Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation

Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. This paper proposes a hybrid ant swarm optimization algorithm by using immunity to discover better fitness value in optimizing rough reducts set. Unlike a conventional PSOIACO algorithm, this hybrid algorithm shows improvement of the classification accuracy in its generated rough reducts to solve NP-Hard problem. This paper has evaluated the immune algorithm in 12 common benchmark dataset to evaluate the performance of rough reducts-based on attribute reduction. The results show that immune ant swarm algorithm is very competitive in terms of fitness value, number of iterations, and classification accuracy to produce a better optimization technique and more accurate results in rough reducts generation. The results also show that immune ant swarm optimization provides a slight increase in accuracy when compared to the differential evolution variant