Future Generation Computer Systems
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Using MPI-2: Advanced Features of the Message Passing Interface
Using MPI-2: Advanced Features of the Message Passing Interface
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Multicore Processors for Science and Engineering
Computing in Science and Engineering
Journal of Global Optimization
Pseudo Parallel Ant Colony Optimization for Continuous Functions
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
A distributed ant-based algorithm for numerical optimization
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
A survey on parallel ant colony optimization
Applied Soft Computing
A Shared-Memory ACO-Based Algorithm for Numerical Optimization
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
An immunological algorithm for global numerical optimization
EA'05 Proceedings of the 7th international conference on Artificial Evolution
The distributed stigmergic algorithm for multi-parameter optimization
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
The differential ant-stigmergy algorithm
Information Sciences: an International Journal
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Numerical optimization techniques are applied to a variety of engineering problems. The cost-function evaluation is an important part of any numerical optimization and is usually realized as a black-box simulator. For the efficient solving of the numerical optimization problem on multi-core systems, new shared-memory and distributed-memory approaches are proposed. The algorithms are based on an ant-stigmergy meta-heuristics, where indirect coordination between the ants drives the search procedure toward the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory and distributed-memory implementations. The Intel-OpenMP 3.0 and MPICH2 libraries are used for the inter-thread and inter-process communications, respectively. It is shown that speed-up strongly depends on the simulation time. This is especially evident in a distributed-memory implementation. Therefore, the algorithms' performances, according to the simulator's time complexity, are experimentally evaluated and discussed.