  BPSO algorithm is a swarm intelligence optimization algorithm, which has the
characteristics of good optimization effect, high efficiency and easy to
implement. In recent years, it has been used to optimize a variety of machine
learning and deep learning models, such as CNN, LSTM, SVM, etc. But it is easy
to fall into local optimum for the lack of exploitation ability. It is found
that in the article, which is different from previous studies, The reason for
the poor performance is an error existing in their velocity update function,
which leads to abnormal and chaotic behavior of particles. This not only makes
the algorithm difficult to converge, but also often searches the repeated
space. So, traditionally, it has to rely on a low w value in the later stage to
force these algorithms to converge, but also makes them quickly lose their
search ability and prone to getting trapped in local optima. This article
proposes a velocity legacy term correction method for all V-shaped BPSOs.
Experimentals based on 0/1 knapsack problems show that it has a significant
effect on accuracy and efficiency for all of the 4 commonly used V-Shaped
BPSOs. Therefore it is an significant breakthrough in the field of swarm
intelligence.
