A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification

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In the recent past, several biological and natural phenomena have extensively attracted researchers towards the rapid development of science and engineering. Basically solving the optimization problems in various Engineering discipline is a popular topic among the other problem solving strategies. Most of the biological processes include the swarm intelligence research areas where the activity and the behavior of real insects have been studied. One of the recently developed Swarm algorithms is the Honey Bee Mating Optimization (HBMO) algorithm which is based on the mating behavior of bees. In this work, a hybrid metaheuristic honey bee mating based Pi-Sigma Neural Network (PSNN) have been proposed to successfully solve the classification problem of data mining. The proposed approach combines HBMO with the PSNN and is compared with other techniques like GA (Genetic Algorithm), DE (Differential Evolution), and PSO (Particle Swarm Optimization). Experimental results reveal that the proposed approach is steady as well as reliable and provides better classification accuracy than others.