Projects per year
Abstract
Since the establishment of the area of search-based software engineering, a wide range of optimisation techniques have been applied to automate various stages of software design and development. Architecture optimisation is one of the aspects that has been automated with methods like genetic algorithms, local search, and ant colony optimisation. A key challenge with all of these approaches is to adequately set the balance between exploration of the search space and exploitation of best candidate solutions. Different settings are required for different problem instances, and even different stages of the optimisation process.
To address this issue, we investigate combinations of different search operators, which focus the search on either exploration or exploitation for an efficient variable neighbourhood search method. Three variants of the variable neighbourhood search method are investigated: the first variant has a deterministic schedule, the second variant uses fixed probabilities to select a search operator, and the third method adapts the search strategy based on feedback from the optimisation process. The adaptive strategy selects an operator based on its performance in the previous iterations. Intuitively, depending on the features of the fitness landscape, at different stages of the optimisation process different search strategies would be more suitable. Hence, the feedback from the optimisation process provides useful guidance in the choice of the best search operator, as evidenced by the experimental evaluation designed with problems of different sizes and levels of difficulty to evaluate the efficiency of varying the search strategy.
To address this issue, we investigate combinations of different search operators, which focus the search on either exploration or exploitation for an efficient variable neighbourhood search method. Three variants of the variable neighbourhood search method are investigated: the first variant has a deterministic schedule, the second variant uses fixed probabilities to select a search operator, and the third method adapts the search strategy based on feedback from the optimisation process. The adaptive strategy selects an operator based on its performance in the previous iterations. Intuitively, depending on the features of the fitness landscape, at different stages of the optimisation process different search strategies would be more suitable. Hence, the feedback from the optimisation process provides useful guidance in the choice of the best search operator, as evidenced by the experimental evaluation designed with problems of different sizes and levels of difficulty to evaluate the efficiency of varying the search strategy.
Original language | English |
---|---|
Title of host publication | Search-Based Software Engineering: 7th International Symposium (SSBSE 2015) |
Subtitle of host publication | Bergamo, Italy, September 5-7, 2015, Proceedings |
Editors | Marcio Barros, Yvan Labiche |
Place of Publication | Cham [Switzerland] |
Publisher | Springer |
Pages | 188 - 202 |
Number of pages | 15 |
ISBN (Electronic) | 9783319221830 |
ISBN (Print) | 9783319221823 |
DOIs | |
Publication status | Published - 2015 |
Event | International Symposium on Search-Based Software Engineering, SSBSE 2015 - Bergamo, Italy Duration: 5 Sept 2015 → 7 Sept 2015 Conference number: 7th https://link.springer.com/book/10.1007/978-3-319-22183-0 |
Conference
Conference | International Symposium on Search-Based Software Engineering, SSBSE 2015 |
---|---|
Abbreviated title | SSBSE 2015 |
Country/Territory | Italy |
City | Bergamo |
Period | 5/09/15 → 7/09/15 |
Internet address |
Keywords
- Adaptive neighbourhood search
- Component deployment optimisation
Projects
- 1 Finished
-
Adaptive Optimisation of Complex Combinatorial Problems
Aleti, A. (Primary Chief Investigator (PCI))
ARC - Australian Research Council
12/01/14 → 31/12/19
Project: Research