A Genetic Algorithm Based Clustering Approach for Piecewise Linearization of Nonlinear Functions

2011 ◽  
Author(s):  
Subhojit Ghosh ◽  
Asmini Ray ◽  
Deepak Yadav ◽  
B. M. Karan
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Functions ◽  
Piecewise Linearization ◽  
Clustering Approach

Optimization for Coal Heavy Haul Transportation Assembly Scheme Problem Using Genetic Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2541-2547
Author(s):  
Xue Song Han ◽  
Zheng Hong Gu
Keyword(s):  
Genetic Algorithm ◽  
Residence Time ◽  
Computational Experiments ◽  
Nonlinear Functions ◽  
Heavy Haul ◽  
Disassembly Time

Thispaper presents aCoal Heavy Haul Transportation Assembly Scheme Problem (CHASP), in which the time consuming functions, assembly number constraints and assembly weight constraints etc are considered. The time consuming costs consist of residence time and disassembly time. The disassembly time functions are usually nonlinear functions of unit train departure directions. Then, a nonlinear 0-1 programming is formulated for the problem and solved by lingo mathematical solver. Considering the complexity of the problem, a kind ofGenetic Algorithm is proposed to solve it. Extensive computational experiments are taken on randomly generated data, the detailed results are given and the genetic algorithm is shown to be efficient.

scholarly journals HIERARCHICAL CLUSTERING APPROACH WITH HYBRID GENETIC ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS

2016 ◽  
pp. 214-218
Author(s):  
M. H. MEHTA ◽  
V. V. KAPADIA
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Hierarchical Clustering ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Combinatorial Optimization Problems ◽  
Input Size ◽  
Clustering Approach ◽  
Definite Interval

Engineering field has inherently many combinatorial optimization problems which are hard to solve in some definite interval of time especially when input size is big. Although traditional algorithms yield most optimal answers, they need large amount of time to solve the problems. A new branch of algorithms known as evolutionary algorithms solve these problems in less time. Such algorithms have landed themselves for solving combinatorial optimization problems independently, but alone they have not proved efficient. However, these algorithms can be joined with each other and new hybrid algorithms can be designed and further analyzed. In this paper, hierarchical clustering technique is merged with IAMB-GA with Catfish-PSO algorithm, which is a hybrid genetic algorithm. Clustering is done for reducing problem into sub problems and effectively solving it. Results taken with different cluster sizes and compared with hybrid algorithm clearly show that hierarchical clustering with hybrid GA is more effective in obtaining optimal answers than hybrid GA alone.

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