Multi Objective Line Symmetry Based Evolutionary Clustering Approach

2014 ◽  
pp. 49-58
Author(s):  
Singh Vijendra ◽  
Sahoo Laxman
Keyword(s):  
Multi Objective ◽  
Evolutionary Clustering ◽  
Clustering Approach

An Evolutionary Clustering Approach to Pareto Solutions in Multiobjective Optimization

2002 ◽  
Author(s):  
Min Joong Jeong ◽  
Sinobu Yoshimura
Keyword(s):  
Multiobjective Optimization ◽  
Engineering Design ◽  
Function Space ◽  
Clustering Algorithm ◽  
Computational Effort ◽  
Local Optimum ◽  
Pareto Solutions ◽  
Evolutionary Clustering ◽  
Interpretation Process ◽  
Clustering Approach

Pareto solutions in multiobjective optimization are very problematic to measuring the characteristics of solutions for engineering design because of their considerable variety in function space and parameter space. To overcome these situations, a clustering-based interpretation process for Pareto solutions is considered. For better competitive clustering algorithm, we propose an evolutionary clustering algorithm — ECA. The ECA requires less computational effort, and overcomes local optimum of the K-means clustering algorithm and its related algorithms. Effectiveness of the method is examined in detail through the comparison with other algorithms.

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