scholarly journals An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 48
Author(s):  
Shuai Wang ◽  
Zhongkai Li ◽  
Chao He ◽  
Dengzhuo Liu ◽  
Guangyu Zou
Keyword(s):  
Genetic Algorithm ◽  
Degrees Of Freedom ◽  
Modular Design ◽  
Product Information ◽  
Evaluation Criteria ◽  
Automatic Programming ◽  
Improved Genetic Algorithm ◽  
Modular Architecture ◽  
Integrated Method ◽  
Electromechanical Products

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Study on Welding Path Planning of Welding Position Flock for Welding Robot Based on Improved Genetic Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1332-1335
Author(s):  
Hong Wei Lv
Keyword(s):  
Genetic Algorithm ◽  
Degrees Of Freedom ◽  
Mathematic Model ◽  
Welding Robot ◽  
Improved Genetic Algorithm ◽  
Complete Coverage ◽  
Six Degrees Of Freedom ◽  
Filial Generation ◽  
Full Rotation ◽  
Welding Position

With full rotation articulated ABB-IBR140-M2000 robot with six degrees of freedom used as welding robot, the exploring of efficient complete coverage of welding position flock is an important way of improving the performance of welding robot. Making use of improved genetic algorithm, in which the best father generation is saved and using ordered cross and reverse ordered mutation to constitute filial generation, and this method makes sure that the algorithms is convergent. The complete coverage of welding position optimization mathematic model whose objective is the minimum distance is established, making use of the improved genetic algorithm to solve the problem, an example is analyzed in detail, and the result shows that the algorithms is convergent and efficient.

A Web-Based Platform for Modular Design of Intelligent Instrument Based on Improved Genetic Algorithm

2011 ◽  
Vol 88-89 ◽  
pp. 291-295
Author(s):  
Jian Pan ◽  
Guo Hong Mao ◽  
Jin Xiang Dong
Keyword(s):  
Genetic Algorithm ◽  
Modular Design ◽  
Optimal Solutions ◽  
Improved Genetic Algorithm ◽  
Web Database ◽  
User Requirement ◽  
Web Based ◽  
Encoding Scheme ◽  
Intelligent Instrument ◽  
Selection Of

The design of new products is a creative work based on designer’s knowledge or experience. This paper develops a web-based design platform for intelligent instrument with the technology of Java and web database. It aims at offering near-optimal solutions of product design scheme that meets user requirement with the selection of module. An improved genetic algorithm with a binary encoding scheme is proposed to accomplish the selection of module more effectively.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

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