Swarm intelligence is a cooperative behavior of collective systems like insects such as ant colony optimization (ACO), fish schooling, birds flocking, bee Colony Optimization (BCO) particle swarm optimization (PSO) and so on. In this paper, a hybrid performances for data organization and information extrapolation is recommended. The Honey Bee Mating Optimization algorithm and Artificial Neural Networks (HBMO-ANN) may also be considered as a distinctive swarm-based optimization, in which the exploration algorithm is encouraged by the development of real honey-bee marital and mimic the iterative mating process of honey bees and approaches to select applicable drones for mating progression through the fitness function enrichment for selection of superlative weights for hidden layers of Neural Network classifiers. Enhanced HBMO with Neural Network (EHBMO-NN) algorithm is now realistic to classify the data proficiently by training the neural network. The classification accuracy of EHBMO is much more compared with other algorithm such as Support Vector Clustering Algorithm (EHBMO-SVC). In this paper, enhanced honey-bee mating optimization algorithm is offered and verified. A developed way of Honey Bee Mating Optimization performance is combined with Neural Network which expands accuracy and moderate time delay in difficulty of various real world datasets.

Swarm intelligence, Honey Bee Mating Optimization Algorithm, Support Vector Clustering, Artificial Neural Networks

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