Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization

2006 ◽  
pp. 817-825 ◽  
Author(s):  
Fang Gao ◽  
Gang Cui ◽  
Hongwei Liu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Cultural Algorithms

scholarly journals A Genetic Algorithm Using Infeasible Solutions for Constrained Optimization Problems

2014 ◽  
Vol 8 (1) ◽  
pp. 904-912 ◽  
Author(s):  
Yalong Zhang ◽  
Hisakazu Ogura ◽  
Xuan Ma ◽  
Jousuke Kuroiwa ◽  
Tomohiro Odaka
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Constrained Optimization Problems

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

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