Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 3

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  swarm optimization
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Content available An Ant Algorithm for the Sudoku Problem
EN
In this paper an ant algorithm for the Sudoku problem is presented. This is the first ant algorithm enabling discovery of an optimal solution to the Sudoku puzzle for 100% of investigated cases. The Sudoku is a one of many combinatorial optimisation problems, as well as an NPcomplete problem, hence an ant algorithm which constructs an optimal solution as a meta-heuristic method is important for this problem.
EN
Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel hybrid algorithm based in Bees Algorithm and Particle Swarm Optimization is applied to the Knapsack Problem. The Bee Algorithm is a new population-based search algorithm inspired by the natural foraging behavior of honey bees, it performs a kind of exploitative neighborhood search combined with random explorative search to scan the solution, but the results obtained with this algorithm in the Knapsack Problem are not very good. Although the combination of BA and PSO is given by BSO, Bee Swarm Optimization, this algorithm uses the velocity vector and the collective memories of PSO and the search based on the BA and the results are much better. We use the Greedy Algorithm, which it's an approximate algorithm, to compare the results from these metaheuristics and thus be able to tell which is which gives better results.
EN
In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercises — treadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.