A modified artificial bee colony algorithm with its applications in signal processing

Artificial bee colony ABC algorithm is a biological-inspired optimisation algorithm proposed in recent years. It has been shown to have some advantages than most of conventional biological-inspired algorithms and has been widely used in many applications. However, the ABC algorithm does not consider the balance between global best and local best, and make ABC algorithm insufficiency. In this paper, a modified ABC algorithm is proposed, global best is introduced into the original ABC algorithm to modify the update equation of employed and onlooker bees while the equation for scouts remain unchanged. The effectiveness of the proposed approach is verified on the problem of peak-to-average power ratio reduction in orthogonal frequency division multiplexing signals and multi-level image segmentation. Simulation results showed that the proposed approach has better performance than traditional ABC algorithm with the same computational complexity.

[1]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[2]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[3]  Jae Hong Lee,et al.  An overview of peak-to-average power ratio reduction techniques for multicarrier transmission , 2005, IEEE Wireless Communications.

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Faïçal Mnif,et al.  Particle swarm optimisation of a discontinuous control for a wheeled mobile robot with two trailers , 2011, Int. J. Comput. Appl. Technol..

[6]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[7]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[8]  Tao Jiang,et al.  An Overview: Peak-to-Average Power Ratio Reduction Techniques for OFDM Signals , 2008, IEEE Transactions on Broadcasting.

[9]  Weiyu Yu,et al.  Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation , 2012, Int. J. Comput. Appl. Technol..

[10]  Jay B. Jordan,et al.  Image segmentation using maximum entropy techniques , 1984, ICASSP.

[11]  Lifeng Xi,et al.  A heuristics method based on ant colony optimisation for redundancy allocation problems , 2011, Int. J. Comput. Appl. Technol..

[12]  J. Huber,et al.  OFDM with reduced peak-to-average power ratio by optimum combination of partial transmit sequences , 1997 .

[13]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[14]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[15]  Deng Yong,et al.  Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO) , 2005 .

[16]  Yajun Wang,et al.  A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals , 2010, IEEE Transactions on Wireless Communications.

[17]  A. S. Madhukumar,et al.  Peak-to-average power reduction using partial transmit sequences: a suboptimal approach based on dual layered phase sequencing , 2003, IEEE Trans. Broadcast..

[18]  Wenbing Tao,et al.  Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm , 2003, Pattern Recognit. Lett..

[19]  Dervis Karaboga,et al.  Partial transmit sequences based on artificial bee colony algorithm for peak-to-average power ratio reduction in multicarrier code division multiple access systems , 2011, IET Commun..

[20]  Ahmed S. Abutableb Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .