Title: Fast and consistent images areas recognition using an Improved Shuffled Frog Leaping Algorithm
Authors: Anis Ladgham; Anis Sakly; Abdellatif Mtibaa
Addresses: Electronics and Microelectronics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia; Department of Electrical Engineering, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia ' Department of Electrical Engineering, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia; Research Unit: Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), University of Monastir, Monastir, Tunisia ' Electronics and Microelectronics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia; Department of Electrical Engineering, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
Abstract: Improved Shuffled Frog Leaping Algorithm (IMSFLA) is a novel optimal algorithm of automatic recognition of areas of greyscale-based images. It is as an efficient improvement of the original Shuffled Frog Leaping Algorithm (SFLA). SFLA is a newly developed evolutionary algorithm with good global search capability. In this new paradigm, we propose a new fitness function. It is computationally simple and assists to quickly discover the adequate threshold in a continuous range of grey levels. And more, the new method is enhanced by the cloning of the fitter particles at the expense of the worst particles. The performance of IMSFLA is evaluated towards an Artificial Bee Colony algorithm (ABC) based method, two Genetic algorithm (GA) based method and Artificial Fish-Swarm (AFS) based method for many benchmark images and IMSFLA outperforms these algorithms.
Keywords: image recognition; SFLA; IMSFLA; fitness function; imaging; shuffled frog leaping algorithm; greyscale images.
DOI: 10.1504/IJSISE.2015.071957
International Journal of Signal and Imaging Systems Engineering, 2015 Vol.8 No.5, pp.331 - 342
Received: 21 Nov 2012
Accepted: 23 Feb 2014
Published online: 25 Sep 2015 *