Swine influenza inspired optimization algorithm and its application to multimodal function optimization and noise removal

Abstract

Swine Influenza Inspired Optimization (SIIO) is a search algorithm proposed for optimal solution. The authors followed the SIR (susceptible - infectious-recovered) virus spread model of Swine Influenza to develop the new evolutionary algorithm named as SIIO. SIR model is used to frame optimization algorithm following the spread and control phenomenon of the swine flu virus in the human population. The fitness based classes viz. susceptible (S), infectious (I) and recovered (R) of the individuals are made and treatment is used for the affected individuals by imitating the health information from the best fitness individual. The proposed algorithm shows improved performance on multi-dimensional unimodal and multimodal standard numerical benchmark functions than the compared optimization algorithms. The performance of the SIIO algorithm is better in terms of speed of convergence and quality of solutions. The SIIO is also applied for the Gaussian noise removal with Blind Source Separation (BSS) based on Independent Component Analysis (ICA).