Variable Length Black Hole for Optimization and Feature Selection

In the high dimensional space, the problem of feature selection (FS) can be regarded as combinatorial optimization problem with high complexity due to the huge number of candidate features. In this article, a novel type of meta-heuristic searching based on variable length of solution space is proposed in order to solve the high dimensionality issue of the FS and to obtain more optimal results. The proposed algorithm uses the original black hole optimization as baseline for development. Blackhole optimization assumes in fixed solution space which decreases the efficiency when the number of features is high. Furthermore, the algorithm is subject to stagnation due to the single exemplar or black hole selection. Hence, or novel variable length black hole modifies the original black hole algorithm with various aspects, namely, it enables decomposing the solution space into subset of dimensions and searching within each dimension separately with selecting an exemplar for each dimension which represents the black hole of the corresponding dimension. In addition, it enables length changing of the solutions based on stagnation criterion. Furthermore, it proposes new concept of energy to the black hole which indicates to the decrease in the effectiveness of black hole with time in an exponential way and use it to replace the black hole when it is not effective anymore. The proposed algorithm which is designated as variable length black hole optimization VLBHO is compared with the variable length particle swarm optimization. The approach has increased the accuracy from 50% to 67% for forest cover dataset and from 38% to 80% for wine dataset.