scholarly journals Multiobjective Optimization of Carbon Fiber-Reinforced Plastic Composite Bumper Based on Adaptive Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Dawei Gao ◽  
Haotian Liang ◽  
Guijie Shi ◽  
Liqin Cao
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Plastic Composite ◽  
Mutation Operators ◽  
Adaptive Crossover

Genetic algorithm (GA) is a common optimization technique that has two fatal limitations: low convergence speed and premature convergence to the local optimum. As an effective method to solve these drawbacks, an adaptive genetic algorithm (AGA) considering adaptive crossover and mutation operators is proposed in this paper. Verified by two test functions, AGA shows higher convergence speed and stronger ability to search the global optimal solutions than GA. To meet the crashworthiness and lightweight demands of automotive bumper design, CFRP material is employed in the bumper beam instead of traditional aluminum. Then, a multiobjective optimization procedure incorporating AGA and the Kriging surrogate model is developed to find the optimal stacking angle sequence of CFRP. Compared with the conventional aluminum bumper, the optimized CFRP bumper exhibits better crashworthiness and achieves 43.19% weight reduction.

scholarly journals Fast error calibration of Flexible Measuring Arm based on an adaptive Genetic Algorithm

2021 ◽  
pp. 002029402110309
Author(s):  
Xinhua Zhao ◽  
Jiahao Wang ◽  
Lei Zhao ◽  
Bin Li ◽  
Haibo Zhou
Keyword(s):  
Genetic Algorithm ◽  
Kinematic Model ◽  
Calibration Method ◽  
Convergence Speed ◽  
Least Square ◽  
Adaptive Genetic Algorithm ◽  
Adaptive Mechanism ◽  
Search Efficiency ◽  
Mutation Operators ◽  
Optimization Calculation

With the development of measurement technology, the Flexible Measuring Arm (FMA) is widely used in quality test of automobile processing and industrial production. FMA is a kind of nonlinear system with many parameters. Low cost and efficient calibration method have become the focuses of attention. This article presents a fast calibration method for FMA based on an adaptive Genetic Algorithm (GA) just with several standard balls and a ball plate. It can greatly reduce the calibration cost than common external calibration method which needs high precision instruments and sensors. Firstly, the kinematic model of FMA is established by RPY theory. Secondly, the common GA is optimized and improved, and an adaptive mechanism is added to the algorithms which can realize the automatic adjustment of crossover and mutation operators. A Normalized Genetic Algorithm (NGA) with adaptive mechanism is proposed to complete the optimization calculation. It can improve the numbers of optimal individuals and the convergence speed. So, the search efficiency will be enhanced greatly. Finally, the Least square method (LSM), the General Genetic Algorithm (GGA), and the proposed NGA are respectively used to finish the calibration work. The compensation accuracy and the search efficiency with the above three different algorithms have been systematically analyzed. Experiment indicates that the performance of NGA is much better than LSM and GGA. The data also has proved that the LSM is suitable to complete optimization calculation for linear system. Its convergence stability is much poorer than NGA and GGA because of the ill-condition Jacobin matrix. GGA is easy to fall into local optimization because of the fixed operators. The proposed NGA obviously owns fast convergence speed, high accuracy and better stability than GGA. The position error is reduced from 3.17 to 0.5 mm after compensation with the proposed NGA. Its convergence rate is almost two time of GGA which applies constant genetic factors. The effectiveness and feasibility of proposed method are verified by experiment.

Research of Image Registration Based on Maximum Entropy Template Selection Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1138-1143
Author(s):  
Hong Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Registration ◽  
Maximum Entropy ◽  
Evolutionary Process ◽  
Adaptive Genetic Algorithm ◽  
Image Stitching ◽  
Local Optimum ◽  
Selection Algorithm ◽  
Template Selection ◽  
Crossover And Mutation

This paper concerns an improved adaptive genetic algorithm, and the method is applied to the Maximum Entropy Template Selection Algorithm image registration. This method includes adjusting the probability of crossover and mutation in the evolutionary process. The method can overcome the disadvantage of traditional genetic algorithm that is easy to get into a local optimum answer. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.

