A Hybrid Genetic Algorithm for Constrained Optimization Problems
|
| Title | A Hybrid Genetic Algorithm for Constrained Optimization Problems |
| Authors | |
| Abstract | Abstract—Genetic algorithm (GA) is a powerful method to solve constrained optimization problems (COPs). In this paper, a new fitness function based hybrid genetic optimization algorithm (NFFHGA) for COPs is proposed, in which a new crossover operator based on Union Design is presented, and inspired by the smooth function technique, a new fitness function is designed to automatically search for potential solutions. Furthermore, in order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. Experiments on 6 benchmark problems are performed and the compared results with the best known solutions reported in literature show that NFFHGA can not only quickly converge to the optimal or near-optimal solutions, but also have a high performance. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2013-02-01 |
| Source | Journal of Computers Vol 8, No 2 (2013): Special Issue: Advances in Computational Intelligence |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |