A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

Lisheng Wei,Huacai Lu,Ning Wang,Shi Cheng

doi:10.1155/2021/6694695

Abstract

In order to improve the iris classification rate, a novel biogeography-based optimization algorithm (NBBO) based on local search and nonuniform variation was proposed in this paper. Firstly, the linear migration model was replaced by a hyperbolic cotangent model which was closer to the natural law. And, the local search strategy was added to traditional BBO algorithm migration operation to enhance the global search ability of the algorithm. Then, the nonuniform variation was introduced to enhance the algorithm in the later iteration. The algorithm could achieve a stronger iris classifier by lifting weaker similarity classifiers during the training stage. On this base, the convergence condition of NBBO was proposed by using the Markov chain strategy. Finally, simulation results were given to demonstrate the effectiveness and efficiency of the proposed iris classification method.

Highlights

Iris classification is especially suitable for recognition with uniqueness, stability, inviolability, and reliability characteristics and has been one of the hottest biological characteristic recognition research spot recently [1]
Evaluate individual population and update the optimal solution better adapt to the nonlinear migration problem, this paper proposes a hyperbolic cotangent nonlinear migration model to improve the performance of the basic BBO algorithm
Work is paper proposed a novel biogeography-based optimization algorithm based on local migration strategy and nonuniform mutation for iris classification

Summary

Introduction

Iris classification is especially suitable for recognition with uniqueness, stability, inviolability, and reliability characteristics and has been one of the hottest biological characteristic recognition research spot recently [1]. Wang et al [11] combined the chaotic mapping strategy with the BBO optimal migration model and proposed a biogeography optimization algorithm based on based on the adaptive population migration mechanism. In order to improve the iris classification rate, the NBBO based on local search and nonuniform variation was proposed in this paper. E nonuniform variation was used in the operation to increase the search ability of the algorithm and prevent the local optimum, which could achieve a stronger iris classifier by lifting weaker similarity classifiers during the training stage.

Methods

Simulation Experiments

Findings

Conclusions and Future