| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6905792 | Applied Soft Computing | 2014 | 13 Pages |
Abstract
- We develop an effective novel hybrid multi-population genetic algorithm in which we separate solution space into different parts and each subpopulation represents a separate part.
- This assures the diversity of the algorithm.
- We design the operators so as to search only the feasible space; thus, we save computational time by avoiding infeasible space.
- After perfectly tuning the algorithm, it is compared with 11 available algorithms in the literature.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Hani Pourvaziri, B. Naderi,