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.

scholarly journals Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaorui Shi ◽  
Wei Cui ◽  
Ping Zhu ◽  
Yanhua Yang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Assembly Line ◽  
Analysis Method ◽  
Improved Genetic Algorithm ◽  
Line Balance ◽  
Automobile Assembly ◽  
Assembly Line Balance ◽  
Population Clustering

Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K -means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.

Agricultural Logistics Companies Distribution Based on Fuzzy Clustering Analysis and Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2276-2280 ◽  
Author(s):  
Hang Yu ◽  
Kai Zhang
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Clustering Analysis ◽  
Chromosome Structure ◽  
Agricultural Products ◽  
Improved Genetic Algorithm ◽  
Logistics Distribution ◽  
Logistics Enterprise ◽  
Fuzzy Clustering Analysis ◽  
Matlab Programming

This paper analyzes the logistics distribution system, logistics and distribution of agricultural products and agricultural products logistics enterprise status,Demand for agricultural products logistics companies point clustering analysis, Determine the economic and non-economic rational and reasonable point point,To TSP problem-based, Application of improved genetic algorithm to determine its distribution route, Use natural number coding chromosome structure represents feasible route, greedy crossover, mutation and reverse the 2-opt other methods, With VC + + and MATLAB programming of the algorithm, And empirical analysis proves the validity of the algorithm.

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.

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