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Phased Bee Colony Optimization Algorithm for Solving Mathematical Function
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 8 |
| Year of Publication: 2019 |
| Authors: Rahul Kumar Mishra |
10.5120/ijca2019919456
|
Rahul Kumar Mishra . Phased Bee Colony Optimization Algorithm for Solving Mathematical Function. International Journal of Computer Applications. 177, 8 ( Oct 2019), 21-27. DOI=10.5120/ijca2019919456
Abstract
In this article, we have proposed an efficient Bee Colony Optimization method, namely Phased Bee Colony Optimization (PBCO) technique for solving mathematical functions in a multidimensional space. The search process of the optimization approach is directed towards a region or a hypercube in a multidimensional space to find a global optimum or near global optimum after a predefined number of iterations. The process in the entire search area to another region (new search area) surrounding the optimum value found so far after a few iterations and restarts the search process in the new region. However, the search area of the new region is reduced compared to previous search area. Thus, the search process finding advances and jumps to a new search space (with reduced area space) in several phases until the algorithm is terminated. The PBCO technique has tested on a set of mathematical benchmark functions with number of dimensions up to 100 and compared with several relevant optimizing approaches to evaluate the performance of the algorithm. It has observed that the proposed technique performs either better or similar to the performance of other optimization methods.
References
- DT Pham and MCastellani The Bees Algorithm: modelling foraging behaviour to solve continuous optimization problems, PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C JOURNAL OF NGINEERING SCIENCE 1989-1996 (VOLS 203-210) · DECEMBER 2009.
- Wenping Zou, Yunlong Zhu, Hanning Chen, and Beiwei Zhang Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm , Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2011, Article ID 569784.
- Reza Akbari, Alireza Mohammadi, Koorush Ziarati A novel bee swarm optimization algorithm for numerical function optimization , Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran.
- Dogan Aydn, Tianjun Liao, Marco A. Montes de Oca, Thomas St•utzleI Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm , IRIDIA Technical Report Series Technical Report No. TR/IRIDIA/2011-015 August 2011.
- Rui Zhang, An Artificial Bee Colony Algorithm Based on Problem Data Properties for Scheduling Job Shops , School of Economics and Management, Nanchang University, Nanchang 330031, PR China
- Ali R. Yildiz, Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach , Bursa Technical University, Department of Mechanical Engineering, Bursa, Turkey
- Dervis Karaboga, Celal Ozturk∗, A novel clustering approach: Artificial Bee Colony (ABC) algorithm , Erciyes University, Intelligent Systems Research Group, Department of Computer Engineering, Kayseri, Turkey
- S.A.M. Fahad, M.E.El-Hawary, Overview of Artificial Bee Colony (ABC) algo-rithm and its applications , in: Proceedings of IEEE Conference on Systems, 2012, pp.1–6.
- D. Karaboga, An idea based on honey bee swarm for numerical optimization , TechnicalReport-TR06, ErciyesUniversity Kayseri, Turkey, 2005, pp.1–10
- Y. Xin -She, Engineering Optimization: An Introduction with Metaheuristic Applications , Wiley.com, Hoboken, NJ, 2010.
- Dervis Karaboga · Bahriye Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , © Springer Science+Business Media B.V. 2007
- Efr´en Mezura-Montes_ and Omar CetinaDo ´ı guez, Empirical Analysis of a Modified Artificial Bee Colony for Constrained Numerical Optimization , Laboratorio Nacional de Informatics Avanade (LANIA A.C.) R´ebsamen 80, Centro, Xalapa, Veracruz, 91000, MEXICO
- Miloš Nikolic´ , Duša Teodorovic, Empirical study of the Bee Colony Optimization (BCO) algorithm , Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia
- Bai Li a, Ya Li b,n, Ligang Gong, Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model , Engineering Applications of Artificial Intelligence
- Chin Soon Chong, Malcolm Yoke Hean Low, Appa Iyer Sivakumar, Kheng Leng Gay, A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING , Proceedings of the 2006 Winter Simulation Conference
- C. Y. Lee and X. Yao, Evolutionary programming using mutations based on the levy probability distribution, IEEE Trans. Evol. Comput. , vol. 8, no. 1, pp. 1-13, Feb. 2004
- Y. W. Leung and Y. P. Wang, An orthogonal genetic algorithm with quantization for global numerical optimization, IEEE Trans. Evol. Comput., vol. 5, no. 1, pp. 41-53, Feb 2001
- X. Yao, Y.Liu, and G. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82-102, July 1999
- C. S. Hong and Z Quan, 1988. Integeral global optimization: Theory implementation and application . SpringerVerlag, Berlin, Germany
- Y. Wang and C. Dang, An evolutionary algorithm for global optimization based on Level-set evolution and Latin squares IEEE Trans. On Evol. Comput., vol. 11, no. 5, October 2007, pp. 579-595
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