Particle multi-swarm optimization: A proposal of multiple particle swarm optimizers with information sharing
Hiroshi Sho · 2017
For enhancing the search performance of multiple particle swarm optimizers, in this paper we propose multiple particle swarm optimizers with information sharing by introducing a special strategy, called multi-swarm information sharing. The crucial idea here is to add a new confidence term into the updating rule of the particle's velocity by the best solution found by the multi-swarm search. This is a new approach for the technical development of particle multi-swarm optimization (PMSO) itself. In order to confirm the performance effectiveness of the information sharing strategy in the proposed multiswarm search, computer experiments of implementing a suite of benchmark problems are carried out. We investigate the intrinsic characteristics of the proposed methods, and compare them with the search ability and efficiency of multi-swarm information sharing without this strategy. The obtained experimental results show that the proposed methods have better search performance.