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Open Engineering

formerly Central European Journal of Engineering

Open Access
Online
ISSN
2391-5439
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An efficient algorithm for function optimization: modified stem cells algorithm

1Department of Communications and Electronics, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

2RSISE, Australian National University, Canberra, ACT, 0200, Australia

3Center for Evidence-Based Imaging, Department of Radiology, Brigham & Women’s Hospital, Harvard Medical School, Brookline, MA, USA

© 2012 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Citation Information: Open Engineering. Volume 3, Issue 1, Pages 36–50, ISSN (Online) 2391-5439, DOI: 10.2478/s13531-012-0047-8, December 2012

Publication History

Published Online:
2012-12-29

Abstract

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Keywords: Optimization algorithm; Modified stem cells algorithm; Particle swarm optimization; Ant colony optimization; Artificial bee colony algorithm; Genetic algorithm

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