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A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 13 |
| Year of Publication: 2014 |
| Authors: Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam |
10.5120/16402-6079
|
Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam . A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems. International Journal of Computer Applications. 94, 13 ( May 2014), 15-20. DOI=10.5120/16402-6079
Abstract
Swarm intelligence (SI) algorithms generally come from nature or biological behavior of nature. These algorithms use probabilistic search methods that simulate the behavior of biological entities or the natural biological evolution. Swarm intelligence (SI) is based on collective behavior of self-organized systems. Typical swarm intelligence algorithms include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Recently some new swarm based algorithms like Firefly Algorithm (FA) and Bat Algorithm (BA) has emerged. BA is a new optimization technique, which is based on the echolocation behavior of bats. BA is very efficient in exploitations but relatively poor in explorations. In this paper, a Novel Adaptive Bat Algorithm (NABA) is presented to improve the explorative characteristics of BA. The proposed algorithm incorporates two techniques within BA to improve its degree of explorations, which include the Rechenberg's 1/5 mutation rule and the Gaussian probability distribution to produce mutation step sizes. Both these techniques try to balance between the explorative and exploitative properties of BA. Simulation results on a number of benchmark functions on the continuous optimization problem suggest that the proposed algorithm – NABA often show much improved results, compared to the standard BA.
References
- G. Wang and L. Guo, "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, vol. 2013, p. 21, 2013.
- S. Yang X, "Nature-inspired Metaheuristic Algorithms," Luniver Press, 2008.
- R. Eberhart and J. Kennedy, Swarm Inteligence, Academic Press, 2001.
- j. Kennedy and R. Eberhart, "Practicle swarm optimization," in IEEE International Conference Neural Networks, Perth, Australia, 1995.
- X. S. Yang and J. R. Gonzalez, ""A New Metaheuristic Bat-Inspired Algorithm" in Nature Inspired Cooperative Strategies for Optimization (NISCO 2010)," Springer Press, vol. 284, pp. 65-74, 2010.
- X. S. Yang, "Harmony Search as a Metaheuristic Algorithm,Music-Inspired Harmony Search Algorithm," Theory and Applications,Studies in Computational Intelligence, vol. 191, pp. 1-14, 2009.
- T. Colin, The Varienty of Life, Oxford University Press, 2000.
- J. Altringham, Bats: Biology and Behaviour, Oxford University Press, 1996.
- P. Richardson, "Bats," National history Museum , London, 2008.
- B. A. Faritha and C. Chandrasekar, "An optimized approach of modified bat algorithm to record deduplication," International Journal of Computer Applications, vol. 62, no. 1, pp. 10-15, 2012.
- G. komarasamy and A. Wahi, "An Optimized K-Means Clustering Technique using Bat Algoritm," European Journal of Scientific Research, vol. 84, no. 2, pp. 263-273, August 2012.
- K. Khan and A. Sahai, "A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context," I. J. Intelligent Systems and Applications, pp. 23-29, June 2012.
- E. U. K. Y. C. S. Y?lmaz, "Modified Bat Algorithm," ELEKTRONIKA IR ELEKTROTECHNIKA, vol. 20, no. ISSN 1392-1215, p. 2, 2014.
- S. Yang X, "Bat algorithm for multi-objective optimization," International Journal of Bio-Inspired Computation, vol. 3, no. 5, pp. 267-274, 2011.
- Y. Selim and K. E. U. , "Improved Bat Algorithm (IBA) on Continuous Optimization Problems," Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, August 2013.
- Rechenberg, Evolutionstrategie: Optimirung Technisher Systeme Nach Prinzipen des Biologischen Evolution, FrommanHozlboog, Stuttgard, Germany, 1973. .
- I. Rechenberg, "Evolutionsstrategie," Frommann-Holzboog, 1994.
- T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford, UK: Oxford University Press, 1996.
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