Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem
High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting on...
Saved in:
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2018-07-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions. |
|---|---|
| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/10622 |