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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jayadeva Shah, S. Kothari, R. Chandra, S. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India (Chandra, S.) || IBM India Research Lab., 4 Block C, Institutional Area, Vasant Kunj, New Delhi - 110070, India (Kothari, R.) || Department of Electrical Engineering, Indian Institute of Technology Hauz Khas, New Delhi 110016, India (Jayadeva; Shah, S.) |
| Abstract | Collective foraging in ant colonies is as remarkable in that ants are solving a distributed control and optimization task that is still not fully untravelled. Ants deposit pheromone as they travel, and paths with more pheromone are preferred by succeeding ants. Without any direct communication amongst themselves, ants quickly abandon other trails to concentrate on the shortest one. If the food source moves, or a new path is discovered, ants can still overcome initial bias due to pheromone deposited on an earlier path, and switch to the new path. Nevertheless, important questions remain. How much should be advantage offered by a better path discovered later for the ants to switch to it if an initial preference or bias has already built up on an earlier path ? How much bias is too much ? When multiple food sources are present, ants form multiple trails that seem to optimize overall throughput, if that word may be considered apt. These questions have importance in many real life scenarios. How can information be optimally disseminated ? If a competitors's products or technologies have a large initial bias, then how much gain or advantage should a new product or technology provide in order to cultivate a large following or market share ? What kind of share in the market should you probabilistically expect for the gain you offer ? In the presence of dynamically changing traffic scenarios, how can packets be routed without centralized command and control to maximize network throughput ? This talk summarizes some of our recent work. First, we showed that for traditional ant colony optimization models, beyond a certain threshold, initial bias cannot be overcome. That is, even if a better path is found, ants will not switch from a longer path that has been frequented for a long enough time. Next, motivated by some knowledge of biological ants, we suggest a biologically motivated ant pheromone update model that guarantees convergence to the shortest path regardless of initial bias. The algorithm displays this behaviour even when the food source is moved or path lengths change during foraging. Finally, we examine the utility of such an algorithm in the context of routing in communication networks. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 223631 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781457700293 |
| e-ISBN | 9781457700316 |
| DOI | 10.1109/ICICS.2011.6174275 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-13 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Color Ant colony optimization Routing Algorithm design and analysis Convergence Biological system modeling self-organization Ant pheromone update rule Ant Colony Optimization convergence analysis collective foraging |
| Content Type | Text |
| Resource Type | Article |
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