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.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 904-908 ◽  
Author(s):  
Tong Jie Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Local Optimum ◽  
Speed Of Convergence ◽  
Improved Genetic Algorithm ◽  
Low Speed ◽  
Dynamic Mutation ◽  
Mutation Operators

An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

Ranked-Based Sensitivity Analysis for Size Optimization of Structures

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Babak Dizangian ◽  
Mohammad Reza Ghasemi
Keyword(s):  
Sensitivity Analysis ◽  
Design Space ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Global Optimum ◽  
Size Optimization ◽  
Second Phase ◽  
Local Optimum ◽  
Blast Search ◽  
Sliding Technique

This article proposes a novel ranked-based method for size optimization of structures. This method uses violation-based sensitivity analysis and borderline adaptive sliding technique (ViS-BLAST) on the margin of feasible nonfeasible (FNF) design space. ViS-BLAST maybe considered a multiphase optimization technique, where in the first phase, the first arbitrary local optimum is found by few analyses and in the second phase, a sequence of local optimum points is found through jumps and BLASTs until the global optimum is found. In fact, this technique reaches a sensitive margin zone where the global optimum is located, with a small number of analyses, utilizing a space-degradation strategy (SDS). This strategy substantially degrades the high order searching space and then proceeds with the proposed ViS-BLAST search for the optimum design. Its robustness and effectiveness are then defied by some well-known benchmark examples. The ViS-BLAST not only speeds up the optimization procedure but also it ensures nonviolated optimum designs.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 2925-2929
Author(s):  
Xiao Chun Zhu ◽  
Jian Feng Zhao ◽  
Mu Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method ◽  
Crossover And Mutation

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.

scholarly journals A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm

2016 ◽  
Vol 48 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Song Zhang ◽  
Ling Kang ◽  
Liwei Zhou ◽  
Xiaoming Guo
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Flood Routing ◽  
Adaptive Genetic Algorithm ◽  
Muskingum Model ◽  
Elite Strategy ◽  
Data Application ◽  
Different Characteristics

First, a novel nonlinear Muskingum flood routing model with a variable exponent parameter and simultaneously considering the lateral flow along the river reach (named VEP-NLMM-L) was developed in this research. Then, an improved real-coded adaptive genetic algorithm (RAGA) with elite strategy was applied for precise parameter estimation of the proposed model. The problem was formulated as a mathematical optimization procedure to minimize the sum of the squared deviations (SSQ) between the observed and the estimated outflows. Finally, the VEP-NLMM-L was validated on three watersheds with different characteristics (Case 1 to 3). Comparisons of the optimal results for the three case studies by traditional Muskingum models and the VEP-NLMM-L show that the modified Muskingum model can produce the most accurate fit to outflow data. Application results in Case 3 also indicate that the VEP-NLMM-L may be suitable for solving river flood routing problems in both model calibration and prediction stages.

Hybrid Flow-Shop Scheduling Method and Simulation Based on Adaptive Genetic Algorithm

2014 ◽  
Vol 670-671 ◽  
pp. 1434-1438
Author(s):  
Jian Feng Zhao ◽  
Xiao Chun Zhu ◽  
Bao Sheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problems ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method

The n-job, k-stage hybrid flow shop problem is one of the general production scheduling problems. Hybrid flow shop (HFS) problems are NP-Hard when the objective is to minimize the makespan .The research deals with the criterion of makespan minimization for the HFS scheduling problems. In this paper we present a new encoding method so as to guarantee the validity of chromosomes and convenience of calculation and corresponding crossover and mutation operators are designed for optimum sequencing. The simulation results show that the Sequence Adaptive Cross Genetic Algorithm (SACGA) is an effective and efficient method for solving HFS Problems.

Chaotic Clonal Genetic Algorithm for Routing Optimization

2014 ◽  
Vol 1046 ◽  
pp. 371-374
Author(s):  
Bing Fan ◽  
Ying Zeng ◽  
Liang Rui Tang
Keyword(s):  
Genetic Algorithm ◽  
Load Balance ◽  
Convergence Speed ◽  
Differentiated Service ◽  
Service Networks ◽  
Routing Optimization ◽  
Mutation Operators ◽  
Selection Step ◽  
Crossover And Mutation ◽  
Local Extreme

Clonal operator which can reserve the elites is introduced in the selection step of traditional genetic algorithm (GA) to accelerate the local convergence speed. Chaotic search which is randomness and ergodicity is applied in crossover and mutation operators to avoid the algorithm stopping at a local extreme value. The above hybrid GA is called chaotic clonal GA (CCGA) which can overcome the instability of optimizing processes and results in traditional GA by the certainty of chaotic trajectory. The CCGA is applied to solve the problem of load balance routing in differentiated service networks. The routing optimization model is created and the optimizing objective is load balance and small path length. The simulation results show that CCGA has fast convergence speed and high stability. It can meet the requirements of important business routings.

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