The week's pick
Random Articles
Reseach Article
Bat Algorithm with Self-adaptive Mutation: A Comparative Study on Numerical Optimization Problems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 100 - Number 10 |
| Year of Publication: 2014 |
| Authors: Md. Wasi Ul Kabir, Mohammad Shafiul Alam |
10.5120/17559-8172
|
Md. Wasi Ul Kabir, Mohammad Shafiul Alam . Bat Algorithm with Self-adaptive Mutation: A Comparative Study on Numerical Optimization Problems. International Journal of Computer Applications. 100, 10 ( August 2014), 7-13. DOI=10.5120/17559-8172
Abstract
Swarm intelligence is an emerging research field that tries to mimic the collective intelligent behavior found in swarms of insects and animals. Many algorithms have been proposed that simulate these intelligent swarm models to solve a wide range of scientific and engineering problems. The Bat algorithm is one of the most recent swarm intelligence based algorithms that simulates the intelligent hunting behavior of the bats found in nature. In this paper, we present an improved self-adaptive Bat algorithm (BA SAM) for the problem of global numerical optimization over continuous domains. We have introduced two improved solution search equations — the BA/Normal/1 and BA/Cauchy/1 schemes. We have also used a selection probability to control the frequency of employing BA/Normal/1 and BA/Cauchy/1, which leads to a new self-adaptive search mechanism for the Bat algorithm. Experiments are conducted on both unimodal and multimodal continuous benchmark functions. The results demonstrate the improved performance of the BA SAM algorithm in comparison to the original Bat algorithm and another recently introduced improved variant of the Bat algorithm.
References
- Hsiang-Cheh Huang, John F. Roddick, Jeng-Shyang Pan Shu-Chuan Chu, "Overview of Algorithms for Swarm Intelligence ," in ICCCI 2011, Part I, LNCS 6922, pp. 28–41, Kaohsiung, 2011.
- J Kennedy and R Eberhart, "Practicle swarm optimization," in IEEE International Conference Neural Networks, Perth, Australia, 1995, pp. 1942-1945.
- R Eberhart and J Kennedy, Swarm Inteligence. : Academic Press, 2001.
- 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.
- John D Altringham, Bats: Biology and Behaviour. : Oxford University Press, 1996.
- Md. Wasi Ul Kabir, Md. Monirul Islam Mohammad Shafiul Alam, "On the Performance of Recurring Multistage Evolutionary Algorithm for Continuous Function Optimization," in International Conference onComputer and Information Technology , Dhaka, 2010.
- Yong Liu, Guangming Lin Xin Yao, "Evolutionary Programming Made Faster," in IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 3, NO. 2, 1999.
- 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.
- P Richardson, "Bats," London, 2008.
- Amr Rekaby, "Directed Artificial Bat Algorithm (DABA) A New Bio-Inspired Algorithm," in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Cairo, 2013.
- Banu, 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.
- E. Ugur Kucuksille, Y. Cengiz S. Y?lmaz, "Modified Bat Algorithm," ELEKTRONIKA IR ELEKTROTECHNIKA, vol. 20, no. ISSN 1392-1215, p. 2, 2014.
- Nazmus Sakib, Syed Mustafizur, Mohammad Shafiul Alam Md. Wasi Ul Kabir, "A Novel Adaptive Bat Algorithm to Control Explorations nd Exploitations for Continuous Optimization Problems," International Journal of Computer Applications , vol. 94, no. 13, 2014.
- L. A. M. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa R. Y. M. Nakamura, "BBA: A Binary Bat Algorithm for Feature Selection," in 25th Conference on Graphics, Patterns and Images, 2012.
- Yilma Selim and Kucuksille Ecir, U, , "Improved Bat Algorithm (IBA) on Continuous Optimization Problems," Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, August 2013.
- S, Yang X, "Nature-inspired Metaheuristic Algorithms," 2008.
- T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford, UK: Oxford University Press, 1996.
- Koffka Khan and Ashok 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.
Index Terms
Keywords