structural strength, and external load), and the genetic algorithm (GA)–based structural reliability optimization design has been addressed subsequently. Though the conventional adaptive GA can change automatically with fitness, it is still not unsatisfactory in sufficiently improving the algorithm convergence speed, especially for complex structures. This article presents an improved adaptive technology termed as the distant relative genetic algorithm (DRGA), in which the distant relative pointer and immunity operators can effectively improve the search performance of the GA. In early evolution, by means of cross controlling and avoiding pairing between individuals with the same genes, the methodology prevents the isogenic individuals expanding locally. Besides, the revised algorithm is able to jump out of the local optimal solution, thus ensuring the realization of a fast global convergence. An example based on wing box structure optimization has been demonstrated using the improved method, and the calculation results show that this strategy makes the GA more effective in dealing with the constraint optimization issues.

Ship Cabin Layout Optimization Design Based on the Improved Genetic Algorithm Method

2013 ◽  
Vol 300-301 ◽  
pp. 146-149 ◽  
Author(s):  
Yun Long Wang ◽  
Chen Wang ◽  
Yan Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
The Other ◽  
Improved Genetic Algorithm ◽  
Local Optimal Solution ◽  
Genetic Algorithm Method ◽  
Np Hard Problem ◽  
The One

Based on the improved genetic algorithm method, a kind of the optimization techniques to solve the problem about the ship cabin layout is presented. The problem about the ship cabin layout is a NP-hard problem. This article has used the genetic algorithm method to solve it .However, for the simple and easy procedure, the basic genetic algorithm is slow and easy to fall into a local optimal solution. Therefore, it must be improved. This article has made the following two improvements: on the one hand using the niche method to solve the multi- peak problem; on the other hand using the climbing method to solve the slow and premature convergence. The simulation tests show that this approach proposed by authors is feasible and valid and the result is satisfied.

An Improved Cloud Adaptive Genetic Algorithm Based on Cloud Computing for Active Optimization Calculation

2015 ◽  
Vol 713-715 ◽  
pp. 1579-1582
Author(s):  
Shao Min Zhang ◽  
Ze Wu ◽  
Bao Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Load Optimization ◽  
Active Load ◽  
Optimization Calculation ◽  
Local Optimal Solution ◽  
Optimization Allocation

Under the background of huge amounts of data in large-scale power grid, the active power optimization calculation is easy to fall into local optimal solution, and meanwhile the calculation demands a higher processing speed. Aiming at these questions, the farmer fishing algorithm which is applied to solve the problem of optimal distribution of active load for coal-fired power units is used to improve the cloud adaptive genetic algorithm (CAGA) for speeding up the convergence phase of CAGA. The concept of cloud computing algorithm is introduced, and parallel design has been done through MapReduce graphs. This method speeds up the calculation and improves the effectiveness of the active load optimization allocation calculation.

Application of a new local effective constrained response surface method in structural reliability optimization design

2019 ◽  
Vol 36 (3) ◽  
pp. 1055-1078 ◽  
Author(s):  
Hailiang Su ◽  
Fengchong Lan ◽  
Yuyan He ◽  
Jiqing Chen
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Optimization Design ◽  
Structural Reliability ◽  
Reliability Optimization ◽  
Content Type ◽  
Meta Model ◽  
Proxy Model ◽  
Surface Method ◽  
Sampling Points

Purpose Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model. Design/methodology/approach The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated. Findings The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations. Originality/value This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm.

Design of 3D-Route Planning Based on the Improvement of Simple Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1690-1694
Author(s):  
Jian Xia Zhang ◽  
Tao Yu ◽  
Ji Ping Chen ◽  
Ying Hao Lin ◽  
Yu Meng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Scientific Research ◽  
Route Planning ◽  
Optimal Solution ◽  
Global Search ◽  
Global Search Algorithm ◽  
Simple Genetic Algorithm ◽  
Local Optimal Solution ◽  
Simulation Results

With the wide application of UAV in the scientific research,its route planning is becoming more and more important. In order to design the best route planning when UAV operates in the field, this paper mainly puts to use the simple genetic algorithm to design 3D-route planning. It primarily introduces the advantages of genetic algorithm compared to others on the designing of route planning. The improvement of simple genetic algorithm is because of the safety of UAV when it flights higher, and the 3D-route planning should include all the corresponding areas. The simulation results show that: the improvement of simple genetic algorithm gets rid of the dependence of parameters, at the same time it is a global search algorithm to avoid falling into the local optimal solution. Whats more, it can meet the requirements of the 3D-route planning design, to the purpose of regional scope and high safety.

Structural Reliability Optimization Design Based on Artificial Neural Network

2013 ◽  
Vol 834-836 ◽  
pp. 1877-1880
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Economic Benefit ◽  
Optimization Design ◽  
Structural Reliability ◽  
Minimum Weight ◽  
Reliability Optimization ◽  
Fatigue Reliability ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization design for considering fatigue reliability requirements on structure was proposed in this paper. The ANN-based response surface method was used to analysis fatigue reliability of the structure. The fatigue reliability requirements were taken as constraints while the structural weight as the objective function, the ANN model was performed to simulate the relationship between the fatigue reliability and geometry dimension of the structure, the optimization result of the structure with a minimum weight was obtained, thus can make economic benefit meanwhile ensure the safety of the structure.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

2014 ◽  
Vol 538 ◽  
pp. 193-197
Author(s):  
Jian Jiang Su ◽  
Chao Che ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Layout Design ◽  
Adaptive Genetic Algorithm ◽  
Local Optimality ◽  
Mutation Probability ◽  
Evolutionary Genetic ◽  
Local Optimal Solution ◽  
The Moment ◽  
Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

The Application of Improved Genetic Algorithms in Computer Network Reliability Optimization

2013 ◽  
Vol 765-767 ◽  
pp. 1564-1567
Author(s):  
Li Yan Wang ◽  
Xian Feng Zheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Computer Network ◽  
Network Reliability ◽  
Reliability Optimization ◽  
Improved Genetic Algorithm ◽  
Algorithm Optimization ◽  
Network Link ◽  
Link Cost ◽  
Medium Cost

To decrease network link cost is one of the most important pairs for computer network reliability optimization design. In order to make the network cost minimum, the network link medium cost, mathematics model of reliability and resolve algorithm must be considered when it is designed. In the paper, we present an improved genetic algorithm used in optimization calculation for minimizing total link cost and increasing the reliability of the network. Executive procedures of the improved algorithm are described. The simulation results show that improved genetic algorithm provides a method to resolves the network reliability optimization problem which normal approach can not resolve. By using the maneuverable algorithm, optimization process is accelerated, efficiency is increased.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

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