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Mathematical Problems in Engineering
Volume 2015, Article ID 498626, 12 pages
http://dx.doi.org/10.1155/2015/498626
Research Article

Root Growth Optimizer with Self-Similar Propagation

1College of Information Science & Engineering, Central South University, Changsha 410083, China
2College of Engineering, University of Tennessee, Knoxville, TN 37996, USA
3Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4College of Management, Shenzhen University, Shenzhen 518060, China
5The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Received 1 December 2014; Accepted 4 February 2015

Academic Editor: María Isabel Herreros

Copyright © 2015 Xiaoxian He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions.