A dynamic search space strategy for swarm intelligence

Abstract

As an appendix which is designed to embed in one of complete swarm intelligence algorithms, a novel strategy named dynamic-search-spaces (DS) is proposed to deal with the premature convergence of those algorithms. For realising the decrement of search space, the differences or the distances between individuals and the global performance are to form a threshold for building the self-adaption system. When the value calculating the rate of those individuals sitting near the global performance reaches a stated percentage, the system is working to readjust the borders of search space by the site of the global performance. The search space will be compressed to close the global performance as the centre. After each readjustment, the re-initialisation to distribute individuals in the whole search space should be achieved to enhance individuals' vitality which moves away from the premature convergence and improves the performance of each individual. Meanwhile, the simpler verifications are provided. The improvements of results are exhibited embedding in the genetic algorithm, the particle swarm optimisation and the differential evolution. Thus, this dynamic search space scheme can be embedded in most swarm intelligence algorithms easily.