Date of Award

2013

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Ludwig, Simone

Description

Many scientific, engineering and economic problems involve the optimization of a set of parameters. The Particle Swarm Optimization (PSO) is one of the new techniques that have been empirically shown to perform well. The PSO algorithm is a population-based search algorithm based on simulating the social behavior of birds within a flock. Large-scale engineering optimization problems impose large computational demands, resulting in long solution times even on modern high-end processors. To obtain enhanced computational throughput and global search capability parallel algorithms and parallel architectures have drawn lots of attention. Parallelization of PSO has proved to enhance computational throughput and global search capability In this paper, we detail the parallelization of an increasingly popular global search method, the PSO algorithm using MPJ Express. Both synchronous and asynchronous parallel implementations are investigated. The parallel PSO algorithm’s robustness and efficiency are demonstrated by using four standard benchmark functions Alpine, Rosenbrock, Rastrigin and Schaffer.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/22571

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