Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take hours or days to optimize difficult objective functions which are deceptive or expensive. Deceptive functions may be highly multimodal and multidimensional, and PSO requires extensive exploration to avoid being trapped in local optima. Expensive functions, whose computational complexity may arise from dependence on detailed simulations or large datasets, take a long time to evaluate. For deceptive or expensive objective functions, PSO must be parallelized to use multiprocessor systems and clusters efficiently. This thesis investigates the implications of parallelizing PSO and in particular, the details of parallelization and the effects of large swarms. PSO can be expressed naturally in Google's MapReduce framework to develop a simple and robust parallel implementation that automatically includes communication, load balancing, and fault tolerance. This flexible implementation makes it easy to apply modifications to the algorithm, such as those that improve optimization of difficult objective functions and improve parallel performance. Results show that larger swarms help with both of these goals, but they are most effective if arranged into sparse topologies with lower overhead from communication. Additionally, PSO must be modified to use communication more efficiently in a large sparse swarm for objective functions where information ideally flows quickly through a large swarm. Swarm size is usually fixed at a modest number around 50, but particularly in a parallel computational environment, much larger swarms are much more effective for deceptive objective functions. Likewise, swarms much smaller than 50 are more effective for expensive but less deceptive functions. In general, swarm size should be carefully chosen using all available information about the objective function and computational environment.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
McNabb, Andrew W., "Parallel Particle Swarm Optimization and Large Swarms" (2011). Theses and Dissertations. 2480.
particle swarm optimization, parallelization