The convergence and divergence are two common phenomena in swarm intelligence. To
obtain good search results, the algorithm should have a balance on convergence and divergence.
The premature convergence happens partially due to the solutions getting clustered
together, and not diverging again. The brain storm optimization (BSO), which is a young
and promising algorithm in swarm intelligence, is based on the collective behavior of
human being, that is, the brainstorming process. The convergence strategy is utilized in
BSO algorithm to exploit search areas may contain good solutions. The new solutions
are generated by divergence strategy to explore new search areas. Premature convergence
also happens in the BSO algorithm. The solutions get clustered after a few iterations,
which indicate that the population diversity decreases quickly during the search. A definition
of population diversity in BSO algorithm is introduced in this paper to measure the
change of solutions’ distribution. The algorithm’s exploration and exploitation ability can
be measured based on the change of population diversity. Different kinds of partial reinitialization
strategies are utilized to improve the population diversity in BSO algorithm.
The experimental results show that the performance of the BSO is improved by part of
solutions re-initialization strategies.
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