Parameter Selection Using Fruit Fly Optimization

R.S. Shudapreyaa*, S. Anandamurugan**
* PG Scholar, Department of Information Technology, Kongu Engineering College, Perundurai, India.
** Assistant Professor, Department of Information Technology, Kongu Engineering College, Perundurai, India.
Periodicity:December - February'2016
DOI : https://doi.org/10.26634/jcom.3.4.4831

Abstract

Fruit fly algorithm is a novel perspicacious optimization algorithm predicated on the foraging comportment of the authentic fruit flies. Recently, an incipient Fruit Fly Optimization Algorithm (FOA) has been proposed to solve optimization quandaries. In order to find optimum solution for an optimization quandary, fine-tuned parameters are obtained as a result of manual test in the fruit fly algorithm. This study deals with enhancing the probing efficiency and greatly ameliorate the probing quality and also on an automated approach for finding the cognate parameter by utilizing a grid search algorithm. Also it provides better ecumenical probing ability, more expeditious convergence, and more precise convergence. The optimization of a sizably voluminous antenna array for maximum directivity, utilizing a modified fruit fly optimization algorithm with desultory search of two groups of swarm and adaptive fruit fly swarm population size.

Keywords

Optimization, Fruit Fly Algorithm, Modified Fruit Fly Optimization, Swarm and Adaptive Fruit Fly Swarm.

How to Cite this Article?

Shudapreyaa, R.S., and Anandamurugan, S. (2016). Parameter Selection Using Fruit Fly Optimization. i-manager’s Journal on Computer Science, 3(4), 29-35. https://doi.org/10.26634/jcom.3.4.4831

References

[1]. Eberhart, R.C. and Kennedy, J. (1995). “New Optimizer Using Particle Swarm Theory”. Proceedings of Sixth International Symposium on Nagoya, Japan, pp.39- 43.
[2]. Pan, W.T., (2011). “A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example”. Knowledge-Based Systems, Vol.26, pp.69-74.
[3]. Xiaofang Yuan, Yuanming Liu, Yongzhong Xiang, and Xinggang Yan, (2015). “Parameter Identification of BIPT System Using Chaotic-enhanced Fruit Fly Optimization Algorithm”. Journal on Electrical and Information System, Vol.268, No.2, pp.1267-1281.
[4]. Hazim Iscan, Mesut Gunduz, (2014). “Parameter Analysis on Fruit Fly Optimization Algorithm”. Journal on Computer and Communication, Vol.2, No.4, pp.137- 141.
[5]. Dan Shan, GuoHua Cao, and HongJiang Dong, (2013). “LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems”. Journal on Systems Science and Information, Vol.6, No.5, pp.108- 117.
[6]. Nattaset Mhudtongon, Chuwong Phongcharoenpanich, and Supakit Kawdungta, (2015). “Modified Fruit Fly Optimization Algorithm for Analysis of Large Antenna Array ”. Journal on Antennas and Propagation, Vol.86, No.10, pp.124-137.
[7]. Specht, D.F. (1991). “A General Regression Neural Network”. Journal on Neural Networks, Vol.6, No.2, pp.568-576.
[8]. Dabin Zhang, Jia Ye, Zhigang Zhou, and Yuqi Luan, (2015). “A New Fruit Fly Optimization Algorithm Based on Differential Evolution”. Journal on Systems Science and Information Communication, Vol.3, No.4, pp.365-373.
[9]. W. T. Li, X. W. Shi, and Y. Q. Hei, (2008). “An Improved Particle Swarm Optimization Algorithm for Pattern Synthesis of Phased Arrays”. Progress in Electromagnetics Research, Vol.82, pp.319-332.
[10]. W.-T. Pan, (2012). “A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example”. Knowledge-Based Systems, Vol.26, pp.69-74.
[11]. B. Liu, L. Wang, Y. H. Jin, F. Tang, and D. X. Huang (2005), “Improved particle swarm optimization combined with chaos”. Chaos, Solitons & Fractals, Vol.25, No.5, pp.1261-1271.
[12]. Y. Wang, T. Weise, J. Wang, B. Yuan, and Q. Tian (2011), “Self-adaptive learning based particle swarm optimization”. Information Sciences, Vol.181, No.20, pp.4515-4538.
[13]. P.-W. Chen, W.-Y. Lin, T.-H. Huang, and W.-T. Pan(2013), “Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service”. Applied Mathematics and Information Sciences, Vol.7, No.2, pp.459-465.
[14]. L. Wang, X.-L. Zheng, and S.-Y. Wang(2013), “A Novel Binary Fruit Fly Optimization Algorithm for Solving the Multidimensional Knapsack Problem”. Knowledge-Based Systems, Vol.48, pp.17-23.
[15]. H. Dai, G. Zhao, J. Lu, and S. Dai(2014), “Comment and Improvement on 'a new Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example'”. Knowledge-Based Systems, Vol.59, pp.159- 160.
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