| Improving Network Routing |
| Nodes periodically send forward ants to some recently recorded destinations | |
| Collect information on way | |
| Die if reach already visited node | |
| When reaches destination, estimates time and turns into backward ant | |
| Returns by same route, updating routing tables |
| Some Applications of ACO |
| Routing in telephone networks | ||
| Vehicle routing | ||
| Job-shop scheduling | ||
| Constructing evolutionary trees from nucleotide sequences | ||
| Various classic NP-hard problems | ||
| shortest common supersequence, graph coloring, quadratic assignment, É | ||
| Improvements as Optimizer |
| Can be improved in many ways | |
| E.g., combine local search with ant-based methods | |
| As method of stochastic combinatorial optimization, performance is promising, comparable with best heuristic methods | |
| Much ongoing research in ACO | |
| But optimization is not a principal topic of this course |
| The Nonconvergence Issue |
| AS often does not converge to single solution | ||
| Population maintains high diversity | ||
| A bug or a feature? | ||
| Potential advantages of nonconvergence: | ||
| avoids getting trapped in local optima | ||
| promising for dynamic applications | ||
| Flexibility & robustness are more important than optimality in natural computation | ||
| Natural Computation |
| Natural computation is computation that occurs in nature or is inspired by computation occurring in nature |
| Optimization in Natural Computation |
| Good, but suboptimal solutions may be preferable to optima if: | ||
| suboptima can be obtained more quickly | ||
| suboptima can be adapted more quickly | ||
| suboptima are more robust | ||
| an ill-defined suboptimum may be better than a sharp optimum | ||
| ÒThe best is often the enemy of the goodÓ | ||
| Robust Optima |
| Effect of Error/Noise |
| Demonstration: Human Synchronization |
| Reaction Time |
| Synchronization |
| Flashing Among Fireflies |
| Synchronous Flashing |
| In SE Asia enormous numbers of fireflies gather in trees and flash in synchrony | |
| A group of trees spread over 1/10 mile may flash in synchrony | |
| Only males do synchronous flashing | |
| Had been unexplained for 300 years | |
| Early 1900s: claimed to be an illusion because no explanation could be imagined |
| Why Do They Do It? |
| Females identify males of their own species by flashing rate | ||
| difficult to do if they flash chaotically | ||
| Allows males to detect (unsynchronized flashing of nearby females) | ||
| i.e., enhanced detection | ||
| Allows small groups of males to attract larger numbers of females | ||
| i.e., signal enhancement | ||
| How Do They Do It? |
| Òinnate individual rhythmicity with phase-dependent sensitivity to mutual influencesÓ | ||
| Natural flashing period: 965±90 msec (Å 1 sec) | ||
| Flash from firefly A will reset the clock of nearby firefly B | ||
| thereby shifting the phase of BÕs clock | ||
| If A flashes in first 840 ms of BÕs cycle, will inhibit BÕs next flash & delay until 1 sec after stimulus (i.e. retarded so it is in sync with A) | ||
| If A flashes in last 160 ms, BÕs next flash occurs normally, but subsequent flash will be advanced to be in sync with A | ||
| Free-running Flashing |
| Stimulus in first 840 msec |
| Free-running Flashing (again) |
| Stimulus in last 120 msec |
| Starlogo Simulation of Firefly Synchronization |
| Run firefly.slogo Simulation |
| Schools, Flocks, & Herds |
| Òand the thousands of fishes moved as a
huge beast, piercing the water. They appeared united, inexorably bound to a common fate. How comes this unity?Ó |
| Coordinated Collective Movement |
| Groups of animals can behave almost like a single organism | ||
| Can execute swift maneuvers | ||
| for predation or to avoid predation | ||
| Individuals rarely collide, even in frenzy of attack or escape | ||
| Shape is characteristic of species, but flexible | ||
| Adaptive Significance |
| Prey avoiding predation | |
| More efficient predation by predators | |
| Other efficiencies |
| Avoiding Predation |
| More compact aggregation | ||
| predator risks injury by attacking | ||
| Confusing predator by: | ||
| united erratic maneuvers (e.g. zigzagging) | ||
| separation into subgroups (e.g., flash expansion & fountain effect) | ||
| Flash Expansion |
| Flash Expansion |
| Fountain Effect |
| Fountain Effect |
| Fountain Effect |
| Fountain Effect |
| Better Predation |
| Coordinated movements to trap prey | ||
| e.g. parabolic formation of tuna | ||
| More efficient predation | ||
| e.g., killer whales encircle dolphins | ||
| take turns eating | ||
| Other Efficiencies |
| Fish schooling may increase hydrodynamic efficiency | ||
| endurance may be increased up to 6« | ||
| school acts like Ògroup-level vehicleÓ | ||
| V-formation increases efficiency of geese | ||
| range 70% greater than that of individual | ||
| Lobsters line up single file by touch | ||
| move 40% faster than when isolated | ||
| decreased hydrodynamic drag | ||
| Characteristic Arrangement of School |
| Shape is characteristic of species | ||
| Fish have preferred distance, elevation & bearing relative to neighbors | ||
| Fish avoid coming within a certain minimum distance | ||
| closer in larger schools | ||
| closer in faster moving schools | ||