Abstract
Ant colony optimization algorithm is a novel simulated evolutionary algorithm, which provides a new method for complicated combinatorial optimization problems. In this paper the algorithm is used for solving the knapsack problem. It is improved in selection strategy and information modification, so that it can not easily run into the local optimum and can converge at the global optimum. The experiments show the robustness and the potential power of this kind of meta -heuristic algorithm.
| Original language | English |
|---|---|
| Title of host publication | Geoinformatics 2006 |
| Subtitle of host publication | Remotely Sensed Data and Information |
| DOIs | |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | Geoinformatics 2006: Remotely Sensed Data and Information - Wuhan, China Duration: 28 Oct 2006 → 29 Oct 2006 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 6419 |
| ISSN (Print) | 0277-786X |
Conference
| Conference | Geoinformatics 2006: Remotely Sensed Data and Information |
|---|---|
| Country/Territory | China |
| City | Wuhan |
| Period | 28/10/06 → 29/10/06 |
Keywords
- Ant colony optimization algorithm
- Evolutionary computing
- Knapsack problem
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Li, S., Wang, S., & Zhang, Q. (2006). An improved ant colony algorithm to solve knapsack problem. In Geoinformatics 2006: Remotely Sensed Data and Information Article 64191T (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6419). https://doi.org/10.1117/12.713269