A Dynamic Search Space Strategy for Swarm Intelligence

Abstract

As an appendix which is designed to embed in one of the complete swarm intelligence algorithms, the novel strategy, named dynamic-search-spaces (DS) is proposed to deal with the premature convergence of those algorithms. For realizing the decrement of search space, the differences or the distances between individual sites and the site of the global performance are to form the threshold of the self-adaption system. Once the value reached by calculating the quotient of sum of those sitting near the global performance and others over a stated percentage, the system is working to readjust the borders of search space by the site of the global performance. After each readjustment, the re-initialize to distribute individuals in the whole search space should be achieved to enhance individuals’ vitality which prove away from the premature convergence. Meanwhile, the simpler verifications are provided. The improvements of results are exhibited embedding in the genetic algorithm, the particle swarm optimization and the differential evolution. This dynamic search space scheme can be embedded in most of swarm intelligence algorithms easily abstract environment.