Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Parallelization Strategies for Ant Colony Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
An artificial intelligence approach to the efficiency improvement of a universal motor
Engineering Applications of Artificial Intelligence
A distributed ant-based algorithm for numerical optimization
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
Multi-core implementation of the differential ant-stigmergy algorithm for numerical optimization
The Journal of Supercomputing
| Hi-index | 0.00 |
The paper presents a new distributed Multilevel Ant Stigmergy Algorithm (MASA) for minimizing the power losses in an electric motor by optimizing the independent geometrical parameters of the rotor and the stator. The efficiency of the algorithm, in sequential form, to solve that particular optimization problem has already been shown in literature. However, even if this method offers good quality of solution, it still needs considerable computational time. With distributed implementation of the MASA the computation time is drastically decreased (from one day to few hours) without any noticeable loss in solution quality.