research
Parallel particle swarm optimization algoritms in nuclear problems
- Publication date
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- IEN
Abstract
Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates
evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and
simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO
demonstrated to have advantages over other well-established PBM. However, computational costs are still a
great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time
consuming objective functions. To overcome such difficulty, parallel computation has been used. The default
advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring
this characteristic are the most investigated. However, much more should be expected. It is known that PSO
may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several
different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by
communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two
complex and time consuming nuclear engineering problems: i) reactor core design (CD) and ii) fuel reload (FR)
optimization. After exhaustive experiments, it has been concluded that: i) PPSO still improves solutions after
many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of
realworld problems and ii) PPSO with more elaborated communication strategies demonstrated to be more
efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully
discussed in this work