Abstract
Many bioinspired methods are based on using several simple entities which search for a reasonable solution (somehow) independently. This is the case of Particle Swarm Optimization (PSO), where many simple particles search for the optimum solution by using both their local information and the information of the best solution found so far by any of the other particles. Particles are partially independent, and we can take advantage of this fact to parallelize PSO programs. Unfortunately, providing good parallel implementations for each specific PSO program can be tricky and time-consuming for the programmer. In this paper we introduce several parallel functional skeletons which, given a sequential PSO implementation, automatically provide the corresponding parallel implementations of it. We use these skeletons and report some experimental results. We observe that, despite the low effort required by programmers to use these skeletons, empirical results show that skeletons reach reasonable speedups.
Highlights
Evolutionary and swarm metaheuristics [1,2,3,4,5,6,7] provide generic solutions to face hard computational problems
Particle Swarm Optimization [24,25,26,27] is a metaheuristic inspired on the social behavior of flocks of birds when flying, as well as on the movement of shoals of fish
In order to test the suitability of our Particle Swarm Optimization (PSO) parallel approach, we develop the following methodology
Summary
Evolutionary and swarm metaheuristics [1,2,3,4,5,6,7] provide generic solutions to face hard computational problems. Particle Swarm Optimization [24,25,26,27] ( on PSO) is a metaheuristic inspired on the social behavior of flocks of birds when flying, as well as on the movement of shoals of fish. A population of entities moves in the search space during the execution of the algorithm These entities are very simple and perform local interactions. We can consider that the swarm is a multi-agent system where particles are simple agents that move through the search space and store (and possibly communicate) the best solution found so far. In Handbook of Nature-Inspired and Innovative Computing; Zomaya, A., Ed.; Springer: New York, USA, 2006; pp. 187–219
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