Protein structure prediction using particle swarm optimization and a distributed parallel approach
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Protein structure prediction using distributed parallel particle swarm optimization
Natural Computing: an international journal
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Particle swarm optimization (PSO) algorithm is a population-based algorithm for finding the optimal solution. Because of its simplicity and high efficiency, 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.