Swarm Optimization with Neural Networks for Effective Classification Techniques

Abstract

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 thispaper, a hybrid performance for data organization and information extrapolation is recommended.The Honey Bee MatingOptimization (HBMO) algorithm and Artificial Neural Networks may also be considered as a distinctive swarm-basedoptimization, in which the exploration algorithm is encouraged by the development of real honey-bee marital and mimic theiterative mating process of honey bees and approaches to select applicable drones for mating progression through the fitnessfunction enrichmentfor selection of superlativeweightsfor hidden layers of Neural Network classifiers. Extended HBMO with Neural Network algorithm is now realistic to classify the data proficiently by training the neural network.Extended HBMO (EHBMO-NN) procedure is now realistic to categorize the data efficiently by teaching the neural network. The arrangement precision of EHBMO-NN is associated with several other procedures.  In this paper, extended honey-bee coupling optimization process (EHBMO-NN) is presented and verified with few benchmark instances. A developed way of Honey Bee Mating Optimization performance is joined with Neural Network which increases exactitude and decrease time interruption in complication of numerous factual world datasets.