Login New user?  
01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 7 > No. 5

 
   

An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems

PP: 1933-1941
Author(s)
Sheng-Ta Hsieh, Shih-Yuan Chiu, Shi-Jim Yen,
Abstract
Multi-objective optimization (MO) has been an active area of research in the last two decades. In multi-objective genetic algorithm (MOGA), the quality of newly generated offspring of the population will directly affect the performance of finding the Pareto optimum. In this paper, an improved MOGA, named SMGA, is proposed for solving multi-objective optimization problems. To increase efficiency during solution searching, an effective mutation named sharing mutation is adopted to generate potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results show that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithms (MOEA).

  Home   About us   News   Journals   Conferences Contact us Copyright naturalspublishing.com. All Rights Reserved