Name
Abstract | ||
|---|---|---|
Particle swarm optimization (PSO) algorithm is a population-based algorithm for finding the optimal solution. Because of its simplicity in implementation and fewer adjustable parameters compared to the other global optimization algorithms, PSO is gaining attention in solving complex and large scale problems. However, PSO often requires long execution time to solve those problems. This paper proposes a parallel PSO algorithm, called delayed exchange parallelization, which improves performance of PSO on distributed environment by hiding communication latency efficiently. By overlapping communication with computation, the proposed algorithm extracts parallelism inherent in PSO. The performance of our proposed parallel PSO algorithm was evaluated using several applications. The results of evaluation showed that the proposed parallel algorithm drastically improved the performance of PSO, especially in high-latency network environment. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.parco.2010.09.003 | Parallel Computing |
Keywords | Field | DocType |
particle swarm optimization,parallel pso algorithm,population-based algorithm,proposed parallel pso algorithm,global optimization algorithm,proposed parallel algorithm,proposed algorithm extract,communication latency,overlapping communication,communication latency tolerant parallel,high-latency network environment,parallel algorithm,distributed environment,global optimization | Particle swarm optimization,Population,Global optimization,Distributed Computing Environment,Computer science,Parallel algorithm,Parallel computing,Multi-swarm optimization,Theoretical computer science,Imperialist competitive algorithm,Computation | Journal |
Volume | Issue | ISSN |
37 | 1 | Parallel Computing |
ISBN | Citations | PageRank |
978-1-4244-5467-9 | 4 | 0.42 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 578 | 45.93 |
| Koichi Wada | 2 | 319 | 54.11 |