In recent years,with the rapid development of computer and database technologies,the number of large-scale datasets has rapidly increased.Thus,the use of feature selection technology is important to screen features with massive amounts of information.In this study,a salp swarm feature selection algorithm with a hybrid strategy(HS-SSA)is pro-posed.Initially,a sorted table based on mutual information is generated,and a new initialization strategy is proposed on the basis of this sorted table.Furthermore,a novel dynamic search algorithm with conditional call is proposed.With re-spect to location updates,the efficiency of exploration and development steps is improved,and the flexibility and di-versity of the solution space are increased by combining the transient search algorithm(TSO).Consequently,the al-gorithm can rapidly locate the global optimal location.To verify algorithm performance,14 UCI datasets were selected for the test.In addition,the proposed algorithm was compared with the salp swarm algorithm(SSA),the improved SSA,and many other improved algorithms in recent years.The results show that HS-SSA is more competitive in feature selection.
维普
